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COVID-19 mortality dynamics: The future modelled as a (mixture of) past(s).

Samuel SoubeyrandMélina RibaudVirgile BaudrotDenis AllardDenys PommeretLionel Roques
Published in: PloS one (2020)
Discrepancies in population structures, decision making, health systems and numerous other factors result in various COVID-19-mortality dynamics at country scale, and make the forecast of deaths in a country under focus challenging. However, mortality dynamics of countries that are ahead of time implicitly include these factors and can be used as real-life competing predicting models. We precisely propose such a data-driven approach implemented in a publicly available web app timely providing mortality curves comparisons and real-time short-term forecasts for about 100 countries. Here, the approach is applied to compare the mortality trajectories of second-line and front-line European countries facing the COVID-19 epidemic wave. Using data up to mid-April, we show that the second-line countries generally followed relatively mild mortality curves rather than fast and severe ones. Thus, the continuation, after mid-April, of the COVID-19 wave across Europe was likely to be mitigated and not as strong as it was in most of the front-line countries first impacted by the wave (this prediction is corroborated by posterior data).
Keyphrases
  • coronavirus disease
  • cardiovascular events
  • sars cov
  • risk factors
  • decision making
  • cardiovascular disease
  • electronic health record
  • type diabetes
  • mass spectrometry
  • machine learning
  • deep learning
  • data analysis