  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.
