Temporal and probabilistic forecasts of epidemic interventions
Conference Year
2023
Abstract
In this work, time-dependent probability generating functions (PGFs) model a stochastic branching process of disease spread over a network of contacts where public health interventions are introduced over time. We define a general transmissibility equation to account for varying contact patterns and percentage of the population immunized. The resulting framework showcases a temporal and probabilistic analysis of an intervention's impact on disease spread, which match continuous-time stochastic simulations. To aid decision makers, we define several metrics over which these forecasts can be compared. Our work provides a more detailed short-term forecast of disease spread and comparison of intervention strategies.
Primary Faculty Mentor Name
Laurent Hébert-Dufresne
Status
Graduate
Student College
College of Engineering and Mathematical Sciences
Program/Major
Mathematics
Primary Research Category
Engineering and Math Science
Temporal and probabilistic forecasts of epidemic interventions
In this work, time-dependent probability generating functions (PGFs) model a stochastic branching process of disease spread over a network of contacts where public health interventions are introduced over time. We define a general transmissibility equation to account for varying contact patterns and percentage of the population immunized. The resulting framework showcases a temporal and probabilistic analysis of an intervention's impact on disease spread, which match continuous-time stochastic simulations. To aid decision makers, we define several metrics over which these forecasts can be compared. Our work provides a more detailed short-term forecast of disease spread and comparison of intervention strategies.