Bayesian epidemiological modeling over high-resolution network data

TL;DR

This paper develops a Bayesian framework for epidemiological modeling using high-resolution network data, addressing challenges in parameter estimation with scarce surveillance data and demonstrating effectiveness with synthetic and real pathogen data.

Contribution

It introduces a Bayesian methodology tailored for network-driven epidemiological models, incorporating a hierarchy of experiments to handle data scarcity and identifiability issues.

Findings

Bayesian approach performs well on synthetic tests.

Accurate modeling of E. coli O157 in Swedish cattle.

Effective assessment of disease detection and intervention scenarios.

Abstract

Mathematical epidemiological models have a broad use, including both qualitative and quantitative applications. With the increasing availability of data, large-scale quantitative disease spread models can nowadays be formulated. Such models have a great potential, e.g., in risk assessments in public health. Their main challenge is model parameterization given surveillance data, a problem which often limits their practical usage. We offer a solution to this problem by developing a Bayesian methodology suitable to epidemiological models driven by network data. The greatest difficulty in obtaining a concentrated parameter posterior is the quality of surveillance data; disease measurements are often scarce and carry little information about the parameters. The often overlooked problem of the model's identifiability therefore needs to be addressed, and we do so using a hierarchy of increasingly realistic known truth experiments. Our proposed Bayesian approach performs convincingly across all our synthetic tests. From pathogen measurements of shiga toxin-producing Escherichia coli O157 in Swedish cattle, we are able to produce an accurate statistical model of first-principles confronted with data. Within this model we explore the potential of a Bayesian public health framework by assessing the efficiency of disease detection and -intervention scenarios.