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10.2196/20285

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suck abstract from ncbi


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pmid32730217      J+Med+Internet+Res 2020 ; 22 (8): e20285
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  • Real-Time Forecasting of the COVID-19 Outbreak in Chinese Provinces: Machine Learning Approach Using Novel Digital Data and Estimates From Mechanistic Models #MMPMID32730217
  • Liu D; Clemente L; Poirier C; Ding X; Chinazzi M; Davis J; Vespignani A; Santillana M
  • J Med Internet Res 2020[Aug]; 22 (8): e20285 PMID32730217show ga
  • BACKGROUND: The inherent difficulty of identifying and monitoring emerging outbreaks caused by novel pathogens can lead to their rapid spread; and if left unchecked, they may become major public health threats to the planet. The ongoing coronavirus disease (COVID-19) outbreak, which has infected over 2,300,000 individuals and caused over 150,000 deaths, is an example of one of these catastrophic events. OBJECTIVE: We present a timely and novel methodology that combines disease estimates from mechanistic models and digital traces, via interpretable machine learning methodologies, to reliably forecast COVID-19 activity in Chinese provinces in real time. METHODS: Our method uses the following as inputs: (a) official health reports, (b) COVID-19-related internet search activity, (c) news media activity, and (d) daily forecasts of COVID-19 activity from a metapopulation mechanistic model. Our machine learning methodology uses a clustering technique that enables the exploitation of geospatial synchronicities of COVID-19 activity across Chinese provinces and a data augmentation technique to deal with the small number of historical disease observations characteristic of emerging outbreaks. RESULTS: Our model is able to produce stable and accurate forecasts 2 days ahead of the current time and outperforms a collection of baseline models in 27 out of 32 Chinese provinces. CONCLUSIONS: Our methodology could be easily extended to other geographies currently affected by COVID-19 to aid decision makers with monitoring and possibly prevention.
  • |*Data Analysis[MESH]
  • |*Machine Learning[MESH]
  • |*Models, Biological[MESH]
  • |COVID-19[MESH]
  • |China/epidemiology[MESH]
  • |Coronavirus Infections/*epidemiology/*transmission[MESH]
  • |Disease Outbreaks[MESH]
  • |Forecasting/*methods[MESH]
  • |Humans[MESH]
  • |Internet[MESH]
  • |Mass Media[MESH]
  • |Models, Statistical[MESH]
  • |Pandemics[MESH]
  • |Pneumonia, Viral/*epidemiology/*transmission[MESH]


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