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Predictive Models for Forecasting Public Health Scenarios: Practical Experiences Applied during the First Wave of the COVID-19 Pandemic

Author

Listed:
  • Jose M. Martin-Moreno

    (Department of Preventive Medicine and Public Health, Universitat de Valencia, 46010 Valencia, Spain
    Biomedical Research Institute INCLIVA, Clinic University Hospital, 46010 Valencia, Spain)

  • Antoni Alegre-Martinez

    (Biomedical Sciences Department, Faculty of Health Sciences, Cardenal Herrera CEU University, 46115 Valencia, Spain)

  • Victor Martin-Gorgojo

    (Biomedical Research Institute INCLIVA, Clinic University Hospital, 46010 Valencia, Spain
    Orthopedic Surgery and Traumatology Department, Clinic University Hospital, 46010 Valencia, Spain)

  • Jose Luis Alfonso-Sanchez

    (Department of Preventive Medicine and Public Health, Universitat de Valencia, 46010 Valencia, Spain
    Preventive Medicine Service, General Hospital, 46014 Valencia, Spain)

  • Ferran Torres

    (Biostatistics Unit, Medical School, Universitat Autonoma de Barcelona, 08193 Barcelona, Spain)

  • Vicente Pallares-Carratala

    (Health Surveillance Unit, Castellon Mutual Insurance Union, 12004 Castellon, Spain
    Department of Medicine, Jaume I University, 12071 Castellon, Spain)

Abstract

Background: Forecasting the behavior of epidemic outbreaks is vital in public health. This makes it possible to anticipate the planning and organization of the health system, as well as possible restrictive or preventive measures. During the COVID-19 pandemic, this need for prediction has been crucial. This paper attempts to characterize the alternative models that were applied in the first wave of this pandemic context, trying to shed light that could help to understand them for future practical applications. Methods: A systematic literature search was performed in standardized bibliographic repertoires, using keywords and Boolean operators to refine the findings, and selecting articles according to the main PRISMA 2020 statement recommendations. Results: After identifying models used throughout the first wave of this pandemic (between March and June 2020), we begin by examining standard data-driven epidemiological models, including studies applying models such as SIR (Susceptible-Infected-Recovered), SQUIDER, SEIR, time-dependent SIR, and other alternatives. For data-driven methods, we identify experiences using autoregressive integrated moving average (ARIMA), evolutionary genetic programming machine learning, short-term memory (LSTM), and global epidemic and mobility models. Conclusions: The COVID-19 pandemic has led to intensive and evolving use of alternative infectious disease prediction models. At this point it is not easy to decide which prediction method is the best in a generic way. Moreover, although models such as the LSTM emerge as remarkably versatile and useful, the practical applicability of the alternatives depends on the specific context of the underlying variable and on the information of the target to be prioritized. In addition, the robustness of the assessment is conditioned by heterogeneity in the quality of information sources and differences in the characteristics of disease control interventions. Further comprehensive comparison of the performance of models in comparable situations, assessing their predictive validity, is needed. This will help determine the most reliable and practical methods for application in future outbreaks and eventual pandemics.

Suggested Citation

  • Jose M. Martin-Moreno & Antoni Alegre-Martinez & Victor Martin-Gorgojo & Jose Luis Alfonso-Sanchez & Ferran Torres & Vicente Pallares-Carratala, 2022. "Predictive Models for Forecasting Public Health Scenarios: Practical Experiences Applied during the First Wave of the COVID-19 Pandemic," IJERPH, MDPI, vol. 19(9), pages 1-16, May.
  • Handle: RePEc:gam:jijerp:v:19:y:2022:i:9:p:5546-:d:807724
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    References listed on IDEAS

    as
    1. Cooper, Ian & Mondal, Argha & Antonopoulos, Chris G., 2020. "A SIR model assumption for the spread of COVID-19 in different communities," Chaos, Solitons & Fractals, Elsevier, vol. 139(C).
    2. Daron Acemoglu & Victor Chernozhukov & Iván Werning & Michael D. Whinston, 2021. "Optimal Targeted Lockdowns in a Multigroup SIR Model," American Economic Review: Insights, American Economic Association, vol. 3(4), pages 487-502, December.
    3. Fanelli, Duccio & Piazza, Francesco, 2020. "Analysis and forecast of COVID-19 spreading in China, Italy and France," Chaos, Solitons & Fractals, Elsevier, vol. 134(C).
    4. Chimmula, Vinay Kumar Reddy & Zhang, Lei, 2020. "Time series forecasting of COVID-19 transmission in Canada using LSTM networks," Chaos, Solitons & Fractals, Elsevier, vol. 135(C).
    5. Salgotra, Rohit & Gandomi, Mostafa & Gandomi, Amir H, 2020. "Time Series Analysis and Forecast of the COVID-19 Pandemic in India using Genetic Programming," Chaos, Solitons & Fractals, Elsevier, vol. 138(C).
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