Predictive Models for Forecasting Public Health Scenarios: Practical Experiences Applied during the First Wave of the COVID-19 Pandemic
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Predictive Models for Forecasting Public Health Scenarios: Practical Experiences Applied during the First Wave of the COVID-19 PandemicAutoría
Fecha de publicación
2022-05-03Editor
MDPIISSN
1660-4601Cita bibliográfica
Martin-Moreno, J.M.; Alegre-Martinez, A.; Martin-Gorgojo, V.; Alfonso-Sanchez, J.L.; Torres, F.; Pallares-Carratala, V. Predictive Models for Forecasting Public Health Scenarios: Practical Experiences Applied during the First Wave of the COVID-19 Pandemic. Int. J. Environ. Res. Public Health 2022, 19, 5546. https://doi.org/10.3390/ijerph19095546Tipo de documento
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Resumen
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 ... [+]
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. [-]
Publicado en
International Journal of Environmental Research and Public Health, Vol. 19, Issue 9 (May-1 2022)Derechos de acceso
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