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

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. A well-established approach to the statistical analysis of such surveillance data are endemic-epidemic time-series models. The temporal dependence inherent to communicable diseases is thereby taken into account by an observation-driven formulation conditioning on past counts. Additional spatial dynamics in areal-level counts are largely driven by human travel and can be captured by power-law weights based on the order of adjacency. However, social contacts are highly assortative also with respect to age. For example, characteristic pathways of directly transmitted pathogens are linked to childcare facilities, schools and nursing homes. We therefore investigate how a spatio-temporal endemic-epidemic model can be extended to take social contact data into account. The approach is illustrated in a case study on norovirus gastroenteritis in Berlin, 2011-2014, by age group, city district and week, using additional contact data from the POLYMOD survey. 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.