Author: Dan Valeriu Nicolau; Alexander Hasson; Mona Bafadhel
Title: Recovery Ratios Reliably Anticipate COVID-19 Pandemic Progression Document date: 2020_4_14
ID: c6bc08kw_1
Snippet: The COVID-19 pandemic, at the time of writing, has infected at least 1.5millon people, and caused >100,000 deaths. The development of models to understand and predict the course of COVID-19 is imperative, in order to gain insight into the efficacy of public health measures aimed at containing its spread. Current models are either epidemiological 1,2 or based on reported infection data 3 . These both have limitations: epidemiological models are fo.....
Document: The COVID-19 pandemic, at the time of writing, has infected at least 1.5millon people, and caused >100,000 deaths. The development of models to understand and predict the course of COVID-19 is imperative, in order to gain insight into the efficacy of public health measures aimed at containing its spread. Current models are either epidemiological 1,2 or based on reported infection data 3 . These both have limitations: epidemiological models are forced to make assumptions about many unknowns thus varying wildly in their predictions, whilst reported data are retrospective and thus not predictive. It would be advantageous to have models that are directly data-driven and thus not forced to make assumptions, while retaining the predictive aspect of epidemiological models.
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