Supporting data for "Evaluating short-term forecasting of COVID-19 cases among different epidemiological models under a Bayesian framework.""

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 SIR model into one Bayesian framework to evaluate and compare their short term forecasts. 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. 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.

Keywords:

• covid-19
• sars-cov-2
• stochastic growth model
• stochastic sir model
• time-series cross-validation.

Read the peer-reviewed publication(s):

• Li, Q., Bedi, T., Lehmann, C. U., Xiao, G., & Xie, Y. (2021). Evaluating short-term forecasting of COVID-19 cases among different epidemiological models under a Bayesian framework. GigaScience, 10(2). https://doi.org/10.1093/gigascience/giab009 (PubMed:33604654)

Additional information:

https://scicrunch.org/resolver/RRID:SCR_019291

https://bio.tools/bayesepimodels_webapp

https://qiwei.shinyapps.io/PredictCOVID19/

https://scicrunch.org/resolver/RRID:SCR_019292

https://bio.tools/bayesepimodels

Github links:

https://github.com/liqiwei2000/BayesEpiModels