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?:abstract
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Accurate prediction of COVID-19 cases can optimize clinical trial recruitment, inform mitigation strategies and facilitate rapid medication development. Here we present a country-specific, modified Susceptible, Exposed, Infectious, Removed (SEIR) model of SARS-CoV-2 transmission using data from the Johns Hopkins University COVID-19 Dashboard. Inter-country differences in initial exposure, cultural/environmental factors, reporting requirements and stringency of mitigation strategies were incorporated. Asymptomatic patients and super-spreaders were also factored into our model. Using these data, our model estimated 65.8% of cases as asymptomatic; symptomatic and asymptomatic people were estimated to infect 2.12 and 5.83 other people, respectively. An estimated 9.55% of cases were super-spreaders with a 2.11-fold higher transmission rate than average. Our model estimated a mean maximum infection rate of 0.927 cases/day (inter-country range, 0.63-1.41) without mitigation strategies. Mitigation strategies with a stringency index value of [≥]60% were estimated to be required to reduce the reproduction ratio below 1. It was predicted that cases over the next 2 months would differ between countries, with certain countries likely to experience an accelerated accumulation of cases. Together, results from our model can guide distribution of diagnostic tests, impact clinical trial development, support medication development and distribution and inform mitigation strategies to reduce COVID-19 spread.
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