An Open-Publishing Response to the COVID-19 Infodemic (preprint)

The COVID-19 pandemic catalyzed the rapid dissemination of papers and preprints investigating the disease and its associated virus, SARS-CoV-2. The multifaceted nature of COVID-19 demands a multidisciplinary approach, but the urgency of the crisis combined with the need for social distancing measures present unique challenges to collaborative science. We applied a massive online open publishing approach to this problem using Manubot. Through GitHub, collaborators summarized and critiqued COVID-19 literature, creating a review manuscript. Manubot automatically compiled citation information for referenced preprints, journal publications, websites, and clinical trials. Continuous integration workflows retrieved up-to-date data from online sources nightly, regenerating some of the manuscript's figures and statistics. Manubot rendered the manuscript into PDF, HTML, LaTeX, and DOCX outputs, immediately updating the version available online upon the integration of new content. Through this effort, we organized over 50 scientists from a range of backgrounds who evaluated over 1,500 sources and developed seven literature reviews. While many efforts from the computational community have focused on mining COVID-19 literature, our project illustrates the power of open publishing to organize both technical and non-technical scientists to aggregate and disseminate information in response to an evolving crisis.

Pathogenesis, Symptomatology, and Transmission of SARS-CoV-2 through Analysis of Viral Genomics and Structure (preprint)

The novel coronavirus SARS-CoV-2, which emerged in late 2019, has since spread around the world and infected hundreds of millions of people with coronavirus disease 2019 (COVID-19). While this viral species was unknown prior to January 2020, its similarity to other coronaviruses that infect humans has allowed for rapid insight into the mechanisms that it uses to infect human hosts, as well as the ways in which the human immune system can respond. Here, we contextualize SARS-CoV-2 among other coronaviruses and identify what is known and what can be inferred about its behavior once inside a human host. Because the genomic content of coronaviruses, which specifies the virus's structure, is highly conserved, early genomic analysis provided a significant head start in predicting viral pathogenesis and in understanding potential differences among variants. The pathogenesis of the virus offers insights into symptomatology, transmission, and individual susceptibility. Additionally, prior research into interactions between the human immune system and coronaviruses has identified how these viruses can evade the immune system's protective mechanisms. We also explore systems-level research into the regulatory and proteomic effects of SARS-CoV-2 infection and the immune response. Understanding the structure and behavior of the virus serves to contextualize the many facets of the COVID-19 pandemic and can influence efforts to control the virus and treat the disease.

Testing out of quarantine (preprint)

Since SARS-CoV-2 emerged, a 14-day quarantine has been recommended based on COVID-19”s incubation period. Using an RT-PCR or rapid antigen test to “test out” of quarantine is a frequently proposed strategy to shorten duration without increasing risk. We calculated the probability that infected individuals test negative for SARS-CoV-2 on a particular day post-infection and remain symptom free for some period of time. We estimate that an infected individual has a 20.1% chance (95% CI 9.8-32.6) of testing RT-PCR negative on day five post-infection and remaining asymptomatic until day seven. We also show that the added information a test provides decreases as we move further from the test date, hence a less sensitive test that returns rapid results is often preferable to a more sensitive test with a delay.

Maximizing and evaluating the impact of test-trace-isolate programs (preprint)

Background: Test-trace-isolate programs are an essential part of COVID-19 control that offer a more targeted approach than many other non-pharmaceutical interventions. Effective use of such programs requires methods to estimate their current and anticipated impact. Methods and Findings: We present a mathematical modeling framework to evaluate the expected reductions in the reproductive number, R, from test-trace-isolate programs. This framework is implemented in a publicly available R package and an online application. We evaluated the effects of case detection, speed of isolation, contact tracing completeness and speed of quarantine using parameters consistent with COVID-19 transmission (R0=2.5, generation time 6.5 days). We show that R is most sensitive to changes to the proportion of infections detected in almost all scenarios, and other metrics have a reduced impact when case detection levels are low (<30%). Although test-trace-isolate programs can contribute substantially to reducing R, exceptional performance across all metrics is needed to bring R below one through test-trace-isolate alone, highlighting the need for comprehensive control strategies. Formally framing the dynamical process also indicates that metrics used to evaluate performance of test-trace-isolate, such as the proportion of identified infections among traced contacts, may be misleading. While estimates of program performance are sensitive to assumptions about COVID-19 natural history, our qualitative findings are robust across numerous sensitivity analyses. Conclusions: Effective test-trace-isolate programs first need to be strong in the "test" component, as case detection underlies all other program activities. Even moderately effective test-trace-isolate programs are an important tool for controlling the COVID-19 pandemic, and can alleviate the need for more restrictive social distancing measures.