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Ensemble Algorithms to Improve COVID-19 Growth Curve Estimates

Author

Listed:
  • Raydonal Ospina

    (Statistics Department, LInCa, Federal University of Bahia, Salvador 40170-110, Brazil
    Statistics Department, CASTLab, Federal University of Pernambuco, Recife 50670-901, Brazil)

  • Jaciele Oliveira

    (Statistics Department, CASTLab, Federal University of Pernambuco, Recife 50670-901, Brazil)

  • Cristiano Ferraz

    (Statistics Department, CASTLab, Federal University of Pernambuco, Recife 50670-901, Brazil)

  • André Leite

    (Statistics Department, CASTLab, Federal University of Pernambuco, Recife 50670-901, Brazil)

  • João Gondim

    (Mathematics Department, Federal University of Pernambuco, Recife 50670-901, Brazil)

Abstract

In January 2020, the world was taken by surprise as a novel disease, COVID-19, emerged, attributed to the new SARS-CoV-2 virus. Initial cases were reported in China, and the virus rapidly disseminated globally, leading the World Health Organization (WHO) to declare it a pandemic on 11 March 2020. Given the novelty of this pathogen, limited information was available regarding its infection rate and symptoms. Consequently, the necessity of employing mathematical models to enable researchers to describe the progression of the epidemic and make accurate forecasts became evident. This study focuses on the analysis of several dynamic growth models, including the logistics, Gompertz, and Richards growth models, which are commonly employed to depict the spread of infectious diseases. These models are integrated to harness their predictive capabilities, utilizing an ensemble modeling approach. The resulting ensemble algorithm was trained using COVID-19 data from the Brazilian state of Paraíba. The proposed ensemble model approach effectively reduced forecasting errors, showcasing itself as a promising methodology for estimating COVID-19 growth curves, improving data forecasting accuracy, and providing rapid responses in the early stages of the pandemic.

Suggested Citation

  • Raydonal Ospina & Jaciele Oliveira & Cristiano Ferraz & André Leite & João Gondim, 2023. "Ensemble Algorithms to Improve COVID-19 Growth Curve Estimates," Stats, MDPI, vol. 6(4), pages 1-18, September.
    • Handle: RePEc:gam:jstats:v:6:y:2023:i:4:p:62-1007:d:1250771
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    References listed on IDEAS

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