Special COVID-19 Issue: Summer 2020 by Columbia Science Review

COLUMBIA SCIENCE REVIEW SPECIAL COVID-19 ISSUE SUMMER 2020

Cover illustrated by Megan Zou (Layout Editor/Articles): Lia Chen/Combatting COVID-19 with Machine Learning, Racial Disparities in the COVID-19 Pandemic; Ashley Chung/ToC, A Statistical Sensation; Sally Hwang/Coronavirus: The Basics, Numbers Can Lie; Kevin Li/Contextualizing COVID-19 with the SARS and MERS Outbreaks, New Zealand’s Team of Five Million Eradicates COVID-19; Ning Luo/“The Chinese Virus”; Amanda Klestzick/A Look Towards the Skies; Alejandra Nunez/Vaccine Development, Sex in the Time of Corona

Fair Use Notice Columbia Science Review is a student publication. The opinions represented are those of the writers. Columbia University is not responsible for the accuracy and contents of Columbia Science Review and is not liable for any claims based on the contents or views expressed herein. All editorial decisions regarding grammar, content, and layout are made by the Editorial Board. All queries and complaints should be directed to the Editor-In-Chief. This publication contains or may contain copyrighted material, the use of which has not always been specifically authorized by the copyright owner. We are making such material available in our efforts to advance understanding of issues of scientific significance. We believe this constitutes a “fair use” of any such copyrighted material, as provided for in section 107 of the US Copyright Law. In accordance with Title 17 U.S.C. Section 107, this publication is distributed without profit for research and educational purposes. If you wish to use copyrighted material from this publication for purposes of your own that go beyond “fair use,” you must obtain permission from the copyright owner.

EDITORIAL BOARD EDITOR-IN-CHIEF ALICE SARDARIAN CHIEF DESIGN OFFICER AIDA RAZAVILAR EDITORS SERENA CHENG, ANNA CHRISTOU, ENOCH JIANG, LINGHAO KONG, CHERYL PAN, EMILY SUN, ETHAN WU, VICTORIA YANG LAYOUT EDITORS LIA CHEN, ASHLEY CHUNG, SALLY HWANG, KEVIN LI, NING LUO, AMANDA KLESTZICK, ALEJANDRA NUNEZ

MANAGING EDITOR SARAH HO CHIEF ILLUSTRATOR LIZKA VAINTROB WRITERS ELLEN ALT, ERICA ANDERSEN, LIZA CASELLA, BOYUAN CHEN, VICTORIA COMUNALE, JENNA EVERARD, ENOCH JIANG, RACHELL POWELL, HANNAH PRENSKY ILLUSTRATORS AEJA ROSETTE, SABRINA RUSTGI, REBECCA SIEGEL, EMILY WANG, MEGAN ZOU

EXECUTIVE BOARD PRESIDENT ABHISHEK SHAH PUBLIC RELATIONS JACQUELINE ERLER SECRETARY ARUSHI SAHAI MEDIA TEAM CHENOA BUNTSANDERSON, BRENDON CHOY, ERIC PARSONS, CATHERINE SERIANNI, MAGGIE ZHONG, NICHOLAS ZUMBA

VICE PRESIDENT JASON WANG TREASURER ADRIANA KULUSIC-HO SENIOR OCM ALLI GREENBERG MANASI SHARMA, KAT WU OCM'S AROOBA AHMED, CHINMAYI BALUSU, BOYUAN CHEN, ESME LI, HANNAH LIN, JOHN NGUYEN, ALANA PALOMINO, NICK VAUGHAN, JOJO WU

The Executive Board represents the Columbia Science Review as an ABC-recognized Category B student organization at Columbia University.

COVID-19 Issue Table of Contents

06 08 10 12 16 18

Letters from the Editors Alice Sardarian & Sarah Ho Editorial Board in the Age of COVID Editorial Board Members Coronavirus: The Basics Emily Sun A Statistical Sensation Jenna Everard Contextualizing COVID-19 with the SARS and MERS Outbreaks Victoria Comunale Numbers Can Lie Enoch Jiang

20 22 24 26 30 34 36

Vaccine Development Alice Sardarian Sex in the Time of Corona Ellen Alt A Look Towards the Skies Victoria Comunale Combatting COVID-19 with Machine Learning Serena Cheng Racial Disparities in the COVID-19 Pandemic Sarah Ho New Zealand’s Team of Five Million Eradicates COVID-19 Hannah Prensky “The Chinese Virus” Aida Razavilar

LETTERS FROM Dear Reader: Welcome to the first ever special edition dedicated to COVID-19. This issue is the product of months of dedicated research, analyses, and fact-finding. The Columbia Science Review Editorial staff investigated several interesting aspects of the coronavirus pandemic. Through it all, we remained determined not to only engage the reader, but to also dispel misinformation in these unprecedented times. There has been no better example of the dangers of misinformation than what we have witnessed during this public health crisis. While scientists have raced to develop viable vaccines and identify new ways of effectively dealing with this pandemic, many in politics have attempted to discredit physicians and scientists who are committed to public safety. At the Columbia Science Review, we believe in making scientific information and facts readily available to our readers. We have done our best to cover a broad range of topics and to bring you what we hope to be informative articles on COVID-19. At this time, it is not only critical to stay safe by adhering to public health measures like mask-wearing and social distancing, but also to stay informed. Due to the evolving nature of science, articles have been dated to indicate that only the information available at the time was utilized. Iâ&#x20AC;&#x2122;m proud of the hard work and commitment demonstrated by our staff, and to have worked alongside passionate young scientists and future changemakers. Finally, I am pleased to hand off the position of Editor-in-Chief to Sarah Ho who has worked tirelessly to ensure the success of this publication. Iâ&#x20AC;&#x2122;m certain that the Columbia Science Review will continue to grow and excel under her leadership. Thank you for giving us the opportunity to share our work with you. Stay safe and stay strong! Sincerely,

Alice Sardarian Editor-in-Chief

Dear Reader, Hello and welcome to the Columbia Science Reviewâ&#x20AC;&#x2122;s Summer COVID-19 issue! Whether you read this issue in print or digital form, you will find a variety of indepth articles that cover some of the many facets of the COVID-19 pandemic, ranging from the development of vaccines to the use of machine learning to project and diagnose cases. Ordinarily, the Columbia Science Review only releases two print issues per year, one in the fall and one in the spring. However, a pandemic is anything but ordinary, and given that the COVID-19 pandemic has been accompanied by an abundance of scientific information, misinformation, and controversy, the Editorial Board thought it was only fitting to produce a special issue dedicated to examining the scientific underpinnings and implications of COVID-19. I am incredibly grateful for all of the hard work, passion, and time that the members of the Editorial Boardâ&#x20AC;&#x201D; writers, editors, illustrators, and layout designersâ&#x20AC;&#x201D;devoted to this issue. Zoom sessions are no substitute for in-person company, but nevertheless, I greatly appreciated the camaraderie and comfort of our weekly meetings in the summer. If anything, this pandemic has underlined how much distance often lies between science and the public. Without a doubt, scientific research and discovery are crucial in and of themselves. However, just as importantly, great care needs to be taken to ensure that the public is informed of this information through clear and apolitical means. The Columbia Science Review has long tried to play a role in bridging this gap, and it is my hope that the articles in this issue will be as illuminating and informative for you as they were for me. Happy reading, and wear a mask! Warmly,

Sarah Ho Managing Editor

THE EDITORS

EDITORIAL BOARD IN THE AGE OF COVID-19 The pandemic has caused a great shift in our daily lives. Read about how CSR members are making the most of this time.

“I think the best thing about quarantine is the opportunity to relive aspects of the quintessential childhood back in my home town that I did not appreciate or embrace as much when I was younger...Something that fascinates me the most is how the pandemic has acted as a catalyst for so much change...We are rethinking the way so many industries operate from tech to healthcare, and we have seen much delayed but necessary social movements that make us reevaluate all the systems we thought of as normal. I do not think we will return to normal, but I do not mean that in a pessimistic sense. We are now forced to face problems, find solutions, and make steady progress towards an efficient, but more importantly, an equitable and just system. There’s always a silver lining to it all; it just took me 5 months to process and is something I have to remind myself of each day.” Aida Razaviler ‘23 San Diego, California Layout Designer

“As a volunteer emergency medical technician, I have witnessed the profound impact of COVID-19 on my community and especially its most vulnerable members. I am in awe of the rapidly evolving health system in response to the novel demands and stressors of the pandemic. It has been a distinct privilege to be amongst Connecticut’s first responders. I continue to learn from my mentors in the field as well as find comfort in the fortitude and camaraderie of my colleagues.” Alice Sardarian ‘21 Westport, Connecticut Editor-in-Chief

Hannah Prensky ‘22 Middletown, Maryland Writer

“Before the COVID-19 pandemic, I was accepted into Barnard’s Summer Research Institute as an undergraduate research assistant in the biology department with the Lopatkin Lab. My plans for the summer were to complete a 10-week faculty-mentored lab research project relating to genetic changes in bacterial cells as a result of conferring antibiotic resistance. While I was not able to conduct in-person lab research, I am currently working on a computational biology project in which I am learning how to code for the first time...Although I can’t be in the lab, I’m thrilled to be learning valuable and versatile skills like programming and data analytics.”

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“As quarantine progressed, I found people (including myself) diving into more and more news sources surrounding the COVID-19 pandemic without considering their credibility or intent...In these moments, I realized how pervasive scientific misinformation is in our society and also how difficult it can be for the public to access scientific resources...I decided to begin a platform called Science Made Equal (ScienceMade.org), which raises the voices of our community and journals like Columbia Science Review that strive to make science both accessible and credible. It is so important that we come together as a community...My research is now also remote. I am learning about the association between certain neurodegenerative disease and cancers, specifically Parkinson’s and the NFkB receptor, which plays an active role in many cancers. On a lighter note, I have taught myself how to bake for the first time!” Emily Sun ‘23 Chicago, Illinois Editor

“This summer, I had initially intended to work on a project that utilized astrocytes derived from induced pluripotent stem cells as a research model to study underlying molecular mechanisms of late-onset Alzheimer’s Disease. The shutdown of research labs across the country in response to COVID-19 made me completely rethink my plans. All of my previous laboratory experiences had taken place at the lab bench, but I was fortunate enough to receive guidance on adapting to a remote research environment and pursuing my project from a computational angle instead, namely analyzing the transcriptomes of these differentiated cells. Although I miss my pipettes and daily cell culture routine, I have gained a new interest in learning how to program and using different software to continue my research!”

Rebecca Siegel ‘22 East Brunswick, New Jersey Illustrator

Cheryl Pan ‘21 New York, New York Editor

“I, like everyone else, experienced disruptions in my personal and academic plans and pursuits due to COVID-19, such as the cancellation of Barnard’s SRI program this summer, as well as the cancellation of the study abroad program for next semester, through which I was planning on studying in London. Throughout this time of COVID-19, I have become much more cognizant of those around me...as everyone’s emotional struggles became a shared experience...I’ve found myself stopping and spending my time meaningfully in ways that can be hard to come by in our fast-paced lives, whether it be reading more, picking up old hobbies, donating to or showing support to important causes, and staying informed... I feel that I have experienced much growth and grappled a lot with the idea of mental health...in the hopes of bringing positivity to a reality”

the basics

coronavirus:

In late December of 2019, a disease-tracking system reported mysterious clusters of viral pneumonia in Wuhan, China [1]. Less than a month later, this outbreak of pneumonia became attributed to a complication of a novel virus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). This virus leads to the disease that is currently taking a heavy toll on individuals all across the globe, known as coronavirus disease 2019 (COVID-19).

written & illustrated by emily sun July 24, 2020

what’s the difference between COVID-19 and SARS-CoV-2? Although COVID-19 and SARS-CoV-2 are commonly thrown together in the media, they are not interchangeable. SARS-CoV-2 is the name of the specific virus which causes COVID-19. Wait, there’s a different name for the virus and the disease? It may sound baffling at first, but there is a rationale for this naming system. Viruses refer specifically to the germs that hijack host cells in order to replicate within them, and they are only composed of two things: genetic material and an outer envelope formed by organic molecules [2]. Virus names inform scientists about the specific genetic structure of virus species, facilitating the development of drug treatment and vaccinations [3]. For example, SARS-CoV-2 is named after the SARSCoV virus due to their genetic similarities, but they cause distinct illnesses and should not be confused [3]. A germ’s capacity to cause infection in host cells is known as its pathogenicity, and the more pathogenic a virus is, the higher its ability to invade cells [4]. Most viruses, including SARS-CoV-2, are considered

pathogenic in nature. Initially, when a virus enters the body, the immune system tries to fight it off; if a virus disrupts the normal bodily functions, however, the host may feel unwell— this condition is referred to as the “disease” caused by the virus. Disease names like “COVID-19” tend to be more familiar to the public than their virus counterparts and are typically used for classifying factors such as spread and transmissibility [3].

where did coronavirus get its name? As it turns out, the term “coronavirus” did not come from a beer. Its title comes from the crown, or “corona,” which seems to surround the virus when viewed under a microscope [5]. This appearance is due to protein-based spikes, or peplomers, on the outer layer virus envelope, which bind to specific receptors in host cells [5]. “Coronavirus” is an umbrella term referring to a whole group of viruses with this unique feature, and the new SARS-CoV-2 is just one of the seven known human coronaviruses [6].

some history

concluding remarks

Coronaviruses are nothing new: in fact, they are found in animals frequently exposed to humans, including a variety of farm animals, domestic pets, and small rodents [7]. The first human case of infection by a coronavirus was reported in the mid-1960s after a respiratory sample was taken from a patient with the common cold [8]. Ultimately, scientists found that coronaviruses caused up to 15 percent of common cold cases at the time [8]. While these coronavirus strains seemed both mild and manageable, the first major human coronavirus outbreak from SARS-CoV in the early 2000s proved alarmingly fatal and is thought to have jumped into the population indirectly from bats [9]. These newer coronavirus outbreaks shed light on the alarming risks that these viruses are starting to pose for humans.

COVID-19 is now the third major outbreak of coronaviruses in humans. Although the disease causes symptoms resembling the flu for the majority of the population, old age and underlying health conditions pose a risk for severe complications. The Centers of Disease Control and Prevention (CDC) recommends wearing a face covering, frequent hand-washing, and social distancing of at least six feet to suppress virus transmission [12]. It is crucial that these precautions are taken seriously, especially as scientists dig deeper into the nature of this disease and the virus which causes it.

what does SARS-CoV-2 do to the body?

references

In the journey to develop treatment for the novel coronavirus, scientists discovered a protein crucial to virus entry, called angiotensin-converting enzyme 2 (ACE-2) [10]. This protein normally aids in circulatory system regulation, meaning it is present in most tissue throughout the body; however, it is most concentrated in areas including the lower respiratory tract, kidneys, heart, and gastrointestinal tract [10,11]. This is why infected patients may not only display respiratory illness, but they can also experience other complications regarding digestion, kidney, and heart function [11]. When SARS-CoV-2 infiltrates the human body, a sequence of steps takes place. First, spike proteins on the virus bind to ACE-2 receptors on the host cell membrane; second, the virus enters the host cell through an uptake process called endocytosis; third, the virus and host cell membranes fuse, allowing the release of viral genetic material (RNA) into the host cell; and fourth, RNA is replicated in different regions of the host cell to form fully-fledged viruses, which are then released from the host cell to continue the cycle (see figure) [9]. Better understanding of the interaction between the spike proteins and ACE2 binding site may allow for the development of neutralizing antibodies, or substances that bind to the virus particle and take away its ability to interact with other cells [9]. In SARS-CoV-2, for example, a neutralizing antibody that binds to the spike protein would successfully inhibit its ability to bind to and enter host cells. This knowledge may also help specialists quickly target SARS-CoV-2 mutant strains and other related viruses due to their genetic and structural similarities.

[1] Li et al. (2020). Early Transmission Dynamics in Wuhan, China, of Novel Coronavirusâ&#x20AC;&#x201C;Infected Pneumonia. New England Journal of Medicine, 382(13), 1199-1207. doi:10.1056/nejmoa2001316 [2] Virus (n.d.). In Merriam-Webster.com. Retrieved from https://www. merriam-webster.com/dictionary/virus [3] World Health Organization. (2020). Naming the coronavirus disease (COVID-19) and the virus that causes it. Retrieved from https:// www.who.int/emergencies/diseases/novel-coronavirus-2019/technical-guidance/naming-the-coronavirus-disease-(covid-2019)-and-thevirus-that-causes-it [4] Pathogenic (n.d.). In Vocabulary.com Online Dictionary. Retrieved from https://www.vocabulary.com/dictionary/pathogenic [5] Centers for Disease Control and Prevention. (2019). World MERSCoV Photos. Retrieved from https://www.cdc.gov/coronavirus/mers/ photos.html [6] National Center for Health Statistics, Centers for Disease Control and Prevention. (2020). Human Coronavirus Types. Retrieved July 15, 2020, from https://www.cdc.gov/coronavirus/types.html [7] Saif, L.J. (2004). Learning from SARS: Preparing for the Next Disease Outbreak: Workshop Summary. National Academies Press (US). Retrieved from https://www.ncbi.nlm.nih.gov/books/NBK92442/ [8]Petersen et al. (2020). Comparing SARS-CoV-2 with SARS-CoV and influenza pandemics. The Lancet Infectious Diseases. doi:10.1016/ s1473-3099(20)30484-9 [9] Jiang, S., Hillyer, C., & Du, L. (2020). Neutralizing Antibodies against SARS-CoV-2 and Other Human Coronaviruses. Trends in Immunology, 41(5), 355-359. doi:10.1016/j.it.2020.03.007 [10] Tikellis, C., Thomas, M. C. (2012). Angiotensin-Converting Enzyme 2 (ACE2) Is a Key Modulator of the Renin Angiotensin System in Health and Disease. International Journal of Peptides, 2012, 1-8. doi:10.1155/2012/256294 [11] Astuti, I., Ysrafil. (2020). Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2): An overview of viral structure and host response. Diabetes & Metabolic Syndrome: Clinical Research & Reviews, 14(4), 407-412. doi:10.1016/j.dsx.2020.04.020 [12] World Health Organization. (2020).How to Protect Yourself & Others. Retrieved July 15, 2020, from https://www.cdc.gov/coronavirus/2019-ncov/prevent-getting-sick/prevention.html

Contents Contents

A STATISTICAL SENSATION: Defining COVID-19 Transmission, the Rise of Complications and Misinformation, and What the Future Holds Written by Jenna Everard Illustrated by Emily Wang July 24, 2020

y January 21, symptomatic cases of COVID-19 had been confirmed in eight different countries and regions including the United States, with Washington state confirming the first case [1]. After the World Health Organization (WHO) classified COVID-19 as a global health emergency on January 30, it continued to spread rapidly, leading to its official categorization as a global pandemic on March 11 [1]. By March 17, cases had been reported in all fifty U.S. states and by April 4, the global case count reached one million [1]. It took just three months to reach one million cases. If that seems astonishingly fast, then consider the most recent jump of one million cases, which occurred between July 5 and July 10—just five days [2]. By July 10, there had been 12,446,105 global cases of COVID-19 in a total of 188 different countries and regions [2]. The swift spread of COVID-19 seems almost unimaginable by the numbers, so how can we rationalize and explain these trends?

TRANSMISSION: THE VISIBLE AND INVISIBLE In a scientific brief on July 9, the WHO acknowledged three main routes through which COVID-19 is transmitted [3]. Though direct or close contact transmission and fomite transmission had previously been acknowledged, the WHO also added airborne transmission to the list. All three forms of transmission are related to respiratory secretions and droplets. Respiratory droplets, which are produced whenever a person exhales, contain aqueous components ranging from mucus and saliva to bacteria and viruses [3]. This means that a person transmits a disease such as COVID-19 not only when coughing or sneezing, but also during other expiratory activities such as talking, yelling, or singing. In general, more forceful exhalations produce more droplets: while a cough releases around 3,000 droplets of varying sizes, a sneeze can release as many as 40,000 [4]. Based on their size and the force at which they are released, these droplets may be carrying hundreds to millions of viral particles. Larger respiratory droplets are responsible for close contact pathogen transmission, in which virus droplets that are released by an infected person land near or on the mouth, nose, or eyes of another person. These droplets are also responsible for fomite transmission, wherein droplets settle on and infect surfaces, called fomites [3]. A person who comes in contact with a contaminated surface and proceeds to touch their mouth, nose, or eyes may then become infected. Eventually, larger respiratory droplets which have not made contact with another person settle out of the air. However, the smaller droplets, known as droplet nuclei, are capable of remaining suspended in the air and travelling on air currents, and are ultimately responsible for airborne transmission [3]. Though there have been mixed reports on the presence of SARS-CoV-2 in various air samples—and it remains clear that further research on the specific mechanism of airborne transmission in regards to COVID-19 is required—the WHO

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acknowledges that it likely contributes to transmission, especially in crowded indoor spaces with poor air circulation [3]. Although transmission of COVID-19 may be more easily traced in symptomatic individuals, transmission can also be asymptomatic (spreading from a person who never develops symptoms) or pre-symptomatic (spreading from a person who has not yet devel-

Much like other infectious diseases, this race centered around one number: the reproduction value

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oped symptoms) [3]. Such “invisible” transmission, though naturally difficult to trace, has been suggested to account for 44 percent of secondary infections [5]. This percentage is based on a modelling study which found that the period between successive infections is, in general, equal to or less than the amount of time it takes for an infected person to develop symptoms [5]. Such results provide insight into why COVID-19 spreads so swiftly through the global population, and subsequent reports began to focus on one statistic to categorize and compare the spread: R0.

R-VALUES: THE STATISTIC OF THE SPREAD As the SARS-CoV-2 virus began to spread more rapidly, the race to define its transmission in numbers began. Much like other infectious diseases, this race centered around one number: the reproduction value, also known as the R-value. Initially, the focus was placed on accurately determining R0, the “basic reproduction value.” This statistic considers factors such as the contagiousness of COVID-19, how long an individual remains contagious, and how often infected people come into contact with healthy individuals [6]. The result is an estimate of the number of people that each person infected with COVID-19 will transmit the disease to in a completely susceptible population. If R0 is less than 1, then the disease is dissipating. However, if R0 is greater than 1, then the disease is actively spreading through a population exponentially. On March 6, the WHO indicated that the R0 value for COVID-19 was between 2

13 10

and 2.5, but later retrospective investigations have asserted that the R0 value is most likely significantly higher [7]. Specifically, analyses of patient data from Wuhan, China produced an initial R0 value of around 5.7, and researchers predicted that similarly high R0 values were most likely characteristic of the populations of other countries, especially those with limited health-related restrictions [6]. Yet there is hardly a consensus on this highly sought-after value. Reports on the R0 of COVID-19 in articles and scientific papers range from around 2 to around 6 and possibly greater [6, 7]. Such discrepancies arise from the inherent inaccuracies of this value, which have had mathematicians and scientists divided over its use for many years [8]. For starters, R0 is a very difficult value to calculate and one that is, in the end, an estimate based on complex mathematical models. Between studies, the models used to calculate R0 have not remained consistent. Hence, whether drawing comparisons between different diseases or instances of the same disease, R0 values can be misinterpreted and can in turn lead to misrepresentations of diseases. Further-

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It is for these reasons, among others, that R0 came to be a means of misinformation and misrepresentation in the media

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more, R0 is hardly a static value. It is based on, among other factors, the number of susceptible people in a population, how likely an infected person comes into contact with a susceptible individual, and how easily the disease is transmitted. Thus, not only will a calculated R0 vary spatially based on the characteristics of a region, such as population density and community behaviors, but it will also vary temporally as governments enact travel-related restrictions and infected people recover, potentially lowering the number of susceptible people. Moreover, there has been evidence that the threshold value of 1 does not always hold—some diseases with R-values less than 1 have continued to invade populations while other diseases with R-values greater than 1 have dissipated [8].

It is for these reasons, among others, that R0 came to be a means of misinformation and misrepresentation in the media. Months later, in July, many media sources continue to report on changing R0 values

when, in reality, this terminology is incorrect. Rather, the values reported now are actually the general “effective reproduction value” that takes into consideration a population’s lower susceptibility due to prior infections and government restrictions. Many of these reports focus on a specific time frame and are actually reporting on another variation, Rt , the “effective reproduction value at a specific time t.” Though the misuse of this mixed terminology certainly contributes to confusion and misunderstandings, what is even more concerning to scientists is the sheer importance placed on this singular value.

“R-DRIVEN” GOVERNMENT POLICIES The R-values prominent in the media have appeared just as prominent in the decision-making processes and recommendations of many governments around the world. J. J. Pandit, in an article from the medical journal Anesthesia, remarks that “perhaps for the first time in history, a single statistical measure is now dictating the entirety of the UK governmental policy” [9]. Throughout UK policies, the R-value is repeatedly mentioned. In a five-tier alert system published in May, local restrictions were based on the R-value and case counts [10]. Further, in its five tests for proceeding with reopening that were published in June, the UK government included the requirement for an R-value that was low enough to allow for an increase without a loss of control [11]. With the discrepancies in the models used to calculate R-values and their inherent imprecisions, relying on them for such large policy shifts can be problematic. And this didn’t just happen in the UK. In the United States, independent teams of programmers and university labs built their own models for calculating the Rt distributions across different states, displaying their results on websites for the public [12]. As expected, with the inherent complexities and inconsistencies in calculating R-values, different sources yield different results. For example, the popular website rt.live reports the Rt of California on July 6 as only 1.02, whereas the Rt map from Xihong Lin’s lab at Harvard reports the Rt as 1.30 [12, 13]. The clearest demonstration of the limitations of “R-driven” policies, though, may be Germany. German leaders and policies have continually defined and emphasized the importance of the R-value and lowering it. While this goal is undeniably a good one and successfully lowered the severity of COVID-19 in Germany, the emphasis on R may have led to public misunderstanding. In late June, there was much frenzy surrounding reports that Rt , which had been remaining around and below 1, suddenly soared to around 2.06 [14]. This spike was later attributed to a few local outbreaks, most notably one at a local slaughterhouse, and Germany’s Rt quickly fell to 0.75 by July 5 [14]. The public hysteria that resulted from the high Rt value alludes not only to the shortcomings of the value itself, but also to the dangers of using it as the defining factor of COVID-19 transmission. Since Rt is not a precise estimate, it can exhibit drastic increases and decreases even when the overall number of cases are relatively low or consistent. Such was the case with Germany: a few isolated local outbreaks caused Rt to spike, a statistical change that was initially misunderstood.

LOOKING TO THE FUTURE

REFERENCES

In light of COVID-19, R0 and Rt have found a global audience drawn to the information they strive to provide but which is largely unaware of the inconsistencies and inherent imprecisions of these metrics. It is even more evident that the R statistic may not be as useful in determining policies or constructing reopening plans as the media portrays. It has become clear that policymakers appear to understand this more and more, as they shift emphasis to other statistics and pathways of information. Now it is up to us to do the same.

[1] Timeline of WHO’s response to COVID-19 (6, 30, 2020) Retrieved from https://www.who.int/news-room/detail/29-06-2020covidtimeline [2] The COVID-19 Dashboard by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University (2020) Retrieved from https://coronavirus.jhu.edu/map.html [3] World Health Organization. (2020). Transmission of SARS-CoV-2: implications for infection prevention precautions. Retrieved from https:// www.who.int/publications/i/item/modes-of-transmission-of-viruscausing-covid-19-implications-for-ipc-precaution-recommendations [4] Dhand, R., & Li, J. (2020). Coughs and Sneezes: Their Role in Transmission of Respiratory Viral Infections, Including SARS-CoV-2. American Journal of Respiratory and Critical Care Medicine. https:// doi.org/10.1164/rccm.202004-1263pp [5] He, X., Lau, E. H. Y., Wu, P. et al. (2020) Temporal dynamics in viral shedding and transmissibility of COVID-19. Nature Medicine, 26, 672675. https://doi.org/10.1038/s41591-020-0869-5 [6] Sanche, S., Lin, Y., Xu, C., Romero-Severson, E., Hengartner, N., & Ke, R. (2020). High Contagiousness and Rapid Spread of Severe Acute Respiratory Syndrome Coronavirus 2. Emerging Infectious Diseases, 26(7), 1470-1477. https://dx.doi.org/10.3201/eid2607.200282. [7] World Health Organization. (2020). Coronavirus Disease 2019 (COVID-19) Situation Report - 46. Retrieved from https://www.who. int/docs/default-source/coronaviruse/situation-reports/20200306sitrep-46-covid-19.pdf?sfvrsn=96b04adf_4 [8] Delamater, P. L., Street, E. J., Leslie, T. F., Yang, Y. T., & Jacobsen, K. H. (2019). Complexity of the Basic Reproduction Number (R0). Emerging infectious diseases, 25(1), 1–4. https://doi.org/10.3201/ eid2501.171901 [9] Pandit, J. J. (2020). Managing the R0 of COVID19: mathematics fights back. Anaesthesia. https://doi.org/10.1111/anae.15151 [10] Sabbagh, D. (2020). Boris Johnson announces five-tier coronavirus alert system. Retrieved from https://www.theguardian. com/politics/2020/may/10/boris-johnson-to-announce-five-tiercoronavirus-alert-system [11] Roberts, L. (2020). What are the Government’s five tests, and what happens now they’ve been met? Retrieved from https://www.telegraph. co.uk/politics/0/5-tests-uk-lockdown-government-what-next-stage/ [12] Systrom, K., Krieger, M., & Vladeck, T. (2020). Rt COVID-19. Retrieved from https://rt.live/ [13] Lin, X., Shi, A., Gaynor, S., Li, X., Li, H., Li, Z., & Shyr, D. (2020). Visualizing COVID-19’s Effective Reproduction Number (Rt). Retrieved from http://metrics.covid19-analysis.org/ [14] Robert Koch Institute. (2020). Current Situation Report of the RKI on COVID-19 [Data set]. Retrieved from https://www.rki.de/DE/Content/ InfAZ/N/Neuartiges_Coronavirus/Situationsberichte/Gesamt.html [15] Cote, Jackson. (2020). As many states see dramatic coronavirus spikes, Massachusetts’ transmission rate remains low; Residents shouldn’t let their guard down, though, doctors say. Retrieved from https://www. masslive.com/coronavirus/2020/06/as-many-states-see-dramaticcoronavirus-spikes-massachusetts-transmission-rate-remains-lowresidents-shouldnt-let-their-guard-down-though-doctors-say.html

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Despite their shortcomings, R-values do provide useful information, but this information should not be isolated. When combined with and used in the context of other data such as case counts, death counts, and results of contact tracing, safe and effective policies can be created.

R0 and Rt have found a global audience ...which is largely unaware of the inconsistencies and inherent imprecisions

”

It goes without saying that, if nothing else, R-values have certainly acted as an important motivator. Intense focus on lowering R-values in countries around the world has led to the enactment of policies and restrictions to accomplish this goal. The resulting lower R-values are inevitably accompanied by other beneficial outcomes, such as lower positive tests and decreased hospitalizations [15]. In a way, this objective of achieving a lower R may seem like a game, in which victory is determined by this individual factor. Yet, unlike in such a game, when now, or in the future, R-values fall to low values, we can’t just jump up, proclaim victory, and remove all restrictions. Rather, we must consider the indications of other statistics and data, not just those of this singular statistic. The “game” is not truly won until all of these different factors indicate victory.

CONTEXTUALIZING

Written by Victoria Comunale Illustrated by Megan Zou August 19, 2020

WITH THE

COVID-19 SARS AND MERS OUTBREAKS When we grapple with the uncertainties of new experiences, we can often find comfort and familiarity by reflecting on the past. Although COVID-19 has brought a great deal of change to all of our lives, a global health scare caused by a coronavirus is something that we have dealt with beforeâ&#x20AC;&#x201D;granted, on a much smaller scale. Despite the seemingly endless news of skyrocketing case numbers and the novel challenges of rapid vaccine development, we are gradually piecing together an understanding of COVID-19. This knowledge of COVID-19 is in part formed by reflecting on similar diseases from the past: SARS (Severe Acute Respiratory Syndrome) and MERS (Middle East Respiratory Syndrome).

It has become obvious that many nations were far underprepared for the pandemic that we are currently facing. There are at present over ten million cases worldwide which have resulted in over 600,000 deaths, and the numbers only keep climbing [3]. But unlike previous epidemics and pandemics, we are equipped with todayâ&#x20AC;&#x2122;s advanced knowledge and technology. Not only do we have experience from dealing with previous coronaviruses, but technological progress over the past decade has also put us at an advantageous position with which to control the spread of the virus. For example, while it took months for the SARS genome to be sequenced, it took a matter of weeks for the COVID-19 genome [4].

MERS, SARS, and COVID-19 are all respiratory diseases that have been at the center of public health crises. The underlying viruses that cause these diseases are all coronaviruses, which are a large subset of viruses that affect animals such as bats, camels, and cats and can evolve to infect humans [1]. The specific coronaviruses known to cause disease in humans are MERS-CoV, SARS-CoV, and SARSCoV-2, which cause MERS, SARS, and COVID-19, respectively [2]. Interestingly, the viruses that cause SARS and COVID-19 are so similar that the virus responsible for COVID-19 was named the second of its kind, SARS-CoV-2. Although SARS and MERS did not globally agitate societal norms and activities in the way that COVID-19 has, we can ground our perception of COVID-19 in our prior encounters with these viruses. So how does our situation compare to those of the past, and how can our experiences with previous coronaviruses help us today?

While SARS and MERS are both respiratory illnesses caused by coronaviruses, nothing of this magnitude was experienced during those past outbreaks. SARS resulted in over 8,000 cases and 774 deaths mostly between the years 2003 and 2004 [5]. MERS resulted in 2,519 cases and 866 deaths mostly between the years 2012 and 2013, and about 80 percent of cases were localized in Saudi Arabia [5]. During these outbreaks, all non-essential travel was advised against, yet there were no widespread efforts put in place, such as mask-wearing and social distancing, to stop the spread of these viruses.

Although there were far fewer cases of SARS and MERS than of COVID-19, these diseases had higher mortality rates, with MERS having the highest: about one-third of cases resulted in deaths [5]. Like COVID-19, these previous viruses proved to be more fatal for the elderly: one study has found that SARS had a fatality rate of more than 50 percent for people 65 or older [6].

While we are undoubtedly struggling to get the virus under control, many find hope in the promise of a vaccine. Cautiously optimistic forecasts predict that a vaccine will be developed within a year, as there are currently several potential candidates under consideration [1]. Although SARS and MERS caused global scares during their outbreaks, there still is no vaccine for either, which means that if either virus spreads from animal to human again, there is potential for another outbreak [4]. During both the SARS and MERS outbreaks, preventing spread of the disease was prioritized over the development of vaccines, which are costly to produce [4]. Although there are currently some vaccines in development for MERS, the project for a SARS vaccine was shelved in 2004 because by the time a potential candidate was developed, the outbreak was mostly contained [4]. On the other hand, developing a vaccine for COVID-19 is an extremely high priority for many research and government institutions. Simultaneously, many scientists have also advocated for the continuation of research into a MERS vaccine, given that MERSCoV appears to jump more easily from infected animals to humans than does SARS-CoV [4]. A recent study published in April of this year shows promising results for human trials of a MERS vaccine, with subsequent trials currently underway [10]. Ultimately, further exploration of potential MERS or SARS vaccines could also help inform our current efforts to find a vaccine for COVID-19.

[1] Coronaviruses. (2020, May 7). National Foundation for Infectious Diseases. https://www.nfid.org/infectious-diseases/ coronaviruses/

[2] Naming the Coronavirus Disease. (2020). World Health Organization. https://www.who.int/emergencies/diseases/ novel-coronavirus-2019/technical-guidance/naming-thecoronavirus-disease-(covid-2019)-and-the-virus-that-causes-it [3] Maps & Trends. (2020) Johns Hopkins Coronavirus Resource Center https://coronavirus.jhu.edu/data [4] SARS and MERS. (2020). Baylor College of Medicine. https://www.bcm.edu/departments/molecular-virology-andmicrobiology/emerging-infections-and-biodefense/specificagents/sars-mers [5] COVID-19, MERS & SARS. (2020). NIH. https://www.niaid. nih.gov/diseases-conditions/covid-19 [6] Roos, R. (2003). Estimates of SARS death rates revised upward. CIDRAP. https://www.cidrap.umn.edu/newsperspective/2003/05/estimates-sars-death-rates-revisedupward

The average person infected with COVID-19 propagates the virus to about 2.0 to 2.5 people, while the average person infected with SARS propagated the virus to about 1.7 to 1.9 people [11]. Both R0 numbers, which gauge how contagious an infection is, are much higher than that of MERS, wherein the average person infected with MERS spread the virus to less than 1 person [11]. Because COVID-19 is much more contagious than either SARS or MERS, measures such as contact tracing which are used to effectively control the spread of the virus have only become more important [7]. During the SARS epidemic, practices such as isolating infected individuals and quarantining potentially infectious contacts were employed [8]. During the MERS epidemic, there was a rise in contact tracing efforts to track and isolate infected individuals in countries that were especially affected, such as South Korea [9]. These practices have been largely used during the COVID-19 pandemic, and countries that have already employed aggressive contact tracing and isolation tactics have surpassed other countries in â&#x20AC;&#x153;flattening the curveâ&#x20AC;? [9].

REFE

As we expand our understanding of COVID-19, we are learning that people with milder symptoms or those who are asymptomatic may be unknowingly infecting others with COVID-19. What we have observed is a virus as infectious as the flu, but much deadlier than the flu due to its effects on the lower respiratory tract [7]. While the majority of COVID-19 cases are luckily not as severe as cases of SARS or MERS, this comes with a downside: SARS and MERS were much easier to track and contain since most patients developed severe symptoms that were difficult to go undetected [7].

[7] Why is COVID-19 So Dangerous? (2020). UCI Health. https://www.ucihealth.org/blog/2020/04/why-iscovid19-so-dangerous [8] Chew, S. (2007). SARS: how a global epidemic was stopped. Bulletin of the World Health Organization. https:// www.ncbi.nlm.nih.gov/pmc/articles/PMC2636331/ [9] Fisher M., Sang-Hun C.(2020). How South Korea Flattened the Curve. New York Times. https://www.nytimes. com/2020/03/23/world/asia/coronavirus-south-korea-flatten-curve.html [10] German Center for Infection Research. (2020, April 22). Promising MERS coronavirus vaccine trial in humans. ScienceDaily. www.sciencedaily.com/releases/2020/04/200422132600.htm [11] Petrosillo N;Viceconte G;Ergonul O;Ippolito G;Petersen E. COVID-19, SARS and MERS: Are they closely related? https://pubmed.ncbi.nlm.nih.gov/

Looking to our experiences with SARS and MERS, there is much that we have learned from the past that can inform our knowledge of SARS-CoV-2. At the same time, through further exploration of COVID-19, we may be able to advance our knowledge on all three of these coronaviruses. Hopefully, armed with this knowledge, we can reflect on our past, learn from our mistakes, and better prepare for our future.

Numbers Can Lie: The Suspect Methodol Written by Enoch Jiang Illustrated by Lizka Vaintrob August 25, 2020 As of July 20, 2020, Florida has an official count of 360,394 confirmed cases of COVID-19, as well as 5,183 deaths due to the disease [1]. To be honest, the enormity of these abstract figures is somewhat difficult to grasp, especially when you’re on your third hour of scrolling through news in bed at night. With every new soundbite about how State X has an “explosive” rate of transmission yet Senator Y believes that it’s totally fine for restaurants to allow indoor dining now, the picture that the media paints of the pandemic can become exceedingly unclear. In these times, some of us may wish to resort to the data to obtain a seemingly objective view of the pandemic: total cases, deaths, and the “positivity rate,” the ratio of positive cases to total tests. However, a glimpse into Florida’s embattled and controversial COVID-19 data reveals that numbers, in fact, can totally lie—sometimes to an alarming and deeply consequential degree. As with any disease, it is of utmost necessity to establish a consistent definition for a positive case. The Centers for Disease Control and Prevention (CDC) has a concise definition for what counts as a confirmed case of COVID-19: a person who tests positive for the SARS-CoV-2 virus, specifically from the results of a molecular amplification detection test [2]. The two main figures of interest for case counts are the raw number of positive cases and the positivity rate. While this might seem like a straightforward enough set of ground rules, the Florida Department of Health (FDOH) has made subtle choices in its representation of the data that distort it greatly. One choice was in regards to counting retests: if someone tested positive for COVID-19 multiple times, the FDOH only counted the positive tests as one case [3]. In mid-May, it decided to treat negative retests differently, adding each negative result into the official counts—hence inflating both the negative case counts and total tests administered [3]. The data scientist who created the FDOH’s COVID-19 dashboard was fired soon after, when she reportedly refused to alter the counting methodology to the wishes of her superiors [4]. These events soon drew widespread attention in the news, as well as criticism against the FDOH’s apparent distortion of data and suppression of dissent [4]. This decision, which Florida’s official datbase makes no note of unless one digs deep into the data documentation, generates a cascade of inaccurate interpretations [6]. By including repeat tests in overall testing numbers, readers can acquire the false sense that Florida has tested more people than it really has. This data presentation also depresses the positivity rate by injecting a slew of negative retests into the denominator, making the situation in Florida seem safer than it is, and more importantly, giving the state government license to reopen faster than it may have otherwise. To this day, Florida maintains this counting practice, and it is unclear how many other states across the country do the same [5].

More recently, the FDOH has even begun to flaunt core definitions in its methodologies, specifically that of what constitutes a confirmed COVID-19 case. On July 1, the FDOH quietly began to include results of antigen tests in the case counts, which previously only includ-

edthose from polymerase chain reaction (PCR) tests [3]. PCR tests utilize molecular amplification and are the accepted standard of testing due to their high accuracy [7]. They are also the only testing modality that the CDC accepts as confirmatory evidence for COVID-19 infection [7]. Antigen tests, while faster to operate, can suffer from false negative rates up to 20%; in fact, the CDC specifically notes that their results should not be considered as confirmatory evidence [2]. The inclusion of unreliable testing methods has similar consequences to the FDOH’s decision to include negative recounts, inflating Florida’s total tests relative to states that do not count antigen tests in their testing and case totals. Florida is not the only state that has mixed testing results—Pennsylvania, Texas, Virginia, Georgia, and Vermont have also been found to lump the two together—but several of these states have since separated their data upon having these discrepancies flagged by journalists [5]. The key issue with counting antigen tests, other than their exaggeration of the extent of testing, is their lack of sensitivity [7]. In a medical diagnosis, a test has two main attributes: specificity, its ability to correctly rule out negative cases, and sensitivity, its ability to correctly detect positive cases [8]. Hence, the low sensitivity of antigen tests means that it has a bias toward false negative cases; that is, it is especially prone to missing positive COVID-19 cases. Thus, as expected, the addition of antigen test results to the overall case count has decreased Florida’s positivity rate [4, 8]. This choice not only adds to the inaccuracy of the new data, but also renders it incomparable to the numbers before July 1; any decrease in the positivity rate might not be due to a genuine decrease in COVID-19 prevalence, but rather to flawed antigen test results that the FDOH has introduced into the overall pool of data.

logies Behind Florida’s COVID-19 Data The FDOH has also made another judgment call in its reporting of deaths: it only counts the deaths of Florida residents in the total death count, eschewing non-residents who passed away in Florida [13]. This decision carries epidemiological weight: non-residents who contract the coronavirus and pass away in Florida are just as much vectors of the disease as Florida residents, and the FDOH’s decision to exclude them from the total death count presents an understatement of the extent to which the virus has infected and killed the people in the state. Although non-resident deaths are a small fraction of total COVID-19-related deaths in Florida, their exclusion from the figure misrepresents the degree to which the disease incurs a fatal outcome in its victims [6]. As state officials continue to form policies and navigate reopenings based on case counts, deaths, and positivity rates, it is critical that they— and their constituents—fully scrutinize the veracity and meaning of the data they utilize. A look into Florida’s methods in calculating its case and death counts reveals that in many aspects, the official numbers do not align with what seems most accurate to the definitions they purport to represent. Although Florida may appear uniquely shocking, it is not alone in its various manipulations. Systemic faults pervade states’ presentation of data on COVID-19, and perhaps the best first thing we can do is to become aware of what lies behind the figures on the dashboard.

When it comes to Florida’s methodology of counting deaths due to COVID-19, both specific and systemic factors influence the data. In general, epidemiologists have estimated that deaths either directly or indirectly attributed to COVID-19 are underreported across the United States because they fail to completely account for excess mortality [9]. Excess mortality, defined as any deaths beyond the “normally expected” amount in a population in a given year, has spiked this year [10]. Some of this increase might be explained by the indirect effects of the coronavirus pandemic on hospital attendance: studies have found that the rate of emergency visits for various non-COVID-19 issues decreased significantly as the pandemic raged in the early months of 2020, suggesting that patients are delaying treatment for serious conditions due to concerns about contracting the disease [9]. Although no clear causality can be drawn from this decrease in visits to increased mortality, the CDC has considered it enough of a danger to issue a report [11]. In this way, COVID-19 has shown an ability to indirectly exert negative impacts on the health of even those who are not afflicted with the disease. However, researchers have found that indirect effects like these still do not capture all of the excess mortality, suggesting that COVID-19 may have directly contributed to some of the excess mortality [9]. In fact, many patients who passed away in recent months displayed symptoms of COVID-19, although they did not receive a test to confirm a case [9]. Such patients are officially considered to have a probable positive case and thus a probable death due to COVID-19, and the CDC has urged states to track such cases [7]. Unfortunately, Florida, along with roughly half of the states, does not report probable cases or deaths, making it impossible to quantify the extent to which the disease has truly impacted total mortality figures [12].

REFERENCES [1] Current Situation in Florida. (2020). Retrieved from https://floridahealthcovid19.gov/#latest-stats [2] Coronavirus Disease 2019 (COVID-19) 2020 Interim Case Definition, Approved April 5, 2020. (n.d.). Retrieved from https://wwwn. cdc.gov/nndss/conditions/coronavirus-disease-2019-covid-19/ case-definition/2020/ [3] Ariza, M., & DiMichele, A. (2020, July 16). Florida’s hidden data skews COVID-19 test results. Retrieved from https://www.sun-sentinel. com/coronavirus/fl-ne-positivity-rate-florida-paints-rosy-pandemic-picture-20200715-cpwwngaefzdnpitbs7buh7zsei-story.html [4] Wamsley, L. (2020, June 14). Fired Florida Data Scientist Launches A Coronavirus Dashboard Of Her Own. Retrieved from https:// www.npr.org/2020/06/14/876584284/fired-florida-data-scientist-launches-a-coronavirus-dashboard-of-her-own [5] Alexis C. Madrigal, R. (2020, May 21). ‘How Could the CDC Make That Mistake?’ Retrieved from https://www.theatlantic.com/ health/archive/2020/05/cdc-and-states-are-misreporting-covid19-test-data-pennsylvania-georgia-texas/611935/ [6] Florida’s COVID-19 Data and Surveillance Dashboard. (n.d.). Retrieved from https://experience.arcgis.com/experience/96dd742462124fa0b38ddedb9b25e429 [7] Glassman, R., & Lacan, O. (2020, July 10). Florida’s COVID-19 Data: What We Know, What’s Wrong, and What’s Missing. Retrieved from https://covidtracking.com/blog/florida-covid-19-data [8] Skaik, Y. E. (2008). Understanding and using sensitivity, specificity and predictive values. Indian Journal of Ophthalmology, 56(4), 341. doi:10.4103/0301-4738.41424 [9] Pappas, S. (2020, May 19). How COVID-19 Deaths Are Counted. Retrieved from https://www.scientificamerican.com/article/howcovid-19-deaths-are-counted1/ [10] Weinberger, D. M., et al. (2020). Estimation of Excess Deaths Associated With the COVID-19 Pandemic in the United States, March to May 2020. JAMA Internal Medicine. doi:10.1001/jamainternmed.2020.3391 [11] Hartnett KP, et al. Impact of the COVID-19 Pandemic on Emergency Department Visits — United States, January1, 2019–May 30, 2020. MMWR Morb Mortal Wkly Rep 2020; 69: 699–704. doi: http://dx.doi.org/10.15585/mmwr.mm6923e1 [12] Nguyen, Q. P., & Schectman, K. W. (2020, July 8). Blog: 19

VACCINE DEVELOPM Written and Illustrated by Alice Sardarian July 15, 2020

ince the initial wave of the COVID-19 pandemic, we have hoped for a return to normalcy, wishing for an effective vaccine that will cease all suffering. Without a vaccine, the world can only attempt to slow the rate of infection with measures that have been successful in some nations but which have been met with resistance in others. As of July 14, cases in the United States are at a staggering 3,355,457 and rising at alarming rates, despite the implementation of public health measures [1]. Generating vaccines is an arduous and expensive process, though the urgency of the pandemic has hastened progress. There are currently over 155 vaccines in various stages of development [2]. Beginning with the smallpox vaccine in 1796, vaccines have significantly decreased mortality rates, protecting against infectious agents such as the common flu or the yellow fever. Vaccines help the body generate pathogen-specific protective entities by exposing it to pathogenic components known as antigens. Vaccines take advantage of one or more natural components of the immune system, including humoral and cell-mediated immunity, which respond to infections by employing B and T cells [3]. These cells recognize foreign antigens and launch a response to rid the body of infection, producing antibodies which recognize and neutralize antigens. Vaccines stimulate this active immunity,

gen. Memory B and T cells permit a rapid and ef- like the Murdoch Childrenâ&#x20AC;&#x2122;s Research Institute, fective response to future infection by the patho- are investigating vaccines used to prevent gen that was initially targeted via vaccination [4]. other diseases, such as tuberculosis, for their protective efficacy against COVID-19 [2]. Vaccines come in a variety of forms, all of which trigger the immune response and pro- In 2014, West Africa experienced a largetect the body. There are four primary types scale Ebola outbreak. A vaccine from the which are currently being developed for pharmaceutical company Merck was the first COVID-19: whole virus vaccines, subunit vac- to be approved by the Food and Drug cines, genetic vaccines, and viral vector vac- Administration (FDA) in December cines [5]. Whole virus vaccines, also called 2019, nearly 5 years after live-attenuated vaccines, employ a small dose the outbreak [9]. Large of a weakened version of the virus and most outbreaks of the Zika closely mimic actual infection [5]. This is the virus, transmitted by most effective way of generating long-lasting mosquitoes, immunity; however, due to whole virus expo- occurred in 2015 sure, these vaccines cannot be administered and 2016 [10]. to those with weakened or underdeveloped A vaccine for immune systems. Additionally, whole virus vac- the virus has cines must be refrigerated in order to remain not yet been viable, limiting distribution to areas which have approved. With access to refrigeration. Inactive vaccines are this precedent, a subtype of whole virus vaccines, and use how fast could a dead pathogens to provide primarily short- COVID-19 vaccine term protection, such as for the seasonal flu. be generated? The speed of vaccine Subunit vaccines deliver pathogen-specific development is limited components, such as surface proteins, which by available can be recognized by immune cells [5]. In technologies, prior this instance, the coronavirus has unique spike research, funding, and glycoproteins which may be used in subunit approval of various trial stages. vaccines [6]. Genetic vaccines are relatively The Zika virus vaccine progress was novel and have not yet been used for human halted when the outbreaks ended and funding disease. They are, however, faster to gener- was directed towards other causes [11]. Prior ate than other vaccines, especially since the work on the Ebola vaccine had already been COVID-19 genome has already been identi- conducted prior to the outbreak in 2014, perfied and continues to be extensively analyzed mitting faster vaccine development. The White [7]. To generate genetic vaccines, COVID-19 House recently selected five pharmaceutical DNA or RNA is injected into an individual and finalists to receive significant federal funding is taken up by some of the host bodyâ&#x20AC;&#x2122;s cells, as a part of Operation Warp Speed, hoping leading to the production of antigens coded for to hasten the process of developing a vaccine by the pathogenic genes. These antigens then by eliminating monetary barriers [12]. trigger an immune response and the development of pathogen-specific antibodies, leading Developing a vaccine amidst a pandemic has to immunity. Another type of vaccine, viral vec- certainly sped up the traditional timeline. Where-

REFERENCES

MENT now being shortened and executed in parallel; funding availability has also diminished manufacturer hesitancy such that vaccine development no longer proceeds at the slow pace originally intended to reduce the economic losses of late-stage, failed vaccines [11]. Vaccines progress through preclincal trials,

when they are only tested on animal models; phase I trials, when they are tested for safety, dosage, and their ability to trigger the necessary immune response; phase II trials, when they are administered to a slightly larger human sample size to determine efficacy and demographic specific differences; phase III trials, when they are administered to a large sample size and compared to placebo infection rates; and, finally, approval, when they are reviewed and selected by agencies such as the FDA for widespread manufacturing and delivery. One vaccine produced by CanSino Biologics has received limited approval, and four vaccines, including one jointly being developed by AstraZeneca and the University of Oxford, are in Phase III [2].

Dr. Anthony Fauci, Director of the National Institute of Allergy and Infectious Diseases, expects we will know whether an effective vaccine is available by early 2021 [13]. This may seem like an unlikely timeline, but several vaccines have already shown significant progress. Moderna, the first American company to begin human testing, is nearing Phase III trials [14]. Gilead Sciences is investigating the efficacy of Remdesivir—which has already been approved by the FDA for emergency use—as a nebulizer treatment, expanding its inoculation flexibility beyond intravenous administration in the hospital [15, 16]. With the support of governments, educational institutions, and private investors across the globe, vaccines are quickly progressing through the testing phases whilst hopefully maintaining quality assurance and safe practices. Though vaccine development may be forging ahead, it is important to recognize that their availability may not bring about the expected, miraculous end to this pandemic. The vaccine may offer only temporary immunity. It may only be effective for certain age groups. If available and effective, the vaccine may not be distributed globally in an equitable and efficient manner [17]. Additionally, public health measures will need to be maintained after inoculation to ensure that infected individuals do not once again trigger high rates of infection. Nevertheless, vaccines are an important first step towards a safer and healthier future, as they will limit and ultimately prevent transmission. The race for a vaccine continues to reveal breakthroughs which are occuring at record speeds. The world looks to science and scientists with hope.

[1] Coronavirus Disease 2019 (COVID-19) in the U.S.. Centers for Disease Control and Prevention. (2020). Retrieved from https://www.cdc.gov/coronavirus/2019-ncov/cases-updates/cases-in-us.html. [2] Corum, J., Grady, D., Wee, S., & Zimmer, C. (2020). Coronavirus Vaccine Tracker. The New York Times. Retrieved from https://www.nytimes.com/interactive/2020/science/coronavirus-vaccine-tracker. html. [3] Amanna, I., & Slifka, M. (2011). Contributions of humoral and cellular immunity to vaccine-induced protection in humans. Virology, 411(2), 206-215. https:// doi.org/10.1016/j.virol.2010.12.016. [4] Ratajczak, W., Niedźwiedzka-Rystwej, P., Tokarz-Deptuła, B., & Deptuła, W. (2018). Immunological memory cells. Central European Journal Of Immunology, 43(2), 194-203. https://doi.org/10.5114/ ceji.2018.77390. [5] Vaccine Types. Vaccines.gov. (2020). Retrieved from https://www.vaccines.gov/basics/types. [6] McPherson, C., Chubet, R., Holtz, K., Honda-Okubo, Y., Barnard, D., Cox, M., & Petrovsky, N. (2016). Development of a SARS Coronavirus Vaccine from Recombinant Spike Protein Plus Delta Inulin Adjuvant. Vaccine Design, 1403, 269-284. https://doi. org/10.1007/978-1-4939-3387-7_14. [7] Schmidt, C. (2020). Genetic Engineering Could Make a COVID-19 Vaccine in Months Rather Than Years. Scientific American. Retrieved from https:// www.scientificamerican.com/article/genetic-engineering-could-make-a-covid-19-vaccine-in-months-ratherthan-years1/. [8] Viral Vector Vaccines. Global Health Primer, Emory Institute for Drug Development. Retrieved from http:// www.globalhealthprimer.emory.edu/targets-technologies/viral-vector-vaccines.html. [9] Branswell, H. (2019). FDA Approves an Ebola Vaccine For the First Time. Scientific American. Retrieved from https://www.scientificamerican.com/article/fdaapproves-an-ebola-vaccine-for-the-first-time/. [10] Kennedy, R., Ovsyannikova, I., & Poland, G. (2019). Zika Vaccine Development: Current Status. Mayo Clinic Proceedings. Retrieved from https:// www.mayoclinicproceedings.org/article/S00256196(19)30483-5/fulltext. [11] Lurie, N., Saville, M., Hatchett, R., & Halton, J. (2020). Developing Covid-19 Vaccines at Pandemic Speed. New England Journal Of Medicine, 382(21), 1969-1973. https://doi.org/10.1056/ nejmp2005630. [12] Sanger, D., & Weiland, N. (2020). Trump Administration Selects Five Coronavirus Vaccine Candidates as Finalists. The New York Times. Retrieved from https:// www.nytimes.com/2020/06/03/us/politics/coronavirus-vaccine-trump-moderna.html. [13] Miller, S. (2020). “We will at least have an answer” in the winter whether a COVID-19 vaccine works, Fauci says. NBC News. Retrieved from https://www. nbcnews.com/health/health-news/fauci-covid-19vaccine-we-will-least-have-answer-winter-n1233024. [14] Jackson, L., Anderson, E., Rouphael, N., Roberts, P., Makhene, M., & Coler, R. et al. (2020). An mRNA Vaccine against SARS-CoV-2 — Preliminary Report. New England Journal Of Medicine. https://doi. org/10.1056/nejmoa2022483. [15] Gilead Sciences Update On The Company’s Ongoing Response To COVID-19. Gilead Sciences. (2020). Retrieved from https://www.gilead.com/ purpose/advancing-global-health/covid-19. [16] Reuters. (2020). Gilead Begins Testing Inhalable Form of Remdesivir for COVID-19. The New York Times. Retrieved from https://www.nytimes.com/ reuters/2020/07/08/us/08reuters-health-coronavirus-gilead-remdesivir.html?searchResultPosition=1. [17] Irfan, U. (2020). Why a vaccine may not be enough to end the pandemic. Vox. Retrieved from https://www.vox.com/2020/6/3/21258841/coro-

SEX IN THE TIME OF CO I would not recommend being in your 20â&#x20AC;&#x2122;s and being in a pandemic simultaneously. An important aspect of being a young adult is building connections and experiencing new things (as is all of life), activities that are curtailed during quarantine and the spread of the novel coronavirus. COVID-19 is novel in that the virus is biologically new, but also in that none of the current living generations have ever before lived through a pandemic. Coronavirus ripping through the world has affected and altered many spheres of life. It has especially exposed racial and class disparities that characterize the United States, as Black and brown people and people of lower socioeconomic classes are disproportionately affected by the virus [1]. This inequality is undeniable, and further, people everywhere have never grappled with the social and mental ramifications that quarantine, working from home, high levels of unemployment, and the mortal threat that the virus poses. As the pandemic persists and forms what feels like a new normal mode of interaction with others outside of our roommates or family, the virusâ&#x20AC;&#x2122;s effect on social and mental well-being is yet to be fully understood. Precautions surrounding the virus implicate human intimacy, both social and sexual. Humans are social creatures, and socialization, although not always prioritized over productivity in oneâ&#x20AC;&#x2122;s job in American society, is necessary for mental and physical well-being. What happens to intimacy when the pandemic takes away our typical means of connecting? [2]. Social intimacy comes in large part from interactions with friends and family, but during a pandemic, social distancing and limiting exposure prevents these interactions, especially for those particularly vulnerable. In fact, 90 percent of Americans report feeling impacted by the virus, while 44 percent of Americans report that their lives have changed in a major way [3]. Typical activities like movie-going, restaurant dining, and even hugging as a greeting another are severely limited. Physical distance also inhibits love

guages like quality time and physical touch. Calls for physically distancing and the isolation that quarantine brings, in complete contrast to a healthy social life, are bound to cause an increase in mental health issues [4]. Technology helps fill the hole that social distancing and limited exposure cause: video calls, calling in general, social media, and the means of communicating long-distance allow people to feel a little less lonely [5]. But since humans are social creatures, it is more than likely that technology will not be enough to fulfill social needs, and that we need contact beyond the screen. Finding the balance between risk and complete, unhappy isolation is the next logical step in navigating life during a pandemic. Sexual and romantic relationships doubtlessly contribute to human intimacy and mental and overall well-being as well, yet like social intimacy, sexual interactions exist very differently during a pandemic. As many consenting adults might predict, happiness and sex are strongly linked: people are generally happier when engaging in more sex and in a higher quality of sex [6]. Some adults might be quarantining with their partner, and assuming that these people are having more sex and better sex, they would be happier as a result of this intimacy. Yet as of 2017, 45.2 percent of Americans were not in committed relationships, implying that ordinarily, these adults would look to fulfill sexual intimacy with people outside of the home in which they are quarantining [7]. Social distancing obviously does not allow for sexual social needs. Not only does more and better sex lead to happier people, but people might also use sex as a way to cope with the abundance of uncertainty surrounding the pandemic, as well as a way to fill the void of (social) intimacy [8]. Just when adults might want to turn to sex, they might not be able to participate in this natural, healthy, and happiness-driven act. However, in lieu of an all-or-nothing ap-

to sex, minimizing risk and providing relevant information ground public health. There is no denying that pleasure (both social and sexual) is linked to public health as well [9]. Public health officials have worked on providing information on how to decrease risk of exposure to coronavirus while still engaging in healthy sexual activity. The NYC Department of Health ranked sex according to safety, in which sex with yourself tops the tier [10]. Otherwise, virtual sexual relations and abstaining from in-person meetups with

ORONA

Written by Ellen Alt Illustrated by Rebecca Siegel July 22, 2020

people you are not immediately close to are recommended. The officials also provide information and resources on how to monitor yourself and your symptoms if you do meet up with people not immediate to you, given the importance of sex as it relates to health and avoiding an all-or-nothing approach to exposure. Avoiding shaming and veering towards a sex-positive, risk-aware approach might be the way we start having casual sex again [11]. If we cannot deny the positive impact that sex has on mental health and happiness, we must determine a way to have sex despite distancing as the pandemic requires, as long as we are aware of the risk associated with it. Sex itself doesn’t come without risk—sexually transmitted diseases and infections require precaution and prevention. Young adults like myself and anyone else sexually active can agree that not having casual sex as an option detracts from being just that—a sexually active adult. Just as the NYC Department of Health is now, activists promoted a safe approach to sex during the HIV epidemic in the 1980’s [12]. Similar to current guidelines to limit exposure and try sexual activities with less contact, the 1983 pamphlet acknowledged the need for pleasure and provided instructions on safe means to go about it. Even decades later, we must understand the value of risk awareness in place of abstinence—transparency is key.

However, in lieu of an all-or-nothing approach to sex, minimizing risk and providing relevant information ground public health.

The pandemic is not ideal for anyone and is detrimental to health and safety, but navigating spheres like safe sex is possible via awareness and managing risk. Like the promotion of safe sex during the HIV epidemic, like safe-sex guidelines now, and like new infastructure for the new normal, balancing risk is the basis of public health. We cannot return to a normal, coronavirus-free, vaccine-distributed world yet, so we must learn to exist in and navigate through our new normal world. So have fun, but not too much—let’s stay happy and healthy.

References [1] COVID-19 in Racial and Ethnic Minority Groups. (Jun. 25, 2020). Retrieved from https://www.cdc.gov/coronavirus/2019-ncov/need-extra-precautions/ racial-ethnic-minorities.html [2] Winder, I C & Shaw, V. Coronavirus: experts in evolution explain why social distancing feels so unnatural. (Mar. 25, 2020). Retrieved from https://theconversation.com/coronavirus-experts-in-evolution-explain-why-social-distancing-feels-so-unnatural-134271 [3] Most Americans Say Coronavirus Outbreak Has Impacted Their Lives. (Mar. 30, 2020). Retrieved from https://www.pewsocialtrends.org/2020/03/30/most-americans-say-coronavirus-outbreak-has-impacted-their-lives/ [4] Galea S, Merchant RM, Lurie N. The Mental Health Consequences of COVID-19 and Physical Distancing: The Need for Prevention and Early Intervention. JAMA Intern Med. 2020;180(6):817–818. doi: 10.1001/jamainternmed.2020.1562. Retrieved from https:// jamanetwork.com/journals/jamainternalmedicine/fullarticle/2764404 [5] Cross, E S & Henschel, A. The neuroscience of loneliness - and how technology is helping us. (Apr. 17, 2020). Retrieved from https://theconversation.com/the-neuroscience-of-loneliness-and-how-technology-is-helping-us-136093 [6] Cheng, Z & Smyth, R. Sex and Happiness. (2014). Retrieved from https:// www.monash.edu/__data/assets/pdf_ file/0004/925699/sex_and_happiness.pdf [7] Unmarried and Single Americans Week: Sept. 17-23, 2017. (Aug. 14, 2017). Retrieved from https://www.census.gov/newsroom/ facts-for-features/2017/single-americans-week.html [8] Rodriguez, M. We Need a Plan for How to Have Casual Sex Again. (May 28, 2020). Retrieved from https://www.thebody.com/ article/casual-sex-covid-19 [9] Porta, M. Public Health Is Not Afraid of Pleasure. (Feb. 2020). Retrieved from https:// ajph.aphapublications.org/doi/10.2105/ AJPH.2019.305496 [10] Safer Sex and COVID-19. (Jun. 8, 2020). Retrieved from https://www1.nyc.gov/assets/doh/downloads/pdf/imm/covid-sexguidance.pdf [11] Marcus, J. Quarantine Fatigue is Real. (May 11, 2020). Retrieved from https://www. theatlantic.com/ideas/archive/2020/05/ quarantine-fatigue-real-and-shaming-peoplewont-help/611482/ [12] Berkowitz, R and Callen, M. How To Have Sex in an Epedemic. (May, 1983). Retrieved from https://joeclark.org/dossiers/ howtohavesexinanepidemic.pdf

A Look Towards the Skies: The E

OVID-19 has brought many uncertainties to the often taken-forgranted normalcy of daily life. Its impact around the world has been pronounced, yet its larger legacy and effects are still a mystery. Despite the many immediate constraints and pressures the virus has placed on our daily lives, many are wondering if there are upsides to this seemingly chaotic situation. One of the questions that have been raised is whether the effects of the virus will positively impact air quality.

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The carbon footprint for flights is quite large and quite needless at this time, considering that the number of flights greatly exceeds

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Although the answer is difficult to gauge, many studies have already emerged to paint a clearer picture of the situation before us. One of the obvious effects of the virus is its impact and strain on the economy. Gross domestic product (GDP) growth in advanced economies is projected to fall by 6.1 percent, which is far lower than the negative 3 percent growth experienced by advanced economies during the Global Financial Crisis of 2009 [1]. These numbers are optimistic since they assume that there will be an easing of lockdown restrictions in the latter half of the year. Despite the fact that COVID-19 is clearly detrimental to our quality of life, there have been clear patterns that link a decrease of carbon emission levels with the current pandemic. In China, a 25 percent reduction in carbon emission levels was noted at the beginning of its lockdown, and in New York, a 5-10 percent reduction was observed [2]. Yet unlike in previous recessions, the reduction in carbon emissions is not due to the decreased production of manufacturing and shipping, but rather due to declines in transportation [5]. Transportation accounts for a quarter of carbon emissions annually; thus, a widespread lockdown that has limited the mobility of many people is a driving force behind the decline in carbon emissions. Although the usage of transportation has decreased, it is not as low as it should or could be. “Ghost flights,” which are nearly empty flights, continue to fly due to intervention by the federal government [4]. Although demand for flights has decreased, due to the coronavirus relief package, airlines must continue providing a minimum number of flights to their usual destinations as to stimulate the airline industry. The carbon footprint for flights is quite large and quite needless at this time, considering that the number of flights greatly exceeds passenger demand.

Despite the presence of these “ghost flights,” air pollution is still at a low [4]. But will these effects last? The answer largely depends on how long restrictions continue and how governments respond to stimulate the economy. Transportation will likely continue to be down for the duration of the lockdown, but an increase in activity in other sectors such as manufacturing and shipping may counterbalance these positive effects. In China, a stimulus program using billions of tons of steel and cement for construction has already caused a spike in China’s air pollution [2]. Stimulus programs put in place in other countries after the lockdown eventually ends could have similarly adverse effects. However, the effects of other factors that affect pollution, such as food waste, are yet to be determined. With many grocery stores lacking or running out of items, it is possible that this pandemic could alter the mindset of a larger population and make people more conscious of their food and product waste. The global food system is responsible for about

Effects of COVID-19 on Air Pollution

one-third of human-caused greenhouse gas emissions [2]. This is due to the methane that is produced when unused food is left to rot as well as the carbon that is emitted during the growing process and transportation of the food [2]. Scientists estimate that a 7.6 percent decline in emissions is needed every year to stop global temperatures from rising, and while the forecast for this year is around 5 percent, unknown effects such as these may lead to a further decline in this year’s emissions [5]. Even though it seems that air pollution is an intangible issue in comparison to the more immediate concern of the virus, tackling air pollution could be a crucial part in actually ending the lockdown. Researchers in the United States are currently investigating the relationship between air pollution and COVID-19, and evidence suggests that pollution has led to more deaths during this pandemic than if there were no pollution [3]. Studies have indicated that this effect may be due to the negative impact that pollution has on respiratory health, and also due to the fact

“

Written by Victoria Comunale Illustration by Rebecca Siegel August 17, 2020

Researchers in the United States are currently investigating the relationship between air pollution and COVID-19, and evidence suggests that pollution has led to more deaths...

”

that pollution particles may be vehicles of transmission for the virus. Although there are many unknowns to the question of air pollution, further addressing this issue may reciprocally benefit the fight against COVID-19. Pollution levels have already decreased due to less use of transportation, so now would be a perfect opportunity to take advantage of the situation and further lower emission levels, starting by reducing the amounts of “ghost flights.” This would not only help climate change, but may help improve public health in the long run.

References

[1] The Great Lockdown: Worst Economic Downturn Since the Great Depression. (2020, April 21). Retrieved from https://blogs.imf. org/2020/04/14/the-great-lockdown-worst-economic-downturn-since-thegreat-depression/ [2] Has coronavirus helped the environment? (n.d.). Retrieved from https:// www.bbc.com/future/article/20200422-how-has-coronavirus-helped-theenvironment [3] How air pollution exacerbates Covid-19. (n.d.). Retrieved from https:// www.bbc.com/future/article/20200427-how-air-pollution-exacerbatescovid-19 [4] Joselow, M. (2020, April 23). “Ghost Flights” Haunt the Skies, Enlarging Carbon Footprints. Retrieved from https://www.scientificamerican.com/ article/ghost-flights-haunt-the-skies-enlarging-carbon-footprints/ [5] Storrow, B. (2020, April 24). Why CO2 Isn’t Falling More during a Global Lockdown. Retrieved from https://www.scientificamerican.com/article/whyco2-isnt-falling-more-during-a-global-lockdown/

Combatting COVID-19 with

Machine Learning Written by Serena Cheng Illustrated by Emily Wang July 22, 2020

achine learning (ML) has indisputably been one of the fastest growing areas of computer science in the past two decades. It’s an oft-heard buzz word that has actually lived up to its reputation as a revolutionary advancement in computation. ML’s many applications range from content recommendation to algorithmic investing, language translation to medical diagnostics, and gaming to weather forecasting. The reason ML can be applied to such a wide array of disparate industries is that in their most basic conceptual form, ML methods work by receiving data, forming and iteratively improving on an abstract representation of the input data, and outputting probabilistically likely data based on the abstract representation, or model. The common denominator that draws these varied industries to ML? They all produce data, and lots of it. An event that has been producing vast amounts of data is, of course, the ongoing COVID-19 pandemic, which as of July has taken the lives of more than 600,000 people worldwide [1]. The unpredictability and complexity of the situation, coupled with the overflow of data it has produced, have both welcomed and facilitated ML as an effective tool to counter the virus. From the initial outbreak, ML research has been undertaken to detect, forecast, and analyze the virus and has continued to be exceedingly important in assessing and minimizing its associated public health risks. With so many factors at play, the process of quantifying relevant information and events in order to gather data points is necessary for a computer to learn about a given

phenomenon. While humans would not be able to make sense of or meaningfully analyze the massive amounts of data available, the computing power of modern machines and their strictly quantitative approach to modeling data allows them to discover hidden patterns and make better inferences about the data than we ever could. Subsequently, the more data to learn from, the more accurate ML models can become in classifying data and making predictions.

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Diagnosing Cases with COVID-Net

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As mentioned earlier, ML models output data after learning from input data. In the area of supervised learning—learning from data that is entirely known as opposed to data that is only partially known—the input data is called training data. The training process, during which the model is improved, involves tweaking parameters and calculating errors. This process continues for as long as it takes for the model to achieve a desired training loss, or measurement of accuracy, on the training data. Finally, new test data is passed through the model, which outputs either a classification of or prediction from the data. For example, given an image of a patient’s chest X-ray, a classifier could output a diagnosis of “Normal,” “Pneumonia,” or “COVID-19.” In fact, this is exactly

what COVID-Net, a deep convolutional neural network designed by researchers at the University of Waterloo, does [2]. First, what is a convolutional neural network? A neural network is a computer model that simulates the neuron-to-neuron interactions that take place in the human brain and that, as the name suggests, form a network of neurons. The neurons are arranged in multiple successively connected layers, each of which adds a degree of nonlinearity and thus model complexity. Data is fed into each neuron in a layer, which modifies and aggregates the data before feeding it to each of the neurons in the next layer. The exact way in which each neuron alters incoming data determines the network’s final output and is the variable that is learned during the initial training period. In the case of COVID-Net, the final layer contains three neurons, each of which spits out a single number: the probabilities of the input chest X-ray belonging to someone who is normal, has pneumonia, or has COVID-19 [2]. Then, the outcome with the highest probability is selected as the final output. Convolutional neural networks are a special class of neural networks that are especially effective in processing image data because they examine larger features of images rather than just their individual and independent pixels. On its test data set, COVID-Net achieved 93.3 percent classification accuracy across all three diagnoses and 98.9 percent positive predictive value (PPV)—the percent of positive cases classified as positive—for COVID-19 cases [2]. The high PPV for COVID-19 results indicates a low chance of false positives, which would be important in minimizing unnecessary hospitalization counts in a time when hospital resources are stretched extremely thin [2]. Moreover, while the exact values of the parameters assigned to neurons in a neural network are usually uninterpretable by humans due to the sheer number of data features, COVID-Net was designed with GSInquire, an explainability technique that provides critical insight into the relationships between features and outputs that is parseable by humans [2]. Explainability is highly desired of ML models because it helps answer more causal questions by generating an interesting interpretation of the data rather than just discriminately crunching data. By uncovering key visual indicators in X-ray images that are potentially associated with the virus, COVID-Net can possibly help doctors improve screening speed and accuracy [2].

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Forecasting Trends with SuEIR

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Besides analyzing individual cases, ML has also been targeted toward forecasting COVID-19 infection and mortality rates in large populations with impressive accuracy. Researchers at the UCLA Statistical Machine Learning Lab designed a modified Susceptible, Exposed, Infectious, Removed (SEIR) model, which is traditionally used to mathematically model epidemics over time [3]. Their model predicts the number of confirmed virus cases and deaths in the United States by state over the next couple of months. It is also currently one of several models directly contributing to the CDC’s national COVID-19 death toll forecasts [4]. SEIR models map out the dynamics between four distinct classes of people in a geographic region in order to project changes in infection rate over time [3]. The four classes consist of individuals who are 1) susceptible to the virus, 2)

Figure 1

exposed to the virus, 3) infectious with the virus, or 4) recovered, deceased, or immune to the virus [3]. Before training SEIR models, researchers use their understanding of a given epidemic to define fixed mathematical relationships between the rate of change of the size of each of these classes and the sizes of the classes themselves at any given time. These equations vary from model to model, but are generally quite simple and intuitive. For example, in UCLA’s model, the number of susceptible individuals is known to decrease by a percent proportional to the fraction of infected individuals at every unit of time [3]. Those susceptible individuals will join the infected group, and this change is reflected in the model by a symmetric increase in the number of infected individuals at every unit of time [3]. With the relationships between input data already drawn out prior to any training, what learning actually takes place? In reality, these equations contain additional parameters (shown as β, σ, γ, and μ in Figure 1) with initially unknown values that adjust the degree of expected population movement between the groups of interest [3]. It is these parameters that provide the margin of uncertainty in which the model can learn via training. The researchers introduced a fifth class, which has been observed to be highly influential given COVID-19’s unique and significant asymptomatic presence: the Unreported Recovered group [3]. This addition gives the model its name— SuEIR. The SuEIR model is trained on reported data until its parameters are sufficiently accurate, and then its final predictions of infection rate are used to forecast COVID-19 cases and deaths. Notably, in early-April, the SuEIR model had predicted a slowdown in new cases in California starting in mid-April and a leveling off of new cases by mid-May [5]. Although we did see fewer daily case counts late-April and early-May, cases started gradually increasing again after then [6]. SuEIR did not take into account the national wave of reopening policies, including California’s Phase 2 reopening plan, which began on May 8 and saw the opening of lower risk workplaces including retail and manufacturing, and which likely played a role in the increase in case counts [7, 8].

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Accounting for Quarantine with SIR

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While traditional SEIR models cannot accurately factor in the effects of reopening policies or stay-at-home orders, more complex models can circumvent this limitation by stitching neural networks into their population dynamics equations. Researchers at MIT have developed a neural network augmented SIR model—SEIR sans the exposed group—to quantify the effects of quarantine and social distancing regulations in several different countries [9]. The neural network that they designed uses infection and population data to measure the quarantine strength of a region, which informs the infectious variable in the SIR model [9]. The researchers call this added feature “quarantine control” [9]. The quarantine control model was trained on data from Wuhan, China as well as aggregate data from Italy and South Korea taken starting from the 500th reported infection in each of these regions, which occurred around January and February [9]. In comparison with classical SEIR and SIR models, the quarantine control model was better able to predict either the stagnation or slowdown of new infected cases in these regions over the course of approximately one month [9]. Furthermore, the data shows a strong correlation between increasing quarantine control strength, government action, and decreasing effective reproduction number—the average number of individuals infected by a single infected individual [9]. Next, the neural network augmented model was used to forecast U.S. case counts in April with varying degrees of quarantine strength that were extrapolated from the quarantine trends of Wuhan, Italy, South Korea, and the U.S. [9]. The U.S. was projected to enforce the weakest quarantine control out of these regions, and as a result, the predicted infection trend in the U.S. using continued U.S. quarantine

References policies and enforcement was worse than any of the trends based on adopted quarantine policies from Wuhan, Italy, or South Korea [9]. These results suggest that stronger quarantine regulations in the U.S. could accelerate plateauing in infected case count and that a relaxing or reversal of quarantine policies could conversely lead to much higher counts with little deceleration [9]. Models such as these are significant for their quantification of the effectiveness of quarantine efforts during the COVID-19 crisis, which conclusively demonstrate their necessity in preventing further casualties and a potential second wave of infections.

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A Public Effort

}

Despite the highly specialized nature of ML research, researchers and experts are not the only contributors to the many ongoing ML efforts to mitigate the effects of COVID-19. The data that these models rely on for training and testing are made accessible by public health agencies, hospitals, universities, companies, and individuals, who collect, publish, and update the data on public platforms such as GitHub and Kaggle. The New York Times maintains a GitHub repository of U.S. case and death counts at state and county levels over time, which has been used by various models, including one that investigates the risk of a second wave across U.S. counties [10, 11]. In addition to sourcing data from doctors, universities, and a radiology software company, the COVID-Net initiative also accepts submissions of chest X-rays from the public as it continues to research and train its models [12]. The combined dataset that it maintains, COVIDx, contains likely the largest number of publicly available positive COVID-19 data samples [12]. Not only is the data available for use by anyone, but so too is the code that the model runs on. Even Columbia students can examine and experiment with the model to write their own neural networks for chest X-ray classification, as they did last spring for the COMS 4771 Machine Learning capstone project in a Kaggle competition [13]. Whether it be following social distancing guidelines or building ML models as a quarantine hobby, there are no small roles played in our collective fight against COVID-19.

[1] World Health Organization. (2020, July 21). Coronavirus disease (COVID-19) situation reportâ&#x20AC;&#x201C;183. https://www. who.int/docs/default-source/wha-70-and-phe/20200721covid-19-sitrep-183.pdf?sfvrsn=b3869b3_2 [2] Wang, L., Lin, Z. Q., & Wong, A. (2020). COVID-Net: A tailored deep convolutional neural network design for detection of COVID-19 cases from chest X-ray images. arXiv. https://arxiv.org/pdf/2003.09871.pdf [3] Zou, D., Wang, Lingxiao, Xu P., Chen, J., Zhang, W., & Gu, Q. (2020). Epidemic model guided machine learning for COVID-19 forecasts in the United States. medRxiv. https:// doi.org/10.1101/2020.05.24.20111989 [4] Centers for Disease Control and Prevention. (2020, July 15). Forecasts of Total Deaths. Retrieved July 21, 2020, from https://www.cdc.gov/coronavirus/2019-ncov/covid-data/ forecasting-us.html [5] Statistical Machine Learning Lab at UCLA. (2020, April 6). Learning epidemic models for COVID-19. https:// covid19.uclaml.org/model.html [6] Almukhtar, S., Aufrichtig, A., Bloch, M., Calderone, J., Collins, K., Conlen, M., Cook, L., Gianordoli, G., Harmon, A., Harris, R., Hassan, A., Huang, J., Issawi, D., Ivory, D., Lai, K. R., Lemonides, A., McCann, A., Oppel, R. A., Jr., Patel, J. K., . . . Virgilio, B. (2020, July 21). California coronavirus map and case count. The New York Times. Retrieved July 21, 2020, from https://www.nytimes.com/interactive/2020/us/california-coronavirus-cases.html [7] California Coronavirus (COVID-19) Response. (2020, June 18). Resilience roadmap. California Department of Public Health. Retrieved July 21, 2020, from https://covid19. ca.gov/roadmap/ [8] Kambhampati, S., Moore, M., & Krishnakumar, P. (2020, July 21). Which California counties are reopening?. Los Angeles Times. Retrieved July 21, 2020, from https://www. latimes.com/projects/california-coronavirus-cases-tracking-outbreak/reopening-across-counties/ [9] Dandekar, R. & Barbastathis, G. (2020). Quantifying the effect of quarantine control in Covid-19 infectious spread using machine learning. medRxiv. https://doi. org/10.1101/2020.04.03.20052084 [10] The New York Times. (2020). Coronavirus (Covid-19) data in the United States. GitHub. Retrieved July 21, 2020, from https://github.com/nytimes/covid-19-data [11] Vaid, S., McAdie, A., Kremer, R., Khanduja, V., & Bhandari, M. (2020). Risk of a second wave of Covid-19 infections: Using artificial intelligence to investigate stringency of physical distancing policies in North America. International Orthopaedics (SICOT). https://doi.org/10.1007/s00264-020-04653-3 [12] Wang, L., McInnis, P., Al-Haimi, A., & Lin, D. (2020). COVID-Net open source initiative. GitHub. Retrieved July 21, 2020, from https://github.com/lindawangg/COVID-Net [13] Subramanian, J. & Austin, J. (2020, March). COMS 4771 COVID challenge. Kaggle. https://www.kaggle. com/c/4771-sp20-covid/overview

RACIAL DISPARITIES in the

COVID-19 PANDEMIC

Written by Sarah Ho Illustrated by Hannah Prensky September 25, 2020 The ongoing COVID-19 pandemic has been all-encompassing and disruptive, generating a staggering amount of information—whether it be about transmission routes, vaccine developments, or infectivity—that seems to be continually evolving [1-3]. However, a disturbing pattern which emerged early in the pandemic and which has only been reinforced by more data is that Black people have been disproportionately affected by COVID-19 [4]. This disparity has not only persisted throughout the pandemic, but also across urban, suburban, and rural areas, impacting all age groups throughout the United States [5]. According to a New York Times analysis of data released by the Centers for Disease Control and Prevention (CDC), Black Americans are three times more likely to get infected by COVID-19 and are almost twice as likely to die from the disease when compared to white Americans [5]. Ultimately, the severity and pervasiveness of this disparity indicate that population-level factors are at play. To partially explain this inequity, one can examine factors which, on an immediate and direct level, make Black populations more vulnerable to exposure to COVID-19. At a time when it is safer to isolate at home and work remotely, Black people comprise a disproportionately large percentage of essential workers, whose jobs cannot be done remotely and which inherently increase exposure to and risk of contracting COVID-19 [6-8]. In addition, traveling to-and-from work often necessitates the use of public transportation, another potential mode of pathogen transmission [9]. These essential jobs are often low-paid, don’t offer paid sick leave, and don’t provide employer-subsidized health insurance [8]. Moreover, Black residential communities are more likely to be densely populated, which can

increase exposure between members of a household and make social distancing difficult, if possible at all [4, 10]. Disparities in COVID-19 racial outcomes are likely also due to differences in access to diagnostic tools: early studies have indicated that Black people are less likely to be referred for and receive COVID-19 tests when they have symptoms of the virus [11]. There have been recorded instances of COVID-testing centers that are concentrated in white residential communities, while testing centers in minority communities had difficulty acquiring the proper protective and testing equipment [11]. These inequities do not appear in a vacuum; rather, the COVID-19 pandemic has starkly exacerbated the racial disparities that have long existed in public health and healthcare. In the United States, a Black child is more than twice as likely to die before their first birthday when compared to white children [9]. The maternal mortality rate for Black women is four-to-five times higher than it is for white women, and is due in large part to preventable causes of death [12].

so that it is more vulnerable to infections such as that of COVID-19 [6, 15]. Allostatic load is defined as the physiological effects of chronic stressors; when the body is stressed, it releases molecules such as norepinephrine, epinephrine, and cortisol, which can prove deleterious over long periods of time [16]. Studies have shown that even when correcting for differences in age and socioeconomic status, Black people, and especially Black women, have higher degrees of allostatic load than white people [16]. Higher exposure to stressors increases susceptibility to chronic diseases such as hypertension, heart disease, diabetes, and asthma, and high cholesterol, all of which are underlying conditions that make one more vulnerable to COVID-19 [16, 9, 13]. Thus, just from examining the effects of racial housing segregation and allostatic load, it becomes evident how structural racism influences health outcomes, whose disparities then go on to become exacerbated in an extenuating circumstance such as a pandemic. Most overarchingly, historical and institutional structures have caused racial and ethnic minorities to have a lower income, lesser wealth, lower levels of education, and higher levels of unemployment than white people [4]. In fact, even when controlling for factors such as education level, socioeconomic status, and employment, studies have found that Black people have more economic hardships and less purchasing power [17].The impact that these economic disparities have on health outcomes is undeniable: Black people, more so than white people, report that seeing a physician is cost-prohibitive [4]. Whereas 8 percent of white Americans don’t have health insurance, 11 percent of Black Americans don’t have health insurance [7].

As an example of how COVID-19 has amplified existing racial health disparities, much of the racial disparity in COVID-19 outcomes has been attributed to the fact that, when compared to white populations, Black populations have higher incidences of the underlying conditions that can make COVID-19 more dangerous [4]. In other words, poorer pre-pandemic health begets worse COVID-19 complications. These disparities in health can be traced back to the racial housing segregation that was legitimized and perpetuated by the twentieth century policy of “redlining,” wherein the allocation of government funding encouraged racially segregated residential communities [7]. Although these policies no longer exist, similarly discriminatory prac- As much as external, sociological factors contribute to tices lead to continued racial housing segregation, which inequitable health outcomes, racism and biases within in turn, leads to disparate health outcomes in Black versus the healthcare system play a significant, deleterious role white people [7]. For example, for minority groups, racial as well. A report by the National Academy of Medicine housing segregation has been linked to asthma, which has found that healthcare compounds social determinants of been shown to increase the severity of a COVID-19 infec- inequitable health, worsening health outcomes in minority patients [18]. A well-documented bias tion [4, 13in healthcare indicates that Black patients, 14]. Housing ranging from children to elderly adults, segregation the COVID-19 pandemic has starkly consistently receive less pain treatment and also contribmedication than their white counterparts utes to an exacerbated the racial disparities [19-20]. A 2016 survey of white medical inequitable students found that half of them believed distribution that have long existed in public that there were physiological differences of resources: between Black and white people and that Black peohealth and healthcare Black people felt less pain [21-22]. Studies ple are more have also shown that the majority of clilikely than nicians are biased against Black patients, white people leading to poorer patient-clinician communication and to have less access to healthy food options, green spaccare [9, 23-24]. In addition, research has found that Black es, and recreational facilities, primary care and specialpatients are more likely to receive lower quality and unty physicians, and medical facilities, all of which lead necessarily deleterious treatment [25]. Racial inequities in to a higher frequency of underlying medical conditions healthcare have existed long before the current pandemic and thus higher vulnerability to COVID-19 [14, 9, 4]. and no doubt have played a role in the current pandemic. In fact, the chronic stress that results from racism and discrimination itself can decrease a body’s immune response These disparities are also evident in biomedical research;

“

even though the label of “scientific research” denotes intellectual objectivity, the reality is that research has also been limited by racial biases and assumptions. For instance, a disease such as sickle cell disease affects approximately 100,000 Americans, most of whom are Black [26]. Despite the fact that the pathogenesis of this disease has been long-established, and that sickle cell disease can have fatal complications, such as stroke or organ damage, the second drug to treat sickle cell disease was approved only three years ago [26]. Regular treatments and screenings have been shown to prevent complications in children with sickle cell disease, but these preventative measures are often not administered [26]. In comparison, a disease such as cystic fibrosis, which is one-third as common as sickle cell disease and potentially fatal as well, does not predominantly affect Black Americans in the way that sickle cell disease does [26]. In comparing the two diseases, a study found that the National Institutes of Health (NIH) awarded eleven times more funding to cystic fibrosis research than to sickle cell disease research per person affected, more publications and clinical trials had been devoted to cystic fibrosis, and five drugs were approved for cystic fibrosis in a span of four years, whereas none were approved for sickle cell disease [27].

will allow officials and healthcare providers to determine more specifically which populations are most at risk [4]. In the long-term, it is imperative that the fields of healthcare and science take steps to correct the racial disparity in health. For example, the training of healthcare providers must more robustly emphasize bias training. With this training, healthcare providers will hopefully be more capable at recognizing and correcting their biases [18]. It is also necessary that Black physicians and scientists become more represented in their respective fields. Due in part to historical abuses by the healthcare system of Black people, such as the Tuskegee syphilis study, Black people tend to have less trust in physicians and the healthcare system at large [25]. However, research has demonstrated that when Black patients are assigned to Black primary care physicians, they received 34 percent more preventative services, due in part to increased trust and communication; this trend also holds true for other people of color, in that racially matched patients and physicians see better outcomes [25]. Unfortunately, Black people are underrepresented in the healthcare field, comprising only 4 percent of current physicians and less than 7 percent of recent medical school graduates [25]. Less than 10 percent of research scientists are Black, and those scientists are less likely to have their research funded [29].

As another example, the most common test for evaluating kidney function measures estimated glomerular filtration rate (eGFR) as an indication of how effectively kidneys can filter blood [28]. However, the algorithm that helps determine whether a kidney is unhealthy enough to war- By focusing attention and resources on programs that invite rant a transplant calculates kidney health differently de- Black high school and undergraduate students to pursue pending on if the patient is Black or not Black [28]. The careers in medicine or research, and through increasing original developers of the algorithm wrongfully assumed institutional support of existing Black doctors and scientists, that Black people have higher muscle mass and thus a medical and scientific fields can hopefully become more higher average kidney function; as a result, if a patient representative and better able to serve communities that is Black, their eGFR score will autohave been historicalmatically be inflated, making their ly marginalized and kidney seem healthier than it actually underserved [29-30]. it is imperative that the fields of is [28]. This assumption is not only a racist overgeneralization of a diverse It is important to note healthcare and science take steps that this article is in no population of people, but it can also mask conditions that need treatment meant to encapto systematically correct the racial way [28]. When funding isn’t devoted to sulate or fully explain research a disease that overwhelmthe complex effect disparity in health ingly affects Black Americans, and that systemic and inwhen the very conception of kidstitutional racism has ney health rests upon discriminatory had on perpetuating beliefs about the Black body, it becomes very clear that racial disparities. There is an abundance of literature—writprejudices and barriers to treatment for Black Americans ten by people far more qualified than myself—which delves are ingrained in healthcare and biomedical research. extensively into the issues that have been mentioned here. How can we most effectively address these disparities? In the short-term, with regards to the COVID-19 pandemic, federal and local governments need to make sure that resources are being allocated such that communities that are the most vulnerable and most affected can get the help they need, rather than allocating resources in a way that perpetuates existent inequalities. Further, healthcare systems need to be held accountable for reporting data that specifies demographic breakdowns such as race and ethnicity—collecting this kind of detailed data

“

Maintaining one’s health during the COVID-19 pandemic has often been encouraged as an individual responsibility: wear a mask, wash your hands, and maintain social distancing. At the same time, though, health outcomes are never completely determined by choice, but rather constrained and influenced by environmental, population, and sociological factors that are deeply ingrained in all facets of one’s life.

REFERENCES [1] Coronavirus: WHO rethinking how Covid-19 spreads in air. (2020, July 8). BBC News. Retrieved from https://www.bbc. com/news/world-53329946. [2] U.S. Food and Drug Administration. (2020, July 15). Coronavirus (COVID-19) Update: FDA Revokes Emergency Use Authorization for Chloroquine and Hydroxychloroquine. Retrieved from https://www.fda.gov/news-events/press-announcements/coronavirus-covid-19-update-fda-revokes-emergency-use-authorization-chloroquine-and. [3] Joseph, A. (2020, June 9). ‘We don’t actually have that answer yet’: WHO clarifies comments on asymptomatic spread of Covid-19. Stat News. Retrieved from https://www. statnews.com/2020/06/09/who-comments-asymptomatic-spread-covid-19/. [4] Centers for Disease Control and Prevention. (2020, July 24). Health Equity Considerations and Racial and Ethnic Minority Groups. Retrieved from https://www.cdc.gov/coronavirus/2019-ncov/community/health-equity/race-ethnicity. html?CDC_AA_refVal=https%3A%2F%2Fwww.cdc.gov%2Fcoronavirus%2F2019-ncov%2Fneed-extra-precautions%2Fracial-ethnic-minorities.html. [5] Oppel, R.A., Gebeloff, R., Lai, K.K.R., Wright, W., & Smith, M. (2020, July 5). The Fullest Look Yet at the Racial Inequity of Coronavirus. The New York Times. Retrieved from https://www. nytimes.com/interactive/2020/07/05/us/coronavirus-latinos-african-americans-cdc-data.html. [6] Golden, S.H. (2020, April 20). Coronavirus in African Americans and Other People of Color. Johns Hopkins Medicine. Retrieved from https://www.hopkinsmedicine.org/health/conditions-and-diseases/coronavirus/covid19-racial-disparities. [7] Scott, D. (2020, July 10). Housing segregation left Black Americans more vulnerable to Covid-19. Vox. Retrieved from https://www.vox.com/2020/7/10/21319873/covid-19-coronavirus-cases-deaths-black-americans-housing-segregation. [8] Stewart, E. (2020, April 23). Essential workers are taking care of America. Are we taking care of them? Vox. Retrieved from https://www.vox.com/covid-19-coronavirus-explainers/2020/4/23/21228971/essential-workers-stories-coronavirus-hazard-pay-stimulus-covid-19. [9] Williams, D. R., & Cooper, L. A. (2020). COVID-19 and health equity—a new kind of “herd immunity”. JAMA. [10] Centers for Disease Control and Prevention. (2020, July 20). Households Living in Close Quarters. Retrieved from https:// www.cdc.gov/coronavirus/2019-ncov/daily-life-coping/living-inclose-quarters.html. [11] Farmer, B. (2020, April 2). The Coronavirus Doesn’t Discriminate, But U.S. Health Care Showing Familiar Biases. NPR. Retrieved from https://www.npr.org/sections/healthshots/2020/04/02/825730141/the-coronavirus-doesnt-discriminate-but-u-s-health-care-showing-familiar-biases . [12] Centers for Disease Control and Prevention. (2019, September 5). Racial and Ethnic Disparities Continue in Pregnancy-Related Deaths. Retrieved from https://www.cdc.gov/media/releases/2019/p0905-racial-ethnic-disparities-pregnancy-deaths.html. [13] Centers for Disease Control and Prevention. (2020, July 17). People with Certain Medical Conditions and Risk for Severe COVID-19 Illness. Retrieved from https://www.cdc.gov/coronavirus/2019-ncov/need-extra-precautions/people-with-medical-conditions.html?CDC_AA_refVal=https%3A%2F%2Fwww. cdc.gov%2Fcoronavirus%2F2019-ncov%2Fneed-extra-precautions%2Fgroups-at-higher-risk.html#asthma. [14] Rashawn, R. (2020, April 2). Why are Blacks dying at

higher rates from COVID-19? Brookings Institution. Retrieved from https://www.brookings.edu/blog/fixgov/2020/04/09/why-areblacks-dying-at-higher-rates-from-covid-19/. [15] Segerstrom, S. C., & Miller, G. E. (2004). Psychological stress and the human immune system: a meta-analytic study of 30 years of inquiry. Psychological bulletin, 130(4), 601–630. https://doi.org/10.1037/0033-2909.130.4.601. [16] Geronimus, A. T., Hicken, M., Keene, D., & Bound, J. (2006). “Weathering” and age patterns of allostatic load scores among blacks and whites in the United States. American journal of public health, 96(5), 826-833. [17] Williams, D. R., Mohammed, S. A., Leavell, J., & Collins, C. (2010). Race, socioeconomic status and health: Complexities, ongoing challenges and research opportunities. Annals of the New York Academy of Sciences, 1186, 69. [18] Agrawal, S. & Enekwechi, A. It’s Time To Address The Role Of Implicit Bias Within Health Care Delivery. (2020, January 15). Health Affairs. Retrieved from https://www.healthaffairs. org/do/10.1377/hblog20200108.34515/full/. [19] Wyatt, R. (2013). Pain and ethnicity. AMA Journal of Ethics, 15(5), 449-454. [20] Singhal, A., Tien, Y. Y., & Hsia, R. Y. (2016). Racial-ethnic disparities in opioid prescriptions at emergency department visits for conditions commonly associated with prescription drug abuse. PloS one, 11(8), e0159224. [21] Hoffman, K. M., Trawalter, S., Axt, J. R., & Oliver, M. N. (2016). Racial bias in pain assessment and treatment recommendations, and false beliefs about biological differences between blacks and whites. Proceedings of the National Academy of Sciences, 113(16), 4296-4301. [22] Villarosa, L. (2019, August 14). Myths about physical racial differences were used to justify slavery—and are still believed by doctors today. The New York Times. Retrieved from https://www. nytimes.com/interactive/2019/08/14/magazine/racial-differences-doctors.html. [23] Maina, I. W., Belton, T. D., Ginzberg, S., Singh, A., & Johnson, T. J. (2018). A decade of studying implicit racial/ethnic bias in healthcare providers using the implicit association test. Social Science & Medicine, 199, 219-229. [24] Hall, W. J., Chapman, M. V., Lee, K. M., Merino, Y. M., Thomas, T. W., Payne, B. K., ... & Coyne-Beasley, T. (2015). Implicit racial/ethnic bias among health care professionals and its influence on health care outcomes: a systematic review. American journal of public health, 105(12), e60-e76. [25] Frakt, A. (2020, July 8). Bad Medicine: The Harm That Comes From Racism. The New York Times. Retrieved https:// www.nytimes.com/2020/01/13/upshot/bad-medicine-the-harmthat-comes-from-racism.html. [26] Carroll, A.E. (2019, August 5). Sickle Cell Disease Still Tends to Be Overlooked. The New York Times. Retrieved from https://www.nytimes.com/2019/08/05/upshot/sickle-cell-disease-overlooked.html. [27] Strouse, J. J., Lobner, K., Lanzkron, S., & Haywood Jr, C. (2013). NIH and National Foundation Expenditures for sickle cell disease and cystic fibrosis are associated with PubMed publications and FDA approvals. [28] Gaffney, T. (2020, July 17). A yearslong push to remove racist bias from kidney testing gains new ground. Stat News. Retrieved from https://www.statnews.com/2020/07/17/egfrrace-kidney-test/. [29] Gladden-Young, A. (2020, June 13). Give Black Scientists a Place in This Fight. The Atlantic. https://www.theatlantic.com/ ideas/archive/2020/06/give-black-scientists-place-fight/613021/ [30] Blackstock, U. (2020, January 16). Why Black doctors like me are leaving faculty positions in academic medical centers. Stat News. Retrieved from https://www.statnews.com/2020/01/16/ black-doctors-leaving-faculty-positions-academic-medical-centers/.

NEW ZEALAND’S TEAM OF FIVE MILLION ERADICATES COVID-19 Written by Hannah Prensky Illustrated by Alice Sardarian July 30, 2020

“Phone, keys, wallet, …mask.” The traditional mental checklist we go through before leaving the house officially has a new component. During the COVID-19 pandemic, forgetting to bring a mask is the new equivalent of showing up to Butler without a CUID. As cases sharply increase across the United States, many states now require maskwearing in public at all times. Recent research by Goldman Sachs showed that, in addition to promoting personal safety, a national mask mandate could even save the United States’ economy from a 5 percent GDP decline [1]. Although right now it is hard for many of us to imagine a world in which we can safely grocery shop without a face covering, that world does exist, and it’s in New Zealand. On June 8, Jacinda Ardern, the Prime Minister of New Zealand, proudly informed her constituents that the country was officially COVID-19-free [2]. This announcement was made more than three weeks after the last COVID-19 patient in the country recovered from the virus and subsequently tested negative [2]. Few other nations have reached the same status (Montenegro, Eritrea, and Fiji have also reported no current active cases), though with almost five million people, New Zealand is the most populous country to do so [3, 13]. After enduring a period of harsh lockdowns, New Zealand’s residents are now free to live a pre-COVID normal without restriction or fear. Since the beginning of this outbreak, health officials around the world have advocated for “flattening the curve,” but New Zealand’s approach was radically different, opting for complete eradication of the virus instead. New Zealand confirmed its first case of COVID-19 on February 28, 2020 [2]. On March 25, when there were only around 200 confirmed cases, the government moved swiftly to close businesses, schools, and borders, imposing a harsh stay-at-home order with

few exceptions [2]. By comparison, New York state announced its shelter-in-place order only after more than 7,100 residents had already tested positive [3]. In Alabama, where the population is equal to that of New Zealand, a stay-at-home order was imposed after almost 1,500 people tested positive [4]. Three months later, that figure has increased to 85,000 people [4]. Although almost every country on Earth has used stay-at-home orders to control the spread of COVID-19, few seemed to work as well as New Zealand’s. How did its policies differ? Much like wearing masks, adhering to stay-at-home orders is a test of cooperation and togetherness, understanding that slowing the spread of a contagious, airborne virus is a group effort. Prime Minister Ardern tackled the pandemic through public engagement, often referring to her country as a “team of five million” [2]. She led by example, delivering COVID-19 updates via Facebook Live videos filmed from her living room, responding to live questions and comments from her “team,” and assuring children that the Easter Bunny and Tooth Fairy were considered essential workers [2]. By imposing lockdowns early, New Zealand positioned itself to follow through on its promises to eradicate the disease, but the country didn’t start out in such a poised place. New Zealand was ill-prepared for a global pandemic in terms of its limited contact tracing abilities and fewer ICU beds per capita than most other developed countries [5]. On March 25, there were only 153 total ICU beds in public hospitals and 520 ventilators in the country, according to the NZ Ministry of Health [5]. Alabama, which is ranked 46th in the United States for health care, had 1,553 ICU. beds and 1,344 ventilators for the same number of people [6, 7]. To address New Zealand’s original shortcomings, the country’s Ministry of Health announced a $500 million investment in the healthcare system and $32 million to increase ICU capacity and equipment (equivalent to approximately $330 million and $20 million USD respectively) [8]. Dr. Ashley Bloomfield, the director-general of health (think New Zealand’s Dr. Fauci), made plans to scale down the number of patients in hospitals by rescheduling elective surgeries and scale up the number of hospital staff by tripling the number of people trained to use ICU machinery [5]. Once again, the success of this effort depended on teamwork. Highly-trained nurses taught each other in cohorts through sessions called “train-the-trainers,” and senior medical professionals, who don’t usually work in hospitals, volunteered to treat patients in the event of a doctor shortage [5]. In addition, the Ministry of Health rolled out a thorough contact tracing program as well as a COVID tracer app [9]. The app works best when more people participate in its usage. Businesses print free QR code posters provided by New Zealand’s government, and citizens scan those codes wherever they go to create a diary of places they visited in a day [9]. Then, if somebody who visited that business tests positive for COVID-19, the Ministry of Health can alert anyone else who was in that location [9]. If somebody has been identified as a close contact of someone with COVID-19, they can expect a phone call from the Ministry with advice on self-isolation, personal wellbeing, and mental health [9]. For a country with millions of people, this system may seem far-fetched or doomed to fail, but it proved otherwise when the virus recently reached New Zealand’s shores.

Along with Ardern’s June 8 announcement regarding no current cases, the Prime Minister stressed that the country “will almost certainly see cases here again, and that is not a sign that we failed, it is the reality of this virus” [10]. Indeed, just a week later, on June 16, New Zealand reported new COVID-19 cases after two women from the United Kingdom entered the country to visit a dying relative [11]. They were the first new infections in 24 days [11]. Quickly, contact tracing operations identified the people who were on the same flight as the infected travelers and isolated one additional family member who came in contact with them [11]. The Ministry of Health assured New Zealanders that the COVID-19 patients did not visit any public location during their visit. Since the first coronavirus case was identified in New Zealand on February 28, the country has confirmed a total of 1,293 cases and mourned 22 deaths [12]. From January 22 to July 8, healthcare facilities across the country performed a total of 422,144 tests and currently hold a stockpile of over a quarter of a million more testing kits [12]. Although the threat of the virus returning is imminent, New Zealanders live knowing that they will be prepared when it happens.

REFERENCES [1] Hatzius, J., Struyven, D., & Rosenberg, I. (2020). Goldman Sachs | Insights - Face Masks and GDP. Retrieved from https://www. goldmansachs.com/insights/pages/face-masks-and-gdp.html [2] Roy, E. (2020). Ardern thanks ‘team of 5 million’ as New Zealand reopens schools and offices. Retrieved from https://www. theguardian.com/world/2020/may/11/ardern-announces-newzealand-will-reopen-schools-offices-and-restaurants-this-week [3] Governor Cuomo Signs the ‘New York State on PAUSE’ Executive Order. (2020). Retrieved from https://www.governor.ny.gov/news/ governor-cuomo-signs-new-york-state-pause-executive-order [4] Almukhtar, S., & Aufrichtig, A. et al. (2020). Alabama Coronavirus Map and Case Count. Retrieved from https://www.nytimes.com/ interactive/2020/us/alabama-coronavirus-cases.html [5] Morton, J. (2020). Covid 19 coronavirus: New Zealand’s intensive care unit capacity revealed. Retrieved from https://www.nzherald. co.nz/nz/news/article.cfm?c_id=1&objectid=12319640 [6] Health Care Rankings: Measuring how well states are meeting citizens’ health care needs. (2020). Retrieved from https://www. usnews.com/news/best-states/rankings/health-care [7] America’s COVID warning system: Alabama. (2020). Retrieved from https://covidactnow.org/state/AL?s=668112 [8] Hon Dr. David Clark. (2020). Backing our health services to combat COVID-19. Retrieved from https://www.beehive.govt.nz/ release/backing-our-health-services-combat-covid-19 [9] New Zealand Ministry of Health. (2020). NZ COVID Tracer app. New Zealand Government. [10] Associated Press. (2020). New Zealand eradicates coronavirus, at least for now. Retrieved from https://www.cbsnews.com/news/ new-zealand-declares-end-coronavirus-prime-minister-jacindaardern/ [11] NZ’s first Covid cases in 24 days came from UK. (2020). Retrieved from https://www.bbc.com/news/world-asia-53059633 [12] New Zealand Ministry of Health. (2020). COVID-19 - current cases. New Zealand Government. [13] Almukhtar, S., Aufrichtig, A. et al. (2020). Coronavirus Map: Tracking the Global Outbreak Retrieved from https://www.nytimes. com/interactive/2020/world/coronavirus-maps.html#countries

“The Chinese Virus”: How Naming

n January of 2020, the coronavirus was only a rumor whispered around the globe — an elusive pneumonia-like virus originating from China. Initially, the number of deaths did not raise worldwide concerns, even as new cases started to appear. A month and a half later, on March 11, 2020, the World Health Organization (WHO) declared COVID-19 to be a global pandemic [1]. That same day, President Trump addressed the country, detailing plans to handle COVID-19’s spread, which included an extension of travel restrictions beyond China to include all European countries [2]. Within that week, the stock market experienced the greatest single-day fall since 1987, prompting Congress to deliberate on a stimulus bill [3]. More than ever before, the

whole country scrutinized Trump’s every move, seeking direction, unification, and assurance. The same week, however, Trump went on to explicitly refer to COVID-19 as the “Chinese Virus.” In various press briefings and tweets, against caution from advisors and to the admonishment of the WHO, he deliberately employed the phrase, in part perhaps in retaliation against Chinese officials who insinuated that the U.S. Army was responsible for COVID-19 [4-5]. This was a critical period — the United States was experiencing exponential infection spikes and had abysmal testing infrastructure — in which collaboration could have helped create more effective preventative measures. Twitter messages containing the phrase racked up well over two million likes and retweets. In his own tweets, Trump tended to appeal to a sense

of patriotism: “We will be stronger than ever before!” and “The onslaught of the Chinese Virus is not your fault!” [6]. While seemingly good-natured in an attempt to build up spirits, the tweets instead attempted to unite Americans against China, rather than with it in solidarity. The repercussions of Trump’s racially charged naming of the virus have yet to be contained. It has fed into the fears and biases of an emotional and deeply politically polarized populace. Across the United States, incidents of racially motivated attacks against Asian, particularly Chinese, Americans are still on the rise [7]. Some examples include the vandalism of business properties and incidents of harassment, such as hurling expletives and slurs in public. The altercations have even escalated

g and Metaphors Shape Prejudice Written by Aida Razavilar Illustration by Emily Wang July 9, 2020

they should all be intentionally disambiguated or separated from connection to any specific country or culture [11]. The WHO’s protocol for the naming of infectious diseases calls for the use of generic descriptive terms (e.g. respiratory) or those related to the causal pathogen (e.g. coronavirus) [11]. It specifically also cites the role that social media plays in the rapid dissemination of information that further amplifies the need for accurate nomenclature [11]. In focusing on the crisis at a national level, Trump’s explicit use of the “Chinese Virus” continues to devalue the importance of upholding particular practices in virus-naming that have extensive downstream effects.

to physical attacks ranging from spitting to stabbings [8]. From late March to mid-April, the Asian Pacific Policy & Planning Council received over 1,500 incident reports of COVID-19-related discrimination, 40 percent of which were from Chinese Americans [9]. This large uptick in xenophobic incidents has been seen not just in the United States but also across the world [10]. The WHO, having experience with large-scale virus outbreaks, readily recognizes the danger of associating viruses or diseases with ethnic ties. To mitigate some of the issues caused by inappropriate naming, similar to many of the problems that are currently unfolding today, the WHO released a statement in 2015 that clearly outlines how viruses should be named [11]. According to these guidelines, though COVID-19 has many different synonyms ranging from the general vernacular “coronavirus” to SARS-CoV-2,

Those who defend the usage of “Chinese Virus” focus on how previous viruses were named based on places or the supposed country of origin, such as the Spanish Flu, West Nile Virus, Zika, and Ebola [12]. Notably, all of these viruses occurred before the official naming doctrine issued by the WHO, but beyond the importance of moving in the positive, progressive direction, there is an even greater issue at hand: the misattribution of blame that results in implementation of unproductive measures. The 1918 flu was coined “the Spanish Flu,” while in actuality, there is some evidence that it originated in Kansas [13]. Since Spain remained neutral in World War I, the Spanish media was the first to report about the 1918 flu in its headlines. Combined with the fact that those who were fighting had little media coverage of the flu, the in-depth Spanish news reporting suggested an association with the cause of the

virus [13]. However, Trevor Hoppe, an assistant professor at University of North Carolina Greensboro (UNC) and researcher within the field of medical sociology, explains that this scapegoating and stigmatization capitalizes on underlying prejudice and can have greater downstream effects. As Hoppe writes naming a virus after “a foreign or minority community is closely related to the desire to wall off those who are viewed as threats of contagion” [14]. Such conflation of the individuals named, with threats to national security, can be harmful by creating too heavy of a reliance on relatively ineffective measures like travel bans and other security-based efforts. While such travel restrictions are an important step, framing a virus as a foreign issue creates a sense of false security that can delay more productive internal precautionary steps, which is precisely what happened with COVID-19. Although current evidence suggests that the virus originated from China - and even then this may not be the case [15] —there are palpable dangers to misinformation in regards to naming especially in the context of previous epidemics. One prime example is the swine flu. In April of 2009, the Egyptian Ministry of Health had ordered the mass slaughter of thousands of pigs within the country in an effort to prevent the spread of swine flu, as well as out of a general fear for the pigs’ ability to host other viruses. However, the swine flu, while originally genetically derived from a form of swine influenza, was actually a human-to-human transmitted virus [16]. Though this may be seen as an extreme reaction, it shows the extent to which a misinterpreta-

“Chinese people in the United States and across the globe bear the brunt of the blame, subjected to discrimination and various forms of endangement to them and their families.”

REFERENCES tion of the facts can have direct repercussions upon the international and governmental response. The naming of a virus is not something that can be changed haphazardly with no consequences to follow. The instantiation of naming the virus the “Chinese” virus creates an association by personifying the virus, a form of metaphor that frames people’s understanding. Leading cognitive linguists and philosophers George Lakoff and Mark Johnson contend that metaphors are the ways in which one is able to organize ideas and create conceptual schemas to arrange thoughts; these metaphors have noticeable effects on sociopolitical attitudes [17]. The use of Trump’s “Chinese Virus” becomes more than just a casual phrase but a personification, a metaphor of the United States as a body subject to infection. On the metaphorical level, this becomes synonymous with infiltration: infiltration by a supposedly foreign and un-American pathogen and people. Trump has recently chosen to adopt war metaphors when discussing COVID-19, implying a binary of good and bad and the extermination of a foreign threat [18]. The effect of reinforcing such implicit biases is wholly unproductive to the effort of preventing more deaths

“Ultimately, politicians have the ethical responsibilty to watch the names, metaphors, and associations they create because the after-effects are ones that will continue to disseminate far beyond the current COVID-19 crisis and this administration.” from the virus and results in a host of greater dangers to minority communities in the United States. While Chinese Americans are explicitly put into the crossfire, others subject to historical disenfranchisement are also susceptible, especially the Black community. In early April, for example, a Black doctor wearing a face mask, who was helping to test the homeless population in Miami, was arrested outside of his own home. Other Black folk resonated with the experience and remain in constant fear of being racially profiled [19]. The implicit system is one of loose associations that feeds upon dangerous biases and prejudices, and this is all further aggravated by the stress surrounding a global pandemic. The war metaphor focuses on fighting an “invisible enemy,” a virus, but the metaphorical framework of war puts us back where we started with the naming of the “Chinese Virus”: a war fought between nations, but with its people disproportionately affected. Ultimately, politicians have the ethical responsibility to watch the names, metaphors, and associations they create because the after-effects are ones that will continue to disseminate far beyond the current COVID-19 crisis and this administration.

[1] Archived: WHO Timeline - COVID-19. (2020, April 27). Retrieved from http://www.who.int/news-room/detail/27-04-2020-who-timeline---covid-19 [2] Taylor, D. (2020, February 13). A Timeline of the Coronavirus Pandemic. Retrieved from http://www.nytimes. com/article/coronavirus-timeline.html [3] Kopecki, D. (2020, March 18). WHO officials warn US President Trump against calling coronavirus ‘the Chinese virus’. Retrieved from http://www.cnbc.com/2020/03/18/ who-officials-warn-us-president-trump-against-calling-coronavirus-the-chinese-virus.html [4] Trump, D. J. (2020). Chinese+OR+Asian)+(from:Realdonaldtrump) - Twitter Search. Retrieved from twitter. com/search?q=%28chinese%2BOR%2Basian%29%2B%28from%3Arealdonaldtrump%29&src=typed_query&f=live [5] Buscher, R. (2020, April 22). ‘Reality is hitting me in the face’: Asian Americans grapple with racism due to COVID-19. Retrieved from https://www.witf. org/2020/04/21/reality-is-hitting-me-in-the-face-asianamericans-grapple-with-racism-due-to-covid-19/ [6] Jeung, R., & Kai, N. (2020). INCIDENTS OF CORONAVIRUS-RELATED DISCRIMINATION. Asian Pacific Policy & Planning Council. [7] WHO issues best practices for naming new human infectious diseases. (2015, May 08). Retrieved from http:// www.who.int/mediacentre/news/notes/2015/naming-new-diseases/en/ [8] Irish, J. (2020, April 16). Outraged French lawmakers demand answers on ‘fake’ Chinese embassy accusations. Retrieved from http://www.reuters.com/article/ us-health-coronavirus-france-china/outraged-french-lawmakers-demand-answers-on-fake-chinese-embassy-accusations-idUSKCN21X30C [9]Andrews, E. (2016, January 12). Why Was It Called the ‘Spanish Flu?’ Retrieved from http://www.history.com/ news/why-was-it-called-the-spanish-flu [10] Parmet, W. E., & Rothstein, M. A. (2018). The 1918 Influenza Pandemic: Lessons Learned and Not—Introduction to the Special Section. American Journal of Public Health, 108(11), 1435-1436. doi:10.2105/ajph.2018.304695 [11] McNeil, D. G. (2009, June 23). In New Theory, Swine Flu Started in Asia, Not Mexico. Retrieved July 16, 2020, from http://www.nytimes.com/2009/06/24/health/ 24flu.html?hp [12] Lakoff, G., & Johnson, M. (2003, April 01). Metaphors We Live By. Retrieved from https://press.uchicago.edu/ ucp/books/book/chicago/M/bo3637992.html [13] Buncombe, A. (2020, March 18). Trump defends using name ‘Chinese virus’ - ‘It’s not racist, it comes from China’. Retrieved from http://www.independent.co.uk/ news/world/americas/trump-coronavirus-china-defend-racist-attacks-chineseamericans-white-house-latest-death-toll-a9409636.html. [14] Wilkinson, A. (2020, April 15). Pandemics are not wars. Retrieved from http://www.vox.com/culture/2020/4/15/21193679/coronavirus-pandemic-war-metaphor-ecology-microbiome [15] Cineas, F. (2020, April 22). Senators are demanding a solution to police stopping black men for wearing - and not wearing - masks. Retrieved from http://www.vox. com/2020/4/22/21230999/black-men-wearing-

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Special COVID-19 Issue: Summer 2020

Articles inside

Contextualizing COVID-19 with the SARS and MERS Outbreaks

Coronavirus: The Basics

Letters from the Editors

Editorial Board in the Age of COVID