This article reviews some early investigations and research studies in the first weeks of the coron- avirus 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 and why it did not lead to a faster conclu- sion that COVID-19 can be transmitted from human to human. 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 learned in retrospect are discussed.
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