Predictive accuracy of a hierarchical logistic model of cumulative SARS-CoV-2 case growth until May 2020

BACKGROUND: Infectious disease predictions models, including virtually all epidemiological models describing the spread of the SARS-CoV-2 pandemic, are rarely evaluated empirically. The aim of the present study was to investigate the predictive accuracy of a prognostic model for forecasting the development of the cumulative number of reported SARS-CoV-2 cases in countries and administrative regions worldwide until the end of May 2020.

METHODS: The cumulative number of reported SARS-CoV-2 cases was forecasted in 251 regions with a horizon of two weeks, one month, and two months using a hierarchical logistic model at the end of March 2020. Forecasts were compared to actual observations by using a series of evaluation metrics.

RESULTS: On average, predictive accuracy was very high in nearly all regions at the two weeks forecast, high in most regions at the one month forecast, and notable in the majority of the regions at the two months forecast. Higher accuracy was associated with the availability of more data for estimation and with a more pronounced cumulative case growth from the first case to the date of estimation. In some strongly affected regions, cumulative case counts were considerably underestimated.

CONCLUSIONS: With keeping its limitations in mind, the investigated model may be used for the preparation and distribution of resources during the initial phase of epidemics. Future research should primarily address the model's assumptions and its scope of applicability. In addition, establishing a relationship with known mechanisms and traditional epidemiological models of disease transmission would be desirable.