Statistical inference for stochastic epidemic models with three levels of mixing

TL;DR

This paper develops a stochastic epidemic model with three levels of mixing—household, secondary group, and community—and investigates how different data types influence the accuracy of infection rate parameter estimation, highlighting the importance of data granularity.

Contribution

It introduces a multi-level mixing epidemic model and analyzes the impact of various data types on parameter inference, emphasizing the benefits of temporal data and heterogeneity considerations.

Findings

Temporal data significantly improves inference accuracy.

Heterogeneity affects non-household transmission estimates.

Three-level mixing models yield different inferences than two-level models.

Abstract

A stochastic epidemic model is defined in which each individual belongs to a household, a secondary grouping (typically school or workplace) and also the community as a whole. Moreover, infectious contacts take place in these three settings according to potentially different rates. For this model we consider how different kinds of data can be used to estimate the infection rate parameters with a view to understanding what can and cannot be inferred, and with what precision. Among other things we find that temporal data can be of considerable inferential benefit compared to final size data, that the degree of heterogeneity in the data can have a considerable effect on inference for non-household transmission, and that inferences can be materially different from those obtained from a model with two levels of mixing. Keywords: Basic reproduction number, Bayesian inference, Epidemic model, Infectious disease data, Markov chain Monte Carlo, Networks.