Background
Forecasting of COVID-19 cases daily and weekly has been one of the challenges posed to governments and the health sector globally. To facilitate informed public health decisions, the concerned parties rely on short-term daily projections generated via predictive modeling. We calibrate stochastic variants of growth models and the standard susceptible-infectious-removed model into 1 Bayesian framework to evaluate and compare their short-term forecasts.
Results
We implement rolling-origin cross-validation to compare the short-term forecasting performance of the stochastic epidemiological models and an autoregressive moving average model across 20 countries that had the most confirmed COVID-19 cases as of August 22, 2020.
Conclusion
None of the models proved to be a gold standard across all regions, while all outperformed the autoregressive moving average model in terms of the accuracy of forecast and interpretability.
Forecasting of COVID-19 daily confirmed cases has been one of the several challenges posed on the governments and health sectors on a global scale. To facilitate informed public health decisions, the concerned parties rely on short-term daily projections generated via predictive modeling. We calibrate stochastic variants of growth models and the standard SIR model into one Bayesian framework to evaluate their short-term forecasts. In summary, it was noted that none of the models proved to be golden standards across all the regions in their entirety, while all outperformed ARIMA in a predictive capacity as well as in terms of interpretability.
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