The COVID-19 pandemic has led to a surge of interest in mechanistic modeling and simulation of infectious diseases to forecast outbreak evolution. These mathematical models are usually based on a classical compartmental paradigm, which describes the temporal dynamics of disease spread over a certain region of interest whose population is distributed in different compartments according to disease status (e.g., susceptible, exposed, infected, recovered, deceased). These models and other recent model-naïve, purely data-driven statistical approaches have been useful in monitoring and controlling COVID-19 outbreaks.
To contribute to the global effort in understanding and predicting the spread of COVID-19, this research has four main objectives:
(1) defining mathematical models that specifically account for the temporal mechanisms of COVID-19 infection based on classical compartmental formulations,
(2) extending these models to include key spatial mechanisms (e.g., clustering towards highly-densed areas, mobility of individuals),
(3) constructing computational methods to efficiently and accurately calibrate and solve these mathematical models using longitudinal epidemiological data,
(4) investigating the use of simulation results to inform the decision-making of public health interventions (e.g., design levels of restriction, timing and magnitude of lockdowns, regional allocation of medical resources).