CITRIS Seed Funding - COVID-19 Response

CITRIS Seed Funding COVID-19 Response

Awarded Projects

The following CITRIS COVID-19 Response projects were awarded seed funding in an initial round of 25 projects announced on May 13, 2020. See the announcement here. Six additional projects were awarded on May 15, 2020. Download a list of the projects (PDF Document).

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Clinical Care/Therapeutics
Contact Tracing
Data Analytics/Modeling
PPE/Equipment
Testing

Clinical Care/Therapeutics


“A Multicampus Infrastructure to Advance Telehealth Implementation for Low-Income Californians in Response to COVID-19”
Researcher/s: Hector Rodriguez, Denise Payan, Lorena Garcia | UC Berkeley, UC Merced, UC Davis

On March 6, the President signed H.R. 6074, or the Coronavirus Preparedness and Response Supplemental Appropriations Act, 2020. This bill grants $8.3 billion to address COVID-19 and permits the Secretary of Health and Human Services to allow the patient’s home to be an originating site of care. COVID-19 and H.R. 6074 provide substantial incentives for Federally qualified health centers (FQHCs), which care for low-income Californians, to transition from face-to-face visits to telehealth encounters for chronic care management. FQHCs, however, have historically lagged in their adoption of telehealth due to technological constraints; innovation is needed to improve the implementation and impact of telehealth for low-income patients. The proposed project builds a multi-campus research data infrastructure for tracking telehealth utilization among California’s FQHCs and integrates these data with electronic health record (EHR) data to examine the impact of telehealth implementation on clinical outcomes. The resulting integrated dataset will serve as the foundation for a diverse set of natural experiments examining the impact of COVID-19 and the transition to telehealth utilization on health outcomes for low-income Californians with chronic conditions.

“Augmented Reality Video-Assisted Clinical Care for Remote Management of COVID-19”
Researcher/s: Narges Norouzi, Ian Julie | UC Santa Cruz, UC Davis

This project will demonstrate real-time analysis of video and the ability to identify clinically useful information from the live video stream intended to assist a clinical practitioner in the management of COVID-19 patients. PIs are proposing: 1) Implementation and validation of a mobile application and an API that ingests video streams and augments it with patient’s information such as blood oxygen saturation level, respiratory rate, and heart rate in real-time. 2) Analyzing respiration patterns and oxygen saturation levels of COVID-19 patients and building a predictive model to distinguish COVID-19 from other respiratory and flu-like illnesses.

With the rapid adoption of video-assisted clinical care due to the COVID-19 pandemic, providers need solutions that will help them evaluate the clinical state of the patients. This project will utilize a commercially scalable solution designed for healthcare from Twilio (twilio.com) to establish an omnichannel (text, audio, video, and data) connection between two or more participants with the ability to send the data stream from one of the participants to a process that will perform video analysis in real time.

Contact Tracing


“Discovery of Symptom Phenotypes and Trajectories for COVID-19 Adaptive Interventions”
Researcher/s: Katherine Kim, Xin Liu, Jill Joseph, Joanne Natale | UC Davis Health

The COVID-19 pandemic has been characterized by an unprecedented rapid and global spread with reported high rates of asymptomatic transmission. However, our understanding of symptoms has been flawed due to biased and incomplete data collection leading to ineffective containment and mitigation strategies. In this study, we will create an innovative symptom science platform to collect comprehensive, longitudinal data and testing results and apply cutting edge machine learning methods to predict infection. Symptoms can provide early and highly informative population surveillance if we develop valid and reliable symptom phenotypes and trajectory models based on comprehensive data to predict infection. With these models we can institute adaptive interventions that prioritize testing and health monitoring of those who are most likely to develop illness and help to prevent spread. We will enable the generation of new insights to optimize use of public health and health system resources.

“A Data Scientific Approach to Coronavirus Surveillance: Application to Re-Opening UC Campuses”
Researcher/s: Scott Moura, Raja Sengupta | UC Berkeley

The University of California, the largest university system in the world by student enrollment, consists of 285,000 students on 10 campuses — a population which would rank #72 among U.S. cities. To re-open campus operations during the SARS-CoV2 pandemic, we require a scientific and data-driven surveillance system for early warnings of localized outbreaks. This CITRIS COVID-19 seed project leverages data scientific methods to model, survey, and mitigate potential outbreaks within large organizations. In this seed project, we focus on student populations and course networks. In particular, we model contact networks over course schedules, and utilize graph theory, optimization, and Bayesian methods to determine which courses should move online. The methods developed, however, will scale to other large organizations, and thus provide a toolkit for societal leaders to re-open operations.

Data Analytics/Modeling


“Estimating the Local Spread of COVID-19 around Long-Term Care Facilities in California using Social Interaction Networks with Spatial Information”
Researcher/s: Martin Cadeiras, Miriam Nuño, Diego Pinheiro | UC Davis Health

Long-term care facilities (LTCF) are home to patients disproportionately vulnerable to COVID-19 as they are elderly, frail, and have existing comorbidities such as cardiovascular diseases. At the same time, outbreaks in LTCFs are major threats to the health care system. Preventing the spread of COVID-19 among this population is a priority worldwide but requires accurate predictive analytics on the local spread of COVID-19 around LTCFs to enable the creation of tailored resource allocation and social distancing strategies to each LTCF. Predictive analytics on the local spread of COVID-19, however, are lacking, only simple extrapolations of historical data are available, and current epidemic models assume individuals are equally likely to interact with each other. Our proposed research will create a novel epidemic modeling of COVID-19 that accounts for the social interaction network using human mobility data to generate accurate predictive analytics on the local spread of COVID-19 around LTCFs in California.

“Social distancing and sheltering in place: Using a nationwide smartphone panel with location data to understand population heterogeneity and inform intervention methods”
Researcher/s: Daniel Chatman, Joan Walker, Daniel Rodriguez | UC Berkeley

Recent studies of household responses to Covid-19 have failed to collect data on the underlying structural and economic factors that condition people’s ability to comply with social-distancing and shelter-in-place rules. Such data are needed to mitigate the effects of the pandemic and to safely begin to open the economy. We use a unique sample of more than 100,000 U.S. mobile phone users, taking pre- and post-COVID movement data from GPS traces to measure changes in household activity patterns and correlate those with baseline demographics such as household income, household size, and race/ethnicity. We will then, over a minimum three-month period, repeatedly survey a subsample of individuals in ten metropolitan areas to measure economic well-being, mental health, personality, political orientation, and barriers to sheltering along with documenting changes in activity patterns from GPS traces. This novel research will enable future work on experimental interventions delivered via smartphones to improve compliance.

“Identifying and Quantifying COVID-19 Misinformation”
Researcher/s: Hany Farid, Alexa Koenig | UC Berkeley

Limiting the spread of misinformation relating to the COVID-19 pandemic is becoming increasingly important. Numerous initiatives are now dedicated to identifying and debunking hoaxes and conspiratorial narratives. There is not yet, however, a large-scale quantification of the nature and belief in these narratives. Quantifying the penetration of COVID-19 misinformation is crucial to enable: 1) auditing the efficiency of measures taken by social platforms to fight misinformation; 2) informing research for future events; and 3) increasing efficiency in responding to misinformation with targeted, fact-based communication campaigns. Our social media monitoring infrastructure collects daily content from YouTube, Facebook, Instagram, and Twitter. This content is filtered by a machine-learning algorithm which flags posts related to known misinformation. Suspicious posts are reviewed by our team, providing qualitative feedback to the system. In parallel, we will perform country-specific, large-scale, online surveys to assess the penetration and belief in the most wide-spread misinformation narrative.

“Privacy Guarantees for the use of Personal Location Data in COVID models”
Researcher/s: Joshua Blumenstock | UC Berkeley 

Some of the most accurate models used to forecast the spread of COVID-19 rely on personal location data. These data, typically generated by GPS-enabled smartphones and collected by companies like Google and Facebook, provide unprecedented visibility into the travel patterns of individual users. Our research team is currently involved in several efforts that use these data to improve disease forecasting and guide effective response, including a high-impact collaboration with the World Bank in Afghanistan. However, personal location data are highly sensitive, and there is an active policy discourse about the risks of making such private data publicly accessible. The focus of this proposal is on developing privacy-preserving techniques for the use of personal location data in epidemiological methods. We will develop both the theory and the tools for provably private techniques that simultaneously allow for the use of personal location data to study disease spread without compromising individual privacy.

“Strain-level surveillance of SARS-CoV-2 and RNA viromes in municipal wastewater” 
Researcher/s: Kara Nelson, Jillian Banfield | UC Berkeley

A small but growing number of international researchers is exploring the use of wastewater-based epidemiology (WBE) to track the spread of COVID-19 via sewage surveillance. We bring bioinformatic expertise and experience with high-quality experimental design to bear on this problem. We will collect wastewater samples at treatment facilities around the Bay Area, quantify SARS-CoV-2, and recover genomes that can be used to track the spread of the disease at the level of individual strains. This effort will yield data that could be used to monitor community infection levels as shelter-in-place orders are lifted and to detect future reintroductions of the virus. The work will also contribute to the development and cross-validation of methods needed by the global WBE research community.

“Improving COVID-19 severity forecasting and uncertainty quantification”
Researcher/s: Bin Yu | UC Berkeley

We want to improve upon current COVID-19 forecasting methods by developing interpretable models that can forecast COVID-19 cases, hospitalizations, and deaths for each county. We want these forecasts to be accurate up to a month ahead. We are not aware of any model that is able to do this at the current moment. We currently have methods that are able to forecast county-level deaths accurately up to a week ahead. We propose to extend the time horizon of our forecast using matching and aggregation techniques that have not yet been used in COVID-19 modeling. We will develop a method to compare different models in terms of prediction performance. Furthermore, current COVID-19 methods are not able to provide accurate uncertainty quantification. We aim to overcome this using new techniques informed by our group’s many decades of work in data analysis. (Awarded May 15, 2020.)

“Open-source 3D Browser with and without Virtual Reality for Gamified Crowdsourcing of COVID-19 Data Analysis”
Researcher/s: Mircea Teodorescu, Sri Kurniawan | UC Santa Cruz

This project proposes the rapid development of a web-based platform that enables crowdsourced analysis of COVID-19 data through browser-based 3D rendering with and without virtual reality. Specifically, we aim to establish an open-source platform that 1) enables experts to post challenges that COVID-19 data analysis can solve, 2) enables crowdsourced non-expert annotation and data manipulation beyond visual transformation, 3) leverage the immersive capabilities of 3D rendering and virtual reality, 4) allows for cross-platform capabilities between different mediums of extended reality for user collaboration, and 5) utilizes elements of gamification to help stimulate rich data analysis and engagement. (Awarded May 15, 2020.)

PPE/Equipment


“AmbuBox: Fast-deployable Low-cost Ventilator for COVID-19 Emergent Care”
Researcher/s: Tingrui Pan, Andrew Li | UC Davis

The objective of the proposed research is to develop a low-cost clinically viable ventilator, named as AmbuBox, utilizing a controllable pneumatic enclosure and standard manual resuscitators that are readily available (AmbuBag), which can be rapidly deployed during respiratory pandemic situations. AmbuBox aims to address the existing challenges presented in existing low-cost ventilator designs by offering an easy-to-install and simple-to-operate apparatus while maintaining a long lifespan with high-precision flow control. As an outcome of the proposed research, a mass-producible prototype of the AmbuBox will be created and validated in a clinical setting in 3-6 months. Upon successful development, the team will make the prototype designs open access, while working with local industry to enable short-term manufacturability. The overall cost of parts to assemble the AmbuBox would be less than $100.

“At-Home COVID-19 Detection on Face Mask”
Researcher/s: Liwei Lin, Shuvo Roy | UC Berkeley, UCSF

Over 50 percent of COVID-19 patients have delayed or no symptoms while they are spreading the virus. Both healthy and symptomless people could easily conduct self-tests at home if there are at-home detection kits. We propose to collect and detect exhaled breath condensate (EBC) on the face mask which has become mandatory personal protection equipment (PPE) around the world to combat COVID-19. Nanostructured face masks can collect and accumulate virus samples with high concentrations without the time/equipment-intense amplification step in RT-PCR or RT-LAMP. Specifically, nanofibers can provide more than 100 times surface areas than those in conventional masks. By wearing the mask for 6 hours, a person may accumulate 5,400 times more virus than an in-throat swab specimen. Together, 540,000 times higher virus concentration could be achieved, which is equivalent to 19 cycles in a typical PCR amplification process – the same level as current state-of-art detection schemes for COVID-19 virus.

“Developing a 3D-printed Protective Cage for Decontamination of N95 Masks”
Researcher/s: Phillip Messersmith | UC Berkeley

During COVID-19, shortcomings in the supply chain have led to severe shortages of N95 respirator masks worldwide, forcing health practitioners to reuse face masks. Ultraviolet light and hydrogen peroxide have been suggested for mask decontamination, however neither cleans masks, making them unusable after only a few cycles. We will pursue an overlooked approach: liquid carbon dioxide. Liquid CO2 both cleans and sterilizes and does not appear to harm mask performance. Importantly, equipment for liquid CO2 processing is in place throughout the world in “eco-friendly” dry cleaners. What is needed is a way to protect masks from mechanical damage during cleaning. In this project we will use 3D-print technology to develop, optimize and print protective cages, and confirm their performance in benchtop and field tests. This technology is easily scalable (>10,000 masks/day/site) and will be a new tool in management of COVID-19, and future pandemics.

“Developing a mobile, low-cost, scalable, variable output ozone generator for different sanitization applications”
Researcher/s: Reza Ehsani | UC Merced

The World Health Organization has declared the novel coronavirus (COVID-19) outbreak a global pandemic. The best method of managing the pandemic is to decrease the rate of infected patients, requiring extreme measures in sanitization. Current sanitization practices that use chemical sanitizers or UV light have limitations. Ozone is a colorless gas that can be used as a sanitizing agent and has several advantages; however, the commercially available ozone generators are not designed for sanitizing applications. In this project, we will design and fabricate a new, portable, low-cost ozone production system. It will be compact, mobile, scalable, and user-friendly. Unlike conventional ozone generators, this new system will allow the user to adjust the concentration of ozone output, providing more flexibility so that it can be used for sanitizing small or larger indoor areas. It can also be used in agriculture and food production systems. The proposed system can be built in three months and can be mass produced in a small shop using off-the-shelf materials.

“RespiraWorks open-source ventilator”
Researcher/s: Julia Schaletzky | UC Berkeley

Due to the rapid spread of COVID-19, a global ventilator shortage has accelerated the need for low-cost ventilators domestically and in developing countries. We have designed a ventilator that targets addressing market failures in providing full-featured ventilators capable of monitored care for up to two weeks. Our design cannot be used in corner-case patients, but instead we have focused on radically reducing cost, increasing supply chain and simplifying manufacturing and assembly. Unlike most open-source designs, we include the capability for adaptive controls and a full user interface for patient monitoring. We are leveraging the current focus of a global pool of world-class engineers to develop the design in an open-source model to be provided for use during the pandemic or for future hospital use.

“Low-cost, Flexible Oxygen Saturation and Temperature Sensors for COVID-19 Patient Home Monitoring”
Researcher/s: Rikky Muller, Ana Arias | UC Berkeley

COVID-19 patients frequently suffer from hypoxia, a low saturation of oxygen in the blood caused by shallow or impaired breathing. A hypoxic state can occur rapidly and without the patient’s knowledge, therefore early detection can be life-saving, since there is a critical window between the onset of hypoxia and the need for ventilation. COVID-19 patients may be monitored at home with pulse oximeters. These large and bulky devices are not suitable for chronic use or wear. They are used intermittently at the discretion of the patient, potentially missing the critical window of disease progression. This project proposes the development of disposable body-worn patches that chronically measure blood oxygen and heart rate. The patches use organic printed electronic sensors that are fabricated with low-cost manufacturing techniques. A wireless interface allows direct streaming of all data to a mobile device where blood oxygen saturation levels may be tracked and warnings issued.

“Vine Robot for Automated Nasopharyngeal Swabbing”
Researcher/s: Gabriel Elkaim, Lin Zhang | UC Santa Cruz, UC Davis Health

Nasopharyngeal swabbing (NPS) is currently the preferred choice for sampling recommended by the CDC. In essence, NPS is a method for collecting clinical test samples of nasal secretions from the back of the nose and throat by inserting a 6” long swab through the nose and spinning it in place. Patients report discomfort from the NPS test, and the test potentially exposes healthcare workers to viral infection. Our proposal consists of two coupled approaches. The first is to develop a flocked polyester nasopharyngeal swab that is both narrower diameter (1mm vs 2mm) and more flexible, to reduce patient discomfort. The main thrust will be to automate the NPS using a “vine” robot that extends via eversion. The swab tip is carried by the vine, which is soft extended via pressure (air or water). The vine robot allows the test to be carried out without endangering healthcare workers.  (Awarded May 15, 2020.)

Testing


“The UCSC SARS-CoV-2 Genome Browser”
Researcher/s: Maximilian Haeussler, Jim Kent | UC Santa Cruz

Cross-referenced and easily accessible molecular-level data of many types is essential for research, but traditional government genetic databases like NCBI, EBI/Uniprot, and NIAID’s ImmPort are fragmented and relatively slow to update. This project will accelerate successful Covid-19 research by integrating all genetic information from existing resources into the UCSC Genome Browser. The SARS-CoV-2 Genome Browser will convert, cross-reference, and make searchable molecular-level data of all types as it appears in databases and data supplements of publications, allowing users to check these against new mutations in the virus as they appear.

“Applying transformative technology to create a diagnostic testing facility from a research lab”
Researcher/s: David Haussler, Olena Vaske | UC Santa Cruz

UC Santa Cruz, a research university without a medical center, an existing sequencing center, or a molecular diagnostic lab, has taken on the challenge to transform its cell culture facilities, qPCR machines, and liquid handling equipment into a diagnostic lab, with an accompanying surveillance project to assist the Santa Cruz community to assess its exposure to SARS-CoV-2. We will use transformative technology, supported by expertise from our researchers, software engineers and data analysts, to open a CLIA diagnostic testing facility on May 1, 2020, to benefit symptomatic community members and essential workers. This has been an extraordinary effort into new regulatory space, and represents a sea change in how we operate, including a rapid progression to automation, clinical data management, and data security for large amounts of personal data to ensure HIPAA compliance.

“COVID-19 Detection using Nanotechnology Based Devices”
Researcher/s: Waqas Khalid | UC Berkeley

Even though there are current COVID testing solutions, they all require samples to be processed at a clinical-testing-site. Such tests confirm COVID-positive cases in individuals who displayed symptoms. There is a dire need for a portable, point-of-care rapid testing device, such as the one proposed here, to aid in continuous testing of masses. The proposed devices are the size of credit-cards and can be used at homes or sites of large influx of individuals like airports, schools, corporations etc., providing immediate results with high sensitivity, reliability minimum reagents, limiting further spread of COVID-19.

“Delivering safer air in healthcare facilities treating COVID-19 patients”
Researcher/s: Hayden Taylor | UC Berkeley

Effective hospital air filtration is crucial for reducing the exposure of healthcare workers and patients to COVID-19. Yet this important factor has been widely neglected during the current outbreak. Many patients are being treated in improvised isolation facilities in which air quality standards are not met, threatening unnecessary infection of critical workers and vulnerable patients. UC Berkeley’s COVID-19 Design and Manufacturing Group is developing a rapidly manufacturable, easily deployable, and affordable air treatment system that can reduce the number of virus particles inhaled by critical personnel. This project will have several phases: 1) rapidly implementing a lean, plug-in design using high air circulation rates and filtration to reduce particle concentrations; 2) adding a humidification function to reduce the aerosolization of virus particles; 3) developing a product for the wider community (e.g., restaurants, gyms) as restrictions lift; 4) exploring integration of emerging solid-state immunosensing devices for real-time COVID-19 detection.

“Ionizing air to trap COVID-19 virus to prevent airborne transmission”
Researcher/s: Saif Islam | UC Davis

Several studies recently found that viruses can remain suspended in air for hours and can move as much as 27 feet through breath, sneezes, and coughs. These viruses can easily be transported through ventilation and HVAC systems contaminating larger spaces, unless they are trapped where they originate. We propose to build an air ionizer and purifier to trap viruses and inhibit their transmission in different parts of large buildings such as hospitals, manufacturing plants or offices. Our goal is to use one-dimensional semiconductor nanostructures to address the biggest challenge of ionizing devices – dangerously high voltage of operation that enables harmful ozone gas as a byproduct. In addition, a low-voltage, miniaturized and ozone-free air purifier with safe operational voltage can be very effective in enhancing the capabilities of masks such as N95 masks. The device enables unique possibilities for rapid and simple trapping and removal of viruses from the air and offers possibilities to detect them.

“At home personalized monitoring of exhaled breath inflammatory biomarkers for known or suspected COVID-19 patients”
Researcher/s: Cristina Davis, Nicholas Kenyon, Michael Schivo | UC Davis

This proposal addresses two major issues regarding the current COVID-19 pandemic. 1) Most individuals who contract COVID-19 exhibit mild symptoms that do not require hospitalization. However, some, especially the elderly or those with underlying conditions (diabetes, cardiovascular disease) and even the young and healthy, have had their condition suddenly and drastically deteriorate without warning, requiring emergency responses such as intubation and ICU care. There is currently no method to track an individual’s health outside of a hospital and predict if they might require clinical intervention. 2) A lack of testing has limited our knowledge of the spread of COVID-19 in late February/early March 2020 and persists in early April. Our research team has developed a rapid, non-invasive diagnostic platform for pulmonary viral infections through metabolite analysis of a person’s exhaled breath. Our test is self-administered and subjects who are sent home to self-quarantine can collect their exhaled breath samples.

“Development of sensor platforms for rapid COVID-19 antibody detection”
Researcher/s: Wei-Chun Chin, Changqing Li, Jennifer Lu | UC Merced

The most critical question our nation (or almost the whole world) faces now is when we can return to work and resume our normal activities after stay-at-home orders and social distancing. COVID-19 antibody tests have been identified to have the potential to play a role in this complex “reopening” evaluation. Currently there are limited rapid COVID-19 antibody tests available. In this seed project, we aim to build COVID-19 antibody sensor platforms that are based on graphene and carbon nanotubes with very fast response time (within seconds) at very low cost. The proposed sensor is easily scaled up for mass production and has direct electrical read-outs without complex solution rinsing like in conventional immunological testing kits. We are developing business connections to launch our own start-up. The results from this seed grant will expedite our collaborations with potential business partners to further develop and commercialize our COVID-19 antibody testing platform.

“Droplet transport controlling airborne disease transmission”
Researcher/s: Simo Makiharju | UC Berkeley

We will study the transport of human-expelled droplets, which is critical to the transmission of COVID-19 and, as shown in narrative, considered to be poorly understood by the scientific community. The present 2m social distancing guidance is in fact not adequate for many situations, especially in confined spaces such as lecture halls and laboratories. CITRIS support is requested to obtain initial data needed for a joint UC-LBNL proposal. LBNL collaborators have already received their initial funding, and with support of CITRIS, the UC team will conduct proof-of-concept experiments enabling submission of the joint proposal to DOE. The end goal of the larger project is to understand and control the transmission within and between rooms, including the effects of building ventilation in distributing aerosols throughout buildings. The collaboration will produce data that can inform decisions about how to safely operate businesses, schools, offices, thus enabling people to return to common spaces.

“Online visualization and annotation of SARS-CoV-2 protein domains”
Researcher/s: Ian Holmes | UC Berkeley

We have prototyped a dynamic web application that integrates protein phylogenies, alignments and structures in an interactive browsing experience. We propose to develop this into an interactive protein wiki that will allow results from the scientific literature to be annotated, bookmarked, and shared, with potential benefits ranging from discussion of viral origins to development of drugs and vaccines. The code runs as an embeddable open source component and, for the first time, makes algorithms from statistical phylogenetics available from within the web browser. The current dataset in the demo instance of this web application uses the 40 SARS-CoV-2 domains in the Pfam database of protein families. As part of this work we will introduce automated updating, integrate ABrowse with the JBrowse genome browser (so users can see the structural impact of amino acid-level variation), and perform scale engineering so that users can visualize millions of proteins at once.

“Integrated Quantitative Microbial Risk Assessment and Geospatial Analysis of SARS-CoV-2 in Wastewater for Vulnerable Populations”
Researcher/s: Colleen Naughton, Maureen Kinyua | UC Merced, UC Davis

The COVID-19 pandemic has fundamentally changed our daily lives and how we assess risk from person-to-person contact and contaminated surfaces. But what about our wastewater? Much of the SARS-CoV-2 related research has focused on aerosols and contaminated surfaces with less risk quantification related to the water and sanitation sector. SARS-CoV-2 has been detected in wastewater across the globe and previous coronaviruses have been found infective in wastewater. Thus, our research will utilize information technology to quantify the associated risk of SARS-CoV-2 infection for wastewater treatment operators and neighboring communities. We will integrate Quantitative Microbial Risk Assessment (QMRA) and geospatial analysis to create a vulnerability map of 38 wastewater treatment plants in the Bay Area. The methods developed in and results from this research will have local and global implications to inform and protect vulnerable populations. Our project utilizes information technology to serve society, aligning with the CITRIS mission and health, sustainable infrastructures, and policy lab interest areas.

“Detection of Active SARS-CoV-2 Infections in Crude Biofluids”
Researcher/s: Markita Landry | UC Berkeley

The coronavirus pandemic has disrupted life globally. CoV-2 has rapidly infected millions of individuals and resulted in quarantine and shelter-in-place orders to prevent spread of the potentially deadly virus. To date, it remains unknown 1) what proportion of individuals have been infected with CoV-2; 2) whether prior CoV-2 infection provides immunity, and 3) if so, for how long; and 4) what proportion of the population is made up of asymptomatic carriers of the virus. Given recent estimates of ~60 percent asymptomatic individuals in certain age groups, and in the absence of a vaccine or quantitative epidemiological answers to these questions, a return to societal “normalcy” will depend on our ability to rapidly, broadly, and repeatedly test individuals for active – not only prior – CoV-2 infection. We propose to develop a rapid, reversible, and portable device to detect active CoV-2 infected individuals, with a supply chain orthogonal to qPCR-detection of infected individuals. (Awarded May 15, 2020.)

“An Ultra-Sensitive Method to Determine Viral Load of COVID-19 Patients for Patient Stratification and Care”
Researcher/s: Lydia Lee Sohn | UC Berkeley

Limitations in the sensitivity of current COVID-19 diagnostic methods prevent the accurate measurement of SARS-CoV-2 viral load, leading to inability to predict patient outcomes, determine appropriate treatment, or stratify patients for drug trials. We propose an innovative, highly sensitive method to quantify SARS-CoV-2 viral load in COVID-19 patient saliva. Our method involves tagging viral particles with DNA oligonucleotides, isolating the particles using magnetic beads coated with ACE2 (which binds to the virus’ Spike protein), and performing qPCR to quantify the oligo tags, which are proportional to the number of viral particles. We have used this method to detect tumor-derived extracellular vesicles (tdEVs) in saliva for cancer screening. Our tdEV preliminary data suggest that our method would have unprecedented sensitivity for SARS-CoV-2 quantitation. Our success would have a high impact: more informed treatment decisions and triaging would lead to better patient outcomes, shorter hospital stays, and more efficient use of scarce resources.  (Awarded May 15, 2020.)

“Validating the use of Propidium Monoazide (PMA) qRT-PCR to detect viability of SARS-CoV-2 without the need for BSL3 tissue culture”
Researcher/s: Jonathan Eisen | UC Davis

Resources are being deployed to understand and combat the COVID-19 pandemic at an unprecedented scale. One current gap in knowledge that greatly limits development of mitigation strategies relates to how long the SARS-CoV-2 virus is viable in the environment. Most environmental sampling to date has used qRT-PCR to detect and quantify viral RNA in samples (e.g., swabs, air filters). Though valuable, one major limitation of this approach is that viral RNA can persist long after a virion has lost viability. Although viability assays are possible using cell cultures, such work requires limited BSL3 facilities. Here we propose to develop and test a Propidium Monoazide (PMA) based assay to detect viable SARS-CoV-2 virus in environmental samples. Critically, this work can take place under BSL2 conditions, dramatically increasing the number of labs able to do this work and preserving BSL3 resources for clinical and other work. (Awarded May 15, 2020.)

For more about CITRIS Seed Funding, see the program webpage.

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The Center for Information Technology Research in the Interest of Society (CITRIS) and the Banatao Institute drive interdisciplinary innovation for social good with faculty, researchers, and students from four University of California campuses – Berkeley, Davis, Merced, and Santa Cruz – along with public and private partners.

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