tetano
Editor, Senior Moderator
Proc Natl Acad Sci U S A. 2018 Feb 26. pii: 201708856. doi: 10.1073/pnas.1708856115. [Epub ahead of print]
[h=1]Forecasting the spatial transmission of influenza in the United States.[/h] Pei S[SUP]1[/SUP], Kandula S[SUP]2[/SUP], Yang W[SUP]2[/SUP], Shaman J[SUP]1[/SUP].
[h=3]Author information[/h]
[h=3]Abstract[/h] Recurrent outbreaks of seasonal and pandemic influenza create a need for forecasts of the geographic spread of this pathogen. Although it is well established that the spatial progression of infection is largely attributable to human mobility, difficulty obtaining real-time information on human movement has limited its incorporation into existing infectious disease forecasting techniques. In this study, we develop and validate an ensemble forecast system for predicting the spatiotemporal spread of influenza that uses readily accessible human mobility data and a metapopulation model. In retrospective state-level forecasts for 35 US states, the system accurately predicts local influenza outbreak onset,-i.e., spatial spread, defined as the week that local incidence increases above a baseline threshold-up to 6 wk in advance of this event. In addition, the metapopulation prediction system forecasts influenza outbreak onset, peak timing, and peak intensity more accurately than isolated location-specific forecasts. The proposed framework could be applied to emergent respiratory viruses and, with appropriate modifications, other infectious diseases.
[h=4]KEYWORDS:[/h] data assimilation; human mobility; influenza forecast; metapopulation model; spatial transmission
PMID: 29483256 DOI: 10.1073/pnas.1708856115
[h=1]Forecasting the spatial transmission of influenza in the United States.[/h] Pei S[SUP]1[/SUP], Kandula S[SUP]2[/SUP], Yang W[SUP]2[/SUP], Shaman J[SUP]1[/SUP].
[h=3]Author information[/h]
[h=3]Abstract[/h] Recurrent outbreaks of seasonal and pandemic influenza create a need for forecasts of the geographic spread of this pathogen. Although it is well established that the spatial progression of infection is largely attributable to human mobility, difficulty obtaining real-time information on human movement has limited its incorporation into existing infectious disease forecasting techniques. In this study, we develop and validate an ensemble forecast system for predicting the spatiotemporal spread of influenza that uses readily accessible human mobility data and a metapopulation model. In retrospective state-level forecasts for 35 US states, the system accurately predicts local influenza outbreak onset,-i.e., spatial spread, defined as the week that local incidence increases above a baseline threshold-up to 6 wk in advance of this event. In addition, the metapopulation prediction system forecasts influenza outbreak onset, peak timing, and peak intensity more accurately than isolated location-specific forecasts. The proposed framework could be applied to emergent respiratory viruses and, with appropriate modifications, other infectious diseases.
[h=4]KEYWORDS:[/h] data assimilation; human mobility; influenza forecast; metapopulation model; spatial transmission
PMID: 29483256 DOI: 10.1073/pnas.1708856115