| dc.description.abstract | Recent outbreaks of Ebola, Zika, and COVID-19, among others, have shown how infectious diseases can decimate economies and destroy lives. Infectious disease models are important tools for preparing for, preventing, and responding to such epidemics. Here, we use infectious disease modeling to analyze past outbreaks, prepare for future outbreaks, and respond to ongoing outbreaks, with the goal of informing public health response.
We first analyze past Ebola and cholera outbreaks and build a simulation model to understand the role the incubation period, the time between exposure and symptom onset, has on epidemic trajectory. We find that diseases with longer incubation periods, such as Ebola, where infected individuals can travel further before becoming infectious, result in more long-distance sparking events and less predictable disease trajectories, as compared to the more predictable wave-like spread of diseases with shorter incubation periods, such as cholera. Second, we assess if augmenting classical randomized controlled trials of vaccines with pathogen sequence and contact tracing data can permit these trials to estimate vaccine efficacy against infectiousness, or the reduction in onward transmission from a vaccinated person who is infected compared to an unvaccinated infected person. Through simulations of a transmission model and a vaccine trial, we find that these data sources enhance identifiability of this key measure of vaccine efficacy. Finally, we simulate studies of SARS-CoV-2 seroprotection. We find that in studies assessing whether seropositivity confers protection against future infection, time varying epidemic dynamics can cause confounding; it is therefore necessary to adjust for geographic location and time of enrollment in order to reduce bias. These methods and findings demonstrate how infectious disease modeling can be used to enhance epidemic preparedness and response. | |
