Reconstructing contact network structure and cross-immunity patterns from multiple infection histories

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 similarity metrics between hosts based on their multiple infection histories. We find that, depending on infection multiplicity and network sampling, multiple infection summary statistics can recover network properties such as degree distribution. Furthermore, we show that by mining simulation outputs 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 analysis of multiple infection histories opens promising perspectives to infer and validate transmission networks and immunological interference for infectious diseases from longitudinal cohort data.
Author summary Infectious disease dynamics are constrained both by between-host contacts and pathogen interactions within a host. The circulation of multiple parasites within a population constitutes a unique signature for each host’s infection lifespan. We hypothesise that such individual multiple infection histories can inform us about the host contact networks on which parasites can be transmitted, but also on within-host interactions, where prior infections shape susceptibility to new infections. For proof-of-concept, we develop 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 between-pathogen 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 interference information from longitudinal infection data.