Abstract
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.
Original language | English (US) |
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Article number | giab009 |
Journal | GigaScience |
Volume | 10 |
Issue number | 2 |
DOIs | |
State | Published - Feb 1 2021 |
Keywords
- COVID-19
- SARS-CoV-2
- stochastic SIR model
- stochastic growth model
- time-series cross-validation
ASJC Scopus subject areas
- Health Informatics
- Computer Science Applications