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COVID-19 mortality dynamics: The future modelled as a (mixture of) past(s)
Samuel Soubeyrand, Mélina Ribaud, Virgile Baudrot, Denis Allard, Denys Pommeret, Lionel Roques

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

Science & Mathematics Published 2020 Verification Pending

Overview & Synopsis

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).

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Document Information

Document Title COVID-19 mortality dynamics: The future modelled as a (mixture of) past(s)
Author / Organization Samuel Soubeyrand, Mélina Ribaud, Virgile Baudrot, Denis Allard, Denys Pommeret, Lionel Roques
Subject & Discipline Multidisciplinary Academic Open Research
Categories Science & Mathematics
Publication Year 2020
License / Rights Open Access / Creative Commons
Resource Verification Listed PDF (Verification Pending)
Original Host Website HAL Open Science (hal.science)
File Format application/pdf (Portable Document Format)
Language EN
Catalog ID DOC-102657
Original Source Link Visit Source Page on hal.science »