Optimizing Interventions to Control Emerging Infectious Diseases

Abstract

Inherent uncertainties surrounding emerging and re-emerging infectious diseases pose unique challenges to control efforts. Concurrent with the acceleration of disease emergence in recent decades, advances in modern computational methods have enabled new approaches to optimize interventions for disease control. In this dissertation, we leverage mathematical modeling and novel data sources to reveal new approaches to designing and evaluating strategies for controlling cholera, Ebola, and several other communicable diseases. In Chapter 1, we use a mathematical model to demonstrate the impact, and interaction, of key factors which cause vaccine-derived herd immunity to wane over time. We demonstrate that oral cholera vaccines can be powerful tools for quickly protecting a population for a period of time that depends critically on vaccine coverage, vaccine efficacy over time, and the rate of population turnover through human mobility. We use these findings to show that pre-emptive vaccination may best be targeted at intermediate-mobility settings and through a strategy that blends routine vaccination with mass campaigns. In Chapter 2, we develop a quantitative framework for comparing the control performance of quarantine and symptom monitoring of contacts with suspected exposure to an infectious disease. We use a mathematical model of seven case study diseases to show how intervention choice is influenced by the natural history of the infectious disease, its inherent transmissibility, and the intervention feasibility in the particular healthcare setting. We use this information to identify the most important characteristics of the disease and setting that need to be characterized for an emerging pathogen in order to make an informed decision between quarantine and symptom monitoring of individuals. In Chapter 3, we broaden the previous chapter’s discussion on quarantine by focusing on quarantines and other travel restrictions applied to large groups. Historically, the impact of such interventions on human mobility have proven difficult to measure, but we demonstrate that new approaches using mobile phone data can provide the necessary information in near real-time. We use mobile phone data from Sierra Leone to reveal that travel restrictions had an enormous and easily measurable impact on human travel during the 2014-5 Ebola epidemic.