Using multiple infection history to infer contact network structure

The structure of host interactions within a population shapes the spread of infectious diseases but contact patterns between hosts are difficult to access. We hypothesised that key properties of these patterns can be inferred by using multiple infections data from individual longitudinal follow-ups. To show this, we simulated multiple infections on a contact network in an unbiased way by implementing a non-Markovian extension of the Gillespie algorithm for a community of parasites spreading on this network. We then analysed the resulting individual infection time series in an original way by introducing the concept of ‘infection barcodes’ to represent the infection history in each host. We find that, depending on infection multiplicity and immunity assumptions, knowledge about the barcode topology makes it possible to recover key properties of the network topology and even of individual nodes. The combination of individual-based simulations and barcode analysis of infection histories opens promising perspectives for the study of infectious disease transmission networks. Significance Statement The way hosts interact with each other is known to shape epidemics spread. However, these interactions are difficult to infer, especially in human populations. Using recent developments in stochastic epidemiological modeling and barcode theory, we show that the diversity of infections each host has undergone over time contains key information about contact network between hosts. This means that longitudinal follow-ups of some individuals in a population can tell us how hosts are in contact with each other. It can also inform us on how connected a particular individual is. This opens new possibilities regarding the use of genetic diversity of infectious diseases in epidemiology.

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