tetano
Editor, Senior Moderator
PLoS Comput Biol
. 2020 Oct 14;16(10):e1008233.
doi: 10.1371/journal.pcbi.1008233. Online ahead of print.
Forecasting influenza in Europe using a metapopulation model incorporating cross-border commuting and air travel
Sarah C Kramer[SUP] 1 [/SUP], Sen Pei[SUP] 1 [/SUP], Jeffrey Shaman[SUP] 1 [/SUP]
Affiliations
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.
. 2020 Oct 14;16(10):e1008233.
doi: 10.1371/journal.pcbi.1008233. Online ahead of print.
Forecasting influenza in Europe using a metapopulation model incorporating cross-border commuting and air travel
Sarah C Kramer[SUP] 1 [/SUP], Sen Pei[SUP] 1 [/SUP], Jeffrey Shaman[SUP] 1 [/SUP]
Affiliations
- PMID: 33052907
- DOI: 10.1371/journal.pcbi.1008233
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.