A random forest model for forecasting regional COVID-19 cases utilizing reproduction number estimates and demographic data
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DOI: 10.1016/j.chaos.2021.111779
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- Zeroual, Abdelhafid & Harrou, Fouzi & Dairi, Abdelkader & Sun, Ying, 2020. "Deep learning methods for forecasting COVID-19 time-Series data: A Comparative study," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).
- Shelby R. Buckman & Reuven Glick & Kevin J. Lansing & Nicolas Petrosky-Nadeau & Lily Seitelman, 2020. "Replicating and Projecting the Path of COVID-19 with a Model-Implied Reproduction Number," Working Paper Series 2020-24, Federal Reserve Bank of San Francisco.
- Ribeiro, Matheus Henrique Dal Molin & da Silva, Ramon Gomes & Mariani, Viviana Cocco & Coelho, Leandro dos Santos, 2020. "Short-term forecasting COVID-19 cumulative confirmed cases: Perspectives for Brazil," Chaos, Solitons & Fractals, Elsevier, vol. 135(C).
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Keywords
COVID-19; Random forest; Compartmental model; Mobility; US county;All these keywords.
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