Statistical estimation and decision-making for the COVID-19 pandemic

Abstract

This dissertation aims to provide policymakers and health practitioners with statistical tools and actionable information by which to make informed decisions, with a particular focus on the response to infectious disease outbreaks. In the first project, we quantify how many Americans contracted COVID-19 in the first year of the pandemic. We formulate a Bayesian epidemiological model utilizing multiple sources of information, including random sample testing surveys, to debias clinical COVID data and estimate SARS-CoV-2 prevalence and transmission rates in the United States through March 2021. We quantify the extent to which reported COVID cases underestimated true infection counts, which was large (with about 2 in 3 infections missed by testing), especially in the first months. Building on this work, in the second project we determine how to optimally respond to pandemics using non-pharmaceutical interventions (NPIs), which include social distancing measures, school and workplace closure, and testing, tracing, and masking policies. We first estimate the effects of NPIs on SARS-CoV-2 transmission in the US. Coupling these results with estimates of the costs associated to infections and NPIs derived from the public health and economics literature, we evaluate the cost-effectiveness of NPI policies in the year prior to the arrival of COVID vaccines and antiviral treatments. Going further, we frame the problem of policy design in terms of statistical decision theory, with which we derive optimal NPI strategies. We find that pandemic school closures were not cost-effective, but other measures were. In the third project, we propose a new method for the comparison of proportions—a foundational and ubiquitous statistical inference task relevant, in particular, to the analysis of randomized controlled trials with a binary outcome. Framing the problem as one of causal inference, we demonstrate how the likelihood can be cast in terms of clinically meaningful quantities, which facilitates interpretation, sensitivity analysis, and prior specification, and addresses the deficits of existing approaches. We demonstrate the utility of our method in empirical examples including a reanalysis of the Pfizer-BioNTech COVID-19 vaccine trial, which proved safe and highly efficacious in preventing SARS-CoV-2 infection.