Using multiple infection history to infer contact network structure

Interactions within a population shape the spread of infectious diseases but contact patterns between individuals are difficult to access. We hypothesised that key properties of these patterns can be inferred from multiple infection data in longitudinal follow-ups. We developed a simulator for epidemics with multiple infections on networks and analysed the resulting individual infection time series by introducing the concept of infection barcodes. We find that, depending on infection multiplicity and network sampling, infection barcode summary statistics can recover network properties such as degree distribution. Furthermore, we show that by mining infection barcodes for multiple infection patterns, one can detect immunological interference between pathogens (i.e. the fact that past infections in a host condition future probability of infection). The combination of individual-based simulations and barcode analysis of infection histories opens promising perspectives to infer and validate transmission networks and immunological interference for infectious diseases from longitudinal cohort data. Author summaryInfectious disease dynamics are constrained both by between-host contacts and pathogen interactions within a host. Furthermore, multiple parasites circulate such that hosts are infected (sometimes simultaneously) by a variety of strains or species. We hypothesise that multiple infection history can inform us about the networks on which parasites are transmitted, but also on within-host interactions such as immunological interference. We developed a simulator for multiple infections on networks. By combining intuitive novel metrics for multiple infection events and established tools from computational data analysis, we show that similarity in infection history between two hosts correlates with their proximity in the contact network. By analysing pathogens co-occurrence patterns within hosts, we also recover immunological interference at the population level. The demonstrated robustness of our results in terms of observability, network clustering, and pathogen diversity opens new perspectives to extract host contact and between-pathogen immunity information from longitudinal cohort data.