[DOI] 10.5524/100863

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

[Release Date] 2021-01-28

[Citation] Li, Q; Bedi, T; Lehmann, CU; Xiao, G; Xie, Y (2021): Supporting data
for "Evaluating short-term forecasting of COVID-19 cases among different
epidemiological models under a Bayesian framework."" GigaScience Database.
https://dx.doi.org/10.5524/100863

[Data Type] Software,Bioinformatics

[Data Summary] 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.

[File Location] https://s3.ap-northeast-1.wasabisys.com/gigadb-datasets/live/pub/10.5524/100001_101000/100863/

[File name] - [File Description]
readme_100863.txt  -  
BayesEpiModels-master.zip  -  Archival copy of the GitHub repository https://github.com/liqiwei2000/BayesEpiModels downloaded 15-Jan-2021. BayesEpiModels. This project is licensed under the GNU General Public License v3.0. Please refer to the GitHub repo for most recent updates.

[License]
All files and data are distributed under the CC0 1.0 Universal (CC0 1.0) Public
Domain Dedication (https://creativecommons.org/publicdomain/zero/1.0/), unless
specifically stated otherwise, see http://gigadb.org/site/term for more details.

[Comments]

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