Abstract
ObjectiveInfectious disease spread depends on contact rates between infectious and susceptible individuals. Transmission models are commonly informed using empirically collected contact data, but the relevance of different contact types to transmission is still not well understood. Some studies select contacts based on a single characteristic such as proximity (physical/non-physical), location, duration or frequency. This study aimed to explore whether clusters of contacts similar to each other across multiple characteristics could better explain disease transmission. MethodsIndividual contact data from the POLYMOD survey in Poland, Great Britain, Belgium, Finland and Italy were grouped into clusters by the k medoids clustering algorithm with a Manhattan distance metric to stratify contacts using all four characteristics. Contact clusters were then used to fit a transmission model to sero-epidemiological data for varicella-zoster virus (VZV) in each country. Results and discussionAcross the five countries, 9–15 clusters were found to optimise both quality of clustering (measured using average silhouette width) and quality of fit (measured using several information criteria). Of these, 2–3 clusters were most relevant to VZV transmission, characterised by (i) 1–2 clusters of age-assortative contacts in schools, (ii) a cluster of less age-assortative contacts in non-school settings. Quality of fit was similar to using contacts stratified by a single characteristic, providing validation that single stratifications are appropriate. However, using clustering to stratify contacts using multiple characteristics provided insight into the structures underlying infection transmission, particularly the role of age-assortative contacts, involving school age children, for VZV transmission between households.
Highlights
Mathematical models of infectious disease transmission require assumptions about mixing between different subgroups in a population that can potentially lead to transmission between infected and susceptible individuals
A model with age-stratified contact rates can be fitted to age-specific data on infection history to estimate the age-specific effective contact rates in the who acquires infection from whom” (WAIFW) matrix
Evaluating the optimal number of clusters involves three components: the quality of the clustering, how well the resulting clusters fit the transmission model of varicella-zoster virus (VZV) seroprevalence and the number of clusters that are relevant to this model fit (Fig. 1)
Summary
Mathematical models of infectious disease transmission require assumptions about mixing between different subgroups in a population that can potentially lead to transmission between infected and susceptible individuals. Many infection control interventions such as vaccinating children (Thorrington et al, 2015) or closing schools during a pandemic (House et al, 2011) are predicated on the assumption that certain subgroups in the population are the main transmitters. A more realistic assumption is to subdivide the population based on some characteristic, and introduce a matrix of contact rates capable of transmitting infection between each subgroup, called the “who acquires infection from whom” (WAIFW) matrix (Vynnycky and White, 2010). A model with age-stratified contact rates can be fitted to age-specific data on infection history (such as sero-epidemiological data, which marks the prevalence of previous infection) to estimate the age-specific effective contact rates in the WAIFW matrix
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