Forecasting influenza in Europe using a metapopulation model incorporating cross-border commuting and air travel

Sarah C Kramer, Sen Pei and Jeffrey Shaman

PLOS Computational Biology, 2020, vol. 16, issue 10, 1-21

Abstract: Past work has shown that models incorporating human travel can improve the quality of influenza forecasts. Here, we develop and validate a metapopulation model of twelve European countries, in which international translocation of virus is driven by observed commuting and air travel flows, and use this model to generate influenza forecasts in conjunction with incidence data from the World Health Organization. We find that, although the metapopulation model fits the data well, it offers no improvement over isolated models in forecast quality. We discuss several potential reasons for these results. In particular, we note the need for data that are more comparable from country to country, and offer suggestions as to how surveillance systems might be improved to achieve this goal.Author summary: In our increasingly connected world, infectious diseases are more capable than ever of rapid spread over large geographical distances. Previous research has shown that human travel can be used to better forecast the transmission of influenza, which may in turn help public health and medical practitioners to prepare for outbreaks with increasing lead time. Here, we developed a model of twelve European countries, in which countries are connected based on rates of commuting and air travel between them. We then used this model, along with publicly available influenza incidence data, to forecast future incidence in Europe. We found that forecasts produced with the network model were not more accurate than those produced for individual countries in isolation. We emphasize the need for aligned influenza data collection practices that are comparable between different countries and which will likely improve forecast accuracy.

Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pcbi00:1008233

DOI: 10.1371/journal.pcbi.1008233

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