Problem
Research questions and friction points this paper is trying to address.
contact matrices
infectious disease modelling
connectivity matrices
heterogeneity
epidemiology
Constructing Contact and Connectivity Matrices for Infectious Disease Modelling
🤖 AI Summary
Accurately capturing the dynamic heterogeneity of contact patterns among populations is crucial for enhancing the predictive accuracy and intervention efficacy of infectious disease models. This work systematically reviews methodologies for constructing contact and connectivity matrices driven by multi-source data—including surveys, mobile communication records, and wearable devices—with a focus on integrating heterogeneity and uncertainty across demographic, spatial, and temporal dimensions. By critically examining current technical limitations, the study outlines future research directions centered on dynamic modeling, refined representation of heterogeneity, and rigorous quantification of uncertainty. These insights aim to provide both theoretical grounding and methodological guidance for developing more accurate and operationally useful models of infectious disease transmission.
📝 Abstract
Contact (or mixing, or more generally connectivity) matrices are a fundamental component of modelling and inference for infectious disease epidemiology. Their structure and parametrisation directly accounts for the frequency of interactions between different subpopulations of individuals, as well as having the potential to encode dynamic heterogeneity in these interactions across demographic axes, space and time. Considerable research has been devoted to the structure and estimation of (components of) these matrices to help inform outbreak control and forecast disease spread. In this paper, we review the existing literature on the data types used to construct contact matrices and the methods for incorporating uncertainties and heterogeneities into them. We also highlight remaining challenges and future directions in the use of these contact matrices for epidemiological research.
Innovation
Methods, ideas, or system contributions that make the work stand out.
contact matrices
infectious disease modelling
heterogeneity
uncertainty quantification
mixing patterns
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