Hot Topic: Mathematics of Disease – The Science of Epidemic Modeling

The COVID-19 pandemic has put an intense spotlight on the little-known field of epidemiological modeling. Computer models, built with thousands of formulas, have helped scientists understand key aspects of the pandemic and provided forecasts of its future path. Policy makers have based important decisions on models, including whether to lock down entire countries, order people to wear face masks, how to deploy COVID-19 tests, or who to vaccinate first.

Computer models have existed long before the current pandemic. They have been used to forecast many outbreaks of almost every disease, including HIV, malaria, and Ebola, and have inspired and informed policy makers to take action. In every instance, scientists must deal with a range of uncertainties, from basic properties of the pathogen, routes of transmission, the strength of immunity, and human behavior. And models are never perfect. In the case of COVID-19, some have contradicted each other, and some have been spectacularly wrong. At times, they have left the public confused.

In this Hot Topic, two epidemiological modelers will explain how they construct their models, what they are designed to do, and what their limitations are. We'll also discuss with them and two 'consumers' of models what makes epidemic modeling useful, how epidemiologists, policy makers, and the public at large deal with the uncertainties, and how best to communicate about the outcomes of modeling exercises.

Martin Enserink – Science magazine

Amrish Baidjoe – Médecins Sans Frontières (MSF-OCB), LuxOR Operational Research Unit
Julia Fitzner – World Health Organization (WHO)
Sebastian Funk – London School of Hygiene & Tropical Medicine Sheetal Silal – Modelling and Simulation Hub, Africa (MASHA), University of Cape Town