Quantifying Uncertainty in Mechanistic Models of Infectious Disease.

This primer describes the statistical uncertainty in mechanistic models and provides R code to quantify it. We begin with an overview of mechanistic models for infectious disease, and then describe the sources of statistical uncertainty in the context of a case study on severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). We describe the statistical uncertainty as belonging to 3 categories: data uncertainty, stochastic uncertainty, and structural uncertainty. We demonstrate how to account for each of these via statistical uncertainty measures and sensitivity analyses broadly, as well as in a specific case study on estimating the basic reproductive number, ${R}_0$, for SARS-CoV-2.

The Expert Next Door: Interactions With Friends and Family During the COVID-19 Pandemic.

The coronavirus disease 2019 (COVID-19) pandemic thrust the field of public health into the spotlight. For many epidemiologists, biostatisticians, and other public health professionals, this caused the professional aspects of our lives to collide with the personal, as friends and family reached out with concerns and questions. Learning how to navigate this space was new for many of us and required refining our communication style depending on context, setting, and audience. Some of us took to social media, utilizing our existing personal accounts to share information after sorting through and summarizing the rapidly emerging literature to keep loved ones safe. However, those in our lives sometimes asked unanswerable questions, or began distancing themselves when we suggested more stringent guidance than they had hoped for, causing additional stress during an already traumatic time. We often had to remind ourselves that we were also individuals experiencing this pandemic and that our time-intensive efforts were meaningful, relevant, and impactful. As this pandemic and other public health crises continue, we encourage members of our discipline to consider how we can best use shared lessons from this period and to recognize that our professional knowledge, when used in our personal lives, can promote, protect, and bolster confidence in public health.

Pathogenesis, Symptomatology, and Transmission of SARS-CoV-2 through Analysis of Viral Genomics and Structure.

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. IMPORTANCE COVID-19 involves a number of organ systems and can present with a wide range of symptoms. From how the virus infects cells to how it spreads between people, the available research suggests that these patterns are very similar to those seen in the closely related viruses SARS-CoV-1 and possibly Middle East respiratory syndrome-related CoV (MERS-CoV). Understanding the pathogenesis of the SARS-CoV-2 virus also contextualizes how the different biological systems affected by COVID-19 connect. Exploring the structure, phylogeny, and pathogenesis of the virus therefore helps to guide interpretation of the broader impacts of the virus on the human body and on human populations. For this reason, an in-depth exploration of viral mechanisms is critical to a robust understanding of SARS-CoV-2 and, potentially, future emergent human CoVs (HCoVs).

Evaluating sources of bias in observational studies of angiotensin-converting enzyme inhibitor/angiotensin II receptor blocker use during COVID-19: beyond confounding.

Concerns over ACE inhibitor or ARB use to treat hypertension during COVID-19 remain unresolved. Although studies using more robust methodologies provided some clarity, sources of bias persist and it remains critical to quickly address this question. In this review, we discuss pernicious sources of bias using a causal model framework, including time-varying confounder, collider, information, and time-dependent bias, in the context of recently published studies. We discuss causal inference methodologies that can address these issues, including causal diagrams, time-to-event analyses, sensitivity analyses, and marginal structural modeling. We discuss effect modification and we propose a role for causal mediation analysis to estimate indirect effects via mediating factors, especially components of the renin--angiotensin system. Thorough knowledge of these sources of bias and the appropriate methodologies to address them is crucial when evaluating observational studies to inform patient management decisions regarding whether ACE inhibitors or ARBs are associated with greater risk from COVID-19.

Maximizing and evaluating the impact of test-trace-isolate programs: A modeling study.

BACKGROUND: Test-trace-isolate programs are an essential part of coronavirus disease 2019 (COVID-19) control that offer a more targeted approach than many other nonpharmaceutical 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 completeness in case detection and contact tracing and speed of isolation and 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 in the proportion of cases 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. Results from this model also indicate 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 the impact of test-trace-isolate are sensitive to assumptions about COVID-19 natural history and adherence to isolation and quarantine, 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.

Concerns About the Special Article on Hydroxychloroquine and Azithromycin in High-Risk Outpatients With COVID-19.

In May 2020, the Journal published an opinion piece by a member of the Editorial Board, in which the author reviewed several papers and argued that using hydroxychloroquine (HCQ) + azithromycin (AZ) early to treat symptomatic coronavirus disease 2019 (COVID-19) cases in high-risk patients should be broadly applied. As members of the Journal's Editorial Board, we are strongly supportive of open debate in science, which is essential even on highly contentious issues. However, we must also be thorough in our examination of the facts and open to changing our minds when new information arises. In this commentary, we document several important errors in the manuscript, review the literature presented, and demonstrate why it is not of sufficient quality to support scale up of HCQ + AZ, and then discuss the literature that has been generated since the publication, which also does not support use of this therapy. Unfortunately, the current scientific evidence does not support HCQ + AZ as an effective treatment for COVID-19, if it ever did, and even suggests many risks. Continuing to push the view that it is an essential treatment in the face of this evidence is irresponsible and harmful to the many people already suffering from infection.