Monitoring COVID-19 in Wastewater

While public health responses to the pandemic have focused on preventing person-to-person transmission, initial studies suggest that the SARS-CoV-2 virus could also be spread through wastewater. It has recently been shown that the virus can be found in human feces for up to 33 days after a patient has tested negative for the respiratory symptoms of COVID-19. These findings have two broad implications. First, health officials may need to consider the increased risk of infection that exposure to untreated wastewater poses to communities, particularly vulnerable populations without adequate sewage systems. Second, the detectable presence of SARS-CoV-2 in wastewater could potentially offer public health officials an alternative method to monitor the prevalence of infections among the population, and may serve as an early-warning system for an outbreak. Building on international case studies, KAUST Professor Peiying Hong is working with government partners to test for and monitor SARS-CoV-2 in untreated wastewater in local communities.

Monitoring for SARS-CoV-2 in wastewater has already been demonstrated in the Netherlands, the UK, the UAE, Australia, Switzerland and the US. In Queensland, Australia, for example, researchers used viral RNA samples from a wastewater plant to estimate the number of infected individuals in the surrounding communities. Meanwhile, researchers in Switzerland found viral RNA samples in wastewater in cities that had yet to record significant numbers of infections, indicating that wastewater-based epidemiology could be a useful tool for predicting where COVID-19 hotspots might emerge.

With data from the World Health Organization suggesting that approximately 80% of COVID-19 infections are mild or asymptomatic, there is a pressing need to detect and monitor asymptomatic individuals who may be unknowingly transmitting the virus to others during and after an outbreak has subsided.

Professor Hong and other researchers at the Water Desalination and Reuse Center aim to detect SARS-CoV-2-infected community members who have previously gone undetected under the current quarantine and swab-testing system. In collaboration with the Ministry of Health, Professor Hong’s team has started sampling wastewater collected from KAUST’s campus and in Jeddah. Pending the success of this pilot program, monitoring could be expanded at a national level.

PEIYING HONG Associate Professor of Environmental Science and Engineering and Faculty Member at the Water Desalination and Reuse Center

WE CAN ESTABLISH THE BASELINE ABUNDANCE OF THE VIRUS IN WASTEWATER THROUGH CONTINUOUS MONITORING, AND ANY DEVIATIONS FROM THIS BASELINE COULD INDICATE FUTURE PANDEMICS.

IMPACT

Some of the wastewater testing methods being trialed by Professor Hong’s lab could help Saudi health officials more accurately and efficiently measure infection rates across communities. Wastewater-based epidemiology could also prove crucial in predicting localised outbreaks of the virus before they happen. Studies elsewhere have shown how a significant increase in the concentration of virus RNA in wastewater can act as a kind of early-warning system for a pending outbreak. Testing for the virus in wastewater is also potentially a more efficient and affordable way to monitor SARS-CoV-2 infections in Saudi Arabia, as mass testing programs are costly and place significant pressure on the supply of testing kits and laboratory analysis systems. In the midto long term, the kind of continuous wastewater monitoring framework Professor Hong’s lab is developing could also help government authorities predict future outbreaks of other novel viruses.

IDENTIFYING & REPURPOSING EXISTING TREATMENTS

Social distancing and lockdown measures can only do so much to combat the virus. With over 7 million cases and more than 400,000 deaths as of June 2020, COVID-19 is already one of the deadliest viruses in history. A proven vaccine that can be affordably mass produced and distributed is still many months – or even years – away. In the meantime, scientists are trying to identify existing drugs that can be repurposed to treat COVID-19.

REAGENTS IN THERAPEUTIC TREATMENT: Professor Valerio Orlando’s lab is exploring the possibility of using novel generation antisense oligonucleotides as an adjuvant in the therapeutic treatment of COVID-19. Antisense oligonucleotides (ASOs) are small pieces of DNA or RNA that can bind to specific molecules of RNA and block them. Scientists believe that ASOs could be used to effectively block SARS-CoV-2’s RNA-polymerase complex and thereby inhibit virus replication and transcription. ASOs could also be used to prevent immune systems from being overwhelmed by the virus. This hypothesis stems from observations made in hospitals in Italy and China, where beneficial effects and full remission were reported in some patients treated with anti-inflammatory compounds against rheumatoid arthritis, in combination with conventional antiviral drugs.

When used with traditional antivirals, adjuvants such as ASOs can improve a patient’s response to traditional antivirals currently being used to treat COVID-19. Inflammation is the immune system’s natural response to pathogens. However, in acute infections like those caused by coronaviruses, the high rate of inflammatory response may overtake the capacity of the immune system. Professor Orlando’s team is building on their research into the correlation between inflammation and aging to understand how the body’s own immune response to COVID-19 might be damaging lung tissue, and therefore inadvertently aggravating the disease. To prevent this, his team wants to see if ASOs can help the immune system better control its response to the virus. “We thought to target not the virus itself, but the RNA that are associated with the viral infection, with the hope of seeing the amelioration of the immune response,” he said. So far, Professor Orlando’s lab has identified new targets potentially involved in overactive immune system responses, and modeled how ASOs could be used against them. With help from international and national labs, he and his team hope to identify a novel, effective and low-toxicity adjuvant treatment against COVID-19 in the coming months.

EXISTING TREATMENTS: At KAUST’s Computational Bioscience Research Center (CBRC), Distinguished Professor Takashi Gojobori and Senior Bioinformatician Dr. Intikhab Alam are trying to identify existing drugs that could be repurposed to treat COVID-19 patients. Their work involves mapping the genetic structure of the SARS-CoV-2 virus, comparing and analyzing different types of coronaviruses, and scanning billions of environmental samples for traces of SARS-CoV-2. By doing this, CBRC researchers hope to develop a system to identify drugs that are already approved for human use that can be modified to fight COVID-19. At the heart of this is the KAUST metagenome analysis platform, which can analyze the genetic sequence data of millions of types of organisms.

VALERIO ORLANDO Professor of Bioscience and Principal Investigator of the KAUST Environmental Epigenetics Program

THE COVID-19 PANDEMIC HAS HELPED SOCIETY RECOGNIZE THE VALUE OF KNOWLEDGE; SCIENTISTS HAVE A VOICE NOW.

IMPACT

Though much attention is on the race to find a SARS-CoV-2 vaccine, research at KAUST is also focused on finding treatment options that could be implemented at hospitals far sooner, thereby saving countless lives. The research output from Professor Orlando, Professor Gojobori and Professor Gao’s labs are already providing health officials with viable treatment options, and KAUST has been working closely with government partners to ensure that its research can be integrated into public health interventions. For instance, a member of Professor Gojobori’s team currently serves on the National Committee on Coronavirus Research and has been working closely with the Saudi Center for Disease Prevention and Control.

TAKASHI GOJOBORI Distinguished Professor of Bioscience and Acting Director of the Computational Bioscience Research Center

WE HAVE THE RESOURCES – COMPUTER CLUSTERS, AND SPECIALIZED ANALYTIC AND VISUALIZATION SOFTWARE – AS WELL AS THE EXPERTISE TO MEET THIS GLOBAL CHALLENGE.

XIN GAO Associate Professor of Computer Science, Head of KAUST’s Structural and Functional Bioinformatics Group, and Acting Associate Director of the Computational Bioscience Research Center

REPURPOSING DRUGS

Similar to the research currently under way at the CBRC, Professor Xin Gao is also looking into existing drugs that could be repurposed to treat COVID-19. Drug repositioning – that is, the discovery of new clinical applications for drugs already approved for different therapeutic contexts – could provide an effective shortcut to bring COVID-19 treatments to the bedside in a timely manner. Using computational analysis, Professor Gao and other international researchers recently published a study on existing drugs that are potentially able to counteract SARS-CoV-2 infection, as well as provide insights on their mode of action. Overall, the researchers identified 39 compounds that could be tested experimentally against a SARS-CoV-2 infection. Histone deacetylase inhibitors, chemical compounds with a long history of use in psychiatry and neurology as mood stabilizers and anti-epileptics, were identified as potentially promising drugs that could be repurposed to treat COVID-19.

PREDICTING OUTBREAKS & INFORMING POLICY DECISIONS

Governments around the world have been using cell phone tracking in the fight against COVID-19. This data has been used in several intrusive and non-intrusive interventions, ranging from surveillance of virus carriers to ensure that they are self-isolating, to providing warnings to individuals who may have come in contact with an infected person. The Belgian government, for example, has been tracking its population’s movements at a broader and aggregated level to see if confinement measures are working. At KAUST, researchers want to tap into aggregated cell phone data and social media posts in Saudi Arabia to track population movements and sentiments, and thereby predict potential outbreaks before they happen.

MONITORING MOVEMENTS: Professor Paula Moraga is part of a multidisciplinary KAUST research team that is creating computational models to track the evolution of COVID-19, predict the number of cases in a particular region and help health officials allocate resources accordingly. Though scientists know how SARS-CoV-2 is transmitted, it is still unclear what other factors may contribute to increased infection rates. For example, scientists still do not know if seasonal factors such as temperature or humidity affect transmission rates, as is the case with other diseases such as malaria. This makes predicting and tracking SARS-CoV-2 outbreaks more difficult. Dr. Moraga’s team wants to use population movement data from cell phone tracking to monitor the virus. To respect user privacy, Dr. Moraga’s team will only receive aggregated data from mobile providers. They will also tap into aggregated data from social media and other sources, such as Google searches, where users talk about their symptoms or search for information about treatment. This data can be fed into their computational model to potentially forecast the number of cases and help to allocate resources in the best way. The output will be shared with health officials and the general public through a live dashboard, which will keep track of several virus indicators and information on health facilities, such as the number of beds and ventilators.

MAPPING SENTIMENT: At the Computational Bioscience Research Center, Professor Xiangliang Zhang is also tracking and analyzing online sentiment to develop an early-warning system for COVID-19 outbreaks. Building on her team’s experience using computer models to analyze social media posts on Twitter, Professor Zhang shifted her focus to COVID-19 when the pandemic began. Employing algorithms, machine learning and artificial intelligence, the computational model developed by Professor Zhang’s team can analyze tweets to identify Twitter users’ interests and track changes over time. Amid the ongoing pandemic, Professor Zhang’s team has been using machine learning to analyze the millions of tweets published using hashtags like #coronavirus or #covid19. Like Dr. Moraga’s research, Professor Zhang’s analysis of Twitter sentiment will be fed into a real-time dashboard, where it will be mapped according to color codes. As Professor Zhang explains, “Blue means calm, denoting that users are not very anxious or panicked, whereas red means people are fearful, scared or have negative feelings.”

PAULA MORAGA Assistant Professor of Statistics

PUBLIC HEALTH AUTHORITIES WILL BE ABLE TO SEE HOW THEIR REGIONS ARE DOING AND HOW THEY NEED TO PLAN THEIR RESPONSE TO THIS EPIDEMIC, WHILE THE GENERAL PUBLIC WILL GET A SENSE OF THE REAL SITUATION OF THE DISEASE.

IMPACT

By mid-June, news or sentiments about COVID-19 had been tweeted over 628 million times, yet the data from Twitter and other social media platforms is not being utilized to its full potential. Up until now, governments have been using cell phone data and geolocations to enforce confinement measures and encourage social distancing. When plugged into computational models supported by supercomputing systems like Shaheen II, this kind of data can provide invaluable insight on population movements and sentiment during a virus outbreak. As Dr. Moraga and Professor Zhang’s research demonstrates, data from social media can be used in non-intrusive ways to not only monitor outbreaks, but maybe even predict them.

XIANGLIANG ZHANG Associate Professor of Computer Science at the Computational Biosciene Research Center I WANT TO USE SENTIMENT ANALYSIS AND QUESTIONS FROM TWEETS TO HELP PREDICT OUTBREAKS.

SUPERCOMPUTER SUPPORT

The development of computational models to better understand and track COVID-19 is being supported by KAUST’s supercomputer infrastructure and expertise. At the core of this is Shaheen II, the largest and most powerful supercomputer in the Middle East. Housed at the KAUST Supercomputing Core Lab (KSL), Shaheen II has supported hundreds of users since its launch in 2015, and over 50% of the university’s faculty run projects on the system. The system also supports researchers from nearly 20 external research institutions or corporations across Saudi Arabia. Since the start of the pandemic, a portion of Shaheen II’s capacity and KSL resources have been reallocated to support KAUST COVID-19 research. Underlining the university’s commitment to supporting both local and global efforts to combat the virus, KSL recently opened a call for proposals for COVID-19 research projects that require Shaheen II’s support.