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《MedRxiv，4月10日，Real-time detection of COVID-19 epicenters within the United States using a network of smart thermometers》

  • 来源专题：COVID-19科研动态监测
  • 编译者： xuwenwhlib
  • 发布时间：2020-04-11

  • Real-time detection of COVID-19 epicenters within the United States using a network of smart thermometers

    Samuel D Chamberlain, Inder Singh, Carlos A Ariza, Amy L Daitch, Patrick B Philips, Benjamin D Dalziel

    doi: https://doi.org/10.1101/2020.04.06.20039909

    Abstract

    Containing outbreaks of infectious disease requires rapid identification of transmission hotspots, as the COVID-19 pandemic demonstrates. Focusing limited public health resources on transmission hotspots can contain spread, thus reducing morbidity and mortality, but rapid data on community-level disease dynamics is often unavailable. Here, we demonstrate an approach to identify anomalously elevated levels of influenza-like illness (ILI) in real-time, at the scale of US counties. Leveraging data from a geospatial network of thermometers encompassing more than one million users across the US, we identify anomalies by generating accurate, county-specific forecasts of seasonal ILI from a point prior to a potential outbreak and comparing real-time data to these expectations. Anomalies are strongly correlated with COVID-19 case counts and may provide an early-warning system to locate outbreak epicenters.

    *注，本文为预印本论文手稿，是未经同行评审的初步报告，其观点仅供科研同行交流，并不是结论性内容，请使用者谨慎使用.

  • 原文来源：https://www.medrxiv.org/content/10.1101/2020.04.06.20039909v1
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