Abstract
AbstractThis article reviews some early investigations and research studies in the first weeks of the coronavirus disease 2019 (COVID-19) pandemic from a statistician's perspective. These investigations were based on very small datasets but were momentous in the initial global reactions to the pandemic. The article discusses the initial evidence of high infectiousness of COVID-19 and why that conclusion was not reached faster than in reality. Further reanalyses of some published COVID-19 studies show that the epidemic growth was dramatically underestimated by compartmental models, and the lack of fit could have been clearly identified by simple data visualization. Finally, some lessons for statisticians are discussed.
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