Prospective Prediction of Future SARS-CoV-2 Infections Using Empirical Data on a National Level to Gauge Response Effectiveness

Predicting an accurate expected number of future COVID-19 cases is essential to properly evaluate the effectiveness of any treatment or preventive measure. This study aimed to identify the most appropriate mathematical model to prospectively predict the expected number of cases without any intervention. The total number of cases for the COVID-19 epidemic in 28 countries was analyzed and fitted to several simple rate models including the logistic, Gompertz, quadratic, simple square, and simple exponential growth models. The resulting model parameters were used to extrapolate predictions for more recent data. While the Gompertz growth models (mean R2 = 0.998) best fitted the current data, uncertainties in the eventual case limit made future predictions with logistic models prone to errors. Of the other models, the quadratic rate model (mean R2 = 0.992) fitted the current data best for 25 (89 %) countries as determined by R2 values. The simple square and quadratic models accurately predicted the number of future total cases 37 and 36 days in advance respectively, compared to only 15 days for the simple exponential model. The simple exponential model significantly overpredicted the total number of future cases while the quadratic and simple square models did not. These results demonstrated that accurate future predictions of the case load in a given country can be made significantly in advance without the need for complicated models of population behavior and generate a reliable assessment of the efficacy of current prescriptive measures against disease spread.
Comment: 20 pages, 2 tables, 3 figures followed by 12 pages of supporting information