Predictive Models for Forecasting Public Health Scenarios : Practical Experiences Applied during the First Wave of the COVID-19 Pandemic
Martin-Moreno, Jose M. (Universitat de València. Departament de Medicina Preventiva i Salut Pública, Ciències de l'Alimentació, Toxicologia i Medicina Legal)
Alegre-Martinez, Antoni (Universidad CEU Cardenal Herrera)
Martin-Gorgojo, Victor (Hospital Clínic Universitari (València))
Alfonso-Sanchez, Jose Luis (Universitat de València. Departament de Medicina Preventiva i Salut Pública, Ciències de l'Alimentació, Toxicologia i Medicina Legal)
Torres, Ferran (Universitat Autònoma de Barcelona. Departament de Pediatria, Obstetrícia i Ginecologia i de Medicina Preventiva i Salut Pública)
Pallares-Carratala, Vicente (Universitat Jaume I. Departament de Medicina)

Date:	2022
Description:	16 pàg.
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.
Rights:	Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, la comunicació pública de l'obra i la creació d'obres derivades, fins i tot amb finalitats comercials, sempre i quan es reconegui l'autoria de l'obra original.
Language:	Anglès
Document:	Article de revisió ; recerca ; Versió publicada
Subject:	COVID-19 ; Explanatory models ; Forecasting ; Health policy ; Predictive models ; Public health ; SARS-CoV-2 ; COVID-19/epidemiology ; Pandemics ; Humans ; SIR MODEL ; SPREAD ; SDG 3 - Good Health and Well-being
Published in:	International journal of environmental research and public health, Vol. 19 Núm. 9 (May 2022) , p. 5546, ISSN 1660-4601

DOI: 10.3390/ijerph19095546
PMID: 35564940

16 p, 1.2 MB

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