Bias and bias-correction for individual-level models of infectious disease

Abstract

Accurate infectious disease models can help scientists understand how an ongoing disease epidemic spreads and forecast the course of epidemics more effectively. Considering various factors that affect the spread of a disease (e.g. geographical, social, domestic, and genetic), a class of individual-level models (ILMs) was developed to incorporate population heterogeneity. In these models, inferences are developed within a Bayesian Markov chain Monte Carlo (MCMC) framework, obtaining posterior estimates of model parameters. The issues of bias of parameter estimates, and methods for bias correction, have been widely studied with respect to many of the most established and commonly used statistical models and associated methods of parameter estimation. However, these methods are not directly applicable to infectious disease data. This paper investigates circumstances in which ILM parameter estimates may be biased in some simple disease system scenarios. Further, we aim to compare the performance of bias-corrected estimates of ILM parameters, using simulation, with the posterior estimates of the parameter. We also discuss the factors that affect the performance of these estimators.