Author: Li, Qiwei; Bedi, Tejasv; Lehmann, Christoph U; Xiao, Guanghua; Xie, Yang
Title: Evaluating short-term forecasting of COVID-19 cases among different epidemiological models under a Bayesian framework Cord-id: rpsoqihw Document date: 2021_2_19
ID: rpsoqihw
Snippet: 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-ori
Document: 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.
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