Analysing Multiple Epidemic Data Sources

TL;DR

This paper discusses the challenges and methods of synthesizing multiple indirect data sources within a Bayesian framework to estimate infectious disease burden and inform public health decisions.

Contribution

It highlights the development and critique cycle of complex Bayesian models for epidemic data synthesis using influenza case studies.

Findings

Bayesian evidence synthesis effectively combines heterogeneous data sources.

Complex models require efficient computational inference methods.

Conflicting evidence poses significant challenges in model development.

Abstract

Evidence-based knowledge of infectious disease burden, including prevalence, incidence, severity and transmission, in different population strata and locations, and possibly in real time, is crucial to the planning and evaluation of public health policies. Direct observation of a disease process is rarely possible. However, latent characteristics of an epidemic and its evolution can often be inferred from the synthesis of indirect information from various routine data sources, as well as expert opinion. The simultaneous synthesis of multiple data sources, often conveniently carried out in a Bayesian framework, poses a number of statistical and computational challenges: the heterogeneity in type, relevance and granularity of the data, together with selection and informative observation biases, lead to complex probabilistic models that are difficult to build and fit, and challenging to criticize. Using motivating case studies of influenza, this chapter illustrates the cycle of model development and criticism in the context of Bayesian evidence synthesis, highlighting the challenges of complex model building, computationally efficient inference, and conflicting evidence.