Evaluating short-term forecasting of COVID-19 cases among different epidemiological models under a Bayesian framework

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.

Automated summary

The study compared the performance of stochastic epidemiological models and autoregressive moving average models in short-term forecasting of COVID-19 cases in 20 countries globally. It was found that no model emerged as a gold standard across all regions, but all models outperformed the autoregressive moving average model in terms of forecast accuracy and interpretability.