An Interaction-Based Approach to Computational Epidemiology

Abstract

Epidemiology is the study of patterns of health in a population and the factors that contribute to these patterns. Computational Epidemiology is the development and use of computer models to understand the spatio-temporal diffusion of disease through populations. An important factor that greatly influences an outbreak of an infectious disease is the structure of the interaction network across which it spreads. Aggregate or collective computational epidemiology models that have been studied in the literature for over a century, often assume that a population is partitioned into a few subpopulations (e.g. by age) with a regular interaction structure within and between subpopulations. Although useful for obtaining analytical expressions for a number of interesting parameters such as the numbers of sick, infected and recovered individuals in a population, it does not capture the complexity of human interactions that serves as a mechanism for disease transmission. In other words, the aggregate approach does not take the structure of underlying social network into account. Additionally, the number of different subpopulation types considered is small and parameters such as mixing rate and reproductive number are either unknown or hard to observe. Here we describe Simdemics: an interaction-based multiagent approach to support epidemic planning for large urban regions. Simdemics is an example of a disaggregated modeling approach in which interactions between every pair of individuals is represented. It is based on the idea that a better understanding of the characteristics of the social contact network can give better insights into disease dynamics and intervention strategies for epidemic planning. Simdemics details the demographic and geographic distributions of disease and provides decision makers with information about (1) the consequences of a biological attack or natural outbreak, (2) the resulting demand for health services, and (3) the feasibility and effectiveness of response options. A unique feature of Simdemics is the size and scale of urban regions that can be analyzed using it.