Incorporating social contact data in spatio-temporal models for infectious disease spread

TL;DR

This paper introduces a method to incorporate age-structured social contact data into spatio-temporal models for infectious disease spread, demonstrating improved accuracy in modeling norovirus transmission in Berlin.

Contribution

It presents a novel approach to integrate social contact matrices into endemic-epidemic models, enhancing disease spread predictions with age-specific contact patterns.

Findings

The age-structured model outperforms homogeneous mixing models.

A power transformation of contact data better captures within-group transmission.

Application to Berlin norovirus data demonstrates model effectiveness.

Abstract

Routine public health surveillance of notifiable infectious diseases gives rise to weekly counts of reported cases -- possibly stratified by region and/or age group. We investigate how an age-structured social contact matrix can be incorporated into a spatio-temporal endemic-epidemic model for infectious disease counts. To illustrate the approach, we analyze the spread of norovirus gastroenteritis over 6 age groups within the 12 districts of Berlin, 2011-2015, using contact data from the POLYMOD study. The proposed age-structured model outperforms alternative scenarios with homogeneous or no mixing between age groups. An extended contact model suggests a power transformation of the survey-based contact matrix towards more within-group transmission.