Modeling mitigation strategies for infectious disease pandemics in US cities

The novel coronavirus SARS-CoV-2 emerged in 2019. It spread rapidly around the globe and was declared a pandemic by the World Health Organization on March 11, 2020. To mitigate the unprecedented threat of COVID-19, public health agencies, researchers and pharmaceutical companies launched massive efforts to develop effective vaccines and medical treatments. To buy time and save lives, policymakers issued social distancing, mass gathering and face mask orders while ramping up programs for SARS-CoV-2 testing, contact tracing and isolation. In this dissertation, I developed data-driven mathematical models to evaluate the impacts of different intervention strategies on SARS-CoV-2 disease dynamics at a city-level. Chapter 1 provides a brief introduction and the historical context for the four studies, each of which modeled transmission dynamics and mitigation strategies at different stages of the pandemic. In Chapter 2, I estimated the impacts of various school closure and social distancing measures on local healthcare demands. This analysis supported pandemic decision making in Austin, Texas, including the local shelter-in-place order enacted on March 24, 2020. In Chapter 3, I modeled the impacts of relaxing social distancing orders following the initial stay-home orders across the US. For this study, I calibrated the transmission rate in my model based on hospitalization data from the city of Austin and estimated the impacts of cocooning (i.e., sheltering) high-risk individuals as measures relaxed. In Chapter 4, I examined the potential impacts of testing, contact tracing, and case isolation on disease transmission. Using data from a test-trace program jointly administered by Austin Public Health and the University of Texas, I evaluated the relative importance of testing and tracing speed and capacity and found that available resources are insufficient to slow transmission, particularly during times when the virus is spreading rapidly. In Chapter 5, I developed a model to compare SARS-CoV-2 vaccination strategies, that incorporates published efficacy estimates for the Moderna and Pfizer-BioNTech vaccines that received Emergency Use Approval in the US in late 2020. I evaluated the impacts of the timing of the vaccine rollout, the prioritization of vaccines to risk groups and age groups, rates of uptake, and single-dose versus two-dose strategies. Chapter 6 discusses the public health impacts, limitations, and lessons learned from the studies presented in Chapters 2-5. The research presented in this dissertation elucidated SARS-CoV-2 transmission dynamics and effective mitigation strategies at multiple stages of the COVID-19 pandemic and supported decision making by Austin, Texas and the US Centers for Disease Control and Prevention.Cellular and Molecular Biolog