05b8c912cddcaff869c1a7d03e18b559 ijerph_19_05546.pdf 847deedf6d76b203157043aadba28ae0606911eb ijerph_19_05546.pdf 54cfd3410dc21ecdc2bdd6e0ec7c79ea19cb31d747945b1f36435dda233077a5 ijerph_19_05546.pdf Title: Predictive Models for Forecasting Public Health Scenarios: Practical Experiences Applied during the First Wave of the COVID-19 Pandemic Subject: 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. Keywords: COVID-19; health policy; public health; explanatory models; forecasting; predictive models Author: Jose M. Martin-Moreno, Antoni Alegre-Martinez, Victor Martin-Gorgojo, Jose Luis Alfonso-Sanchez, Ferran Torres and Vicente Pallares-Carratala Creator: LaTeX with hyperref Producer: pdfTeX-1.40.21 CreationDate: Tue May 3 09:34:18 2022 CEST ModDate: Tue May 3 09:36:39 2022 CEST Tagged: no UserProperties: no Suspects: no Form: none JavaScript: no Pages: 16 Encrypted: no Page size: 595.276 x 841.89 pts (A4) Page rot: 0 File size: 1236817 bytes Optimized: no PDF version: 1.7 name type encoding emb sub uni object ID ------------------------------------ ----------------- ---------------- --- --- --- --------- HAMWOL+URWPalladioL-Bold Type 1 Custom yes yes no 10 0 BQVNHL+URWPalladioL-Roma Type 1 Custom yes yes no 15 0 RKAZWU+URWPalladioL-Ital Type 1 Custom yes yes no 19 0 LWQXII+TimesNewRomanPS-BoldItalicMT Type 1C WinAnsi yes yes no 37 0 IRWHZX+TimesNewRomanPSMT Type 1C WinAnsi yes yes no 40 0 BCDEEE+PalatinoLinotype-Bold TrueType WinAnsi yes yes no 70 0 BCDFEE+PalatinoLinotype-Roman TrueType WinAnsi yes yes no 74 0 BCDGEE+PalatinoLinotype-Italic TrueType WinAnsi yes yes no 78 0 BCDEEE+PalatinoLinotype-Bold TrueType WinAnsi yes yes no 92 0 BCDFEE+PalatinoLinotype-Roman TrueType WinAnsi yes yes no 96 0 BCDGEE+PalatinoLinotype-Italic TrueType WinAnsi yes yes no 100 0 IHGURL+EURM7 Type 1 Builtin yes yes no 112 0 MFGWFC+CMSY10 Type 1 Builtin yes yes no 116 0 CKBPVA+EURM10 Type 1 Builtin yes yes no 129 0 VVQTWY+VnURWPalladioL Type 1 Custom yes yes no 149 0 AWDAAW+VnURWPalladioL-Italic Type 1 Custom yes yes no 157 0 Jhove (Rel. 1.22.1, 2019-04-17) Date: 2023-03-23 02:38:54 CET RepresentationInformation: ijerph_19_05546.pdf ReportingModule: PDF-hul, Rel. 1.12.1 (2019-04-17) LastModified: 2023-02-22 01:34:23 CET Size: 1236817 Format: PDF Version: 1.7 Status: Well-Formed and valid SignatureMatches: PDF-hul MIMEtype: application/pdf PDFMetadata: Objects: 334 FreeObjects: 1 IncrementalUpdates: 0 DocumentCatalog: PageLayout: SinglePage PageMode: UseNone Outlines: Item: Title: Introduction Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@44c8afef Item: Title: Materials and Methods Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@7b69c6ba Item: Title: Results Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@46daef40 Children: Item: Title: Standard Epidemiological Models Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@12f41634 Children: Item: Title: SIR (Susceptible-Infected-Recovered) Model Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@13c27452 Item: Title: SQUIDER Model Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@262b2c86 Item: Title: SEIR Model Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@371a67ec Item: Title: Time-Dependent SIR Model Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@5ed828d Item: Title: Other Proposed Models Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@50d0686 Item: Title: Data-Driven Models Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@7a3d45bd Children: Item: Title: Autoregressive-Integrated Moving Average (ARIMA) Models Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@1e7c7811 Item: Title: Machine learning (ML) Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@77ec78b9 Item: Title: Genetic Evolutionary Programming (GEP) Model Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@1a3869f4 Item: Title: Long Short-Term Memory (LSTM) Model Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@a38d7a3 Item: Title: Global Epidemic and Mobility (GLEM) Model Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@77f99a05 Item: Title: Discussion Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@63440df3 Item: Title: Conclusions Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@3aeaafa6 Item: Title: Appendix A Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@76a3e297 Item: Title: References Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@4d3167f4 Info: Title: Predictive Models for Forecasting Public Health Scenarios: Practical Experiences Applied during the First Wave of the COVID-19 Pandemic Author: Jose M. Martin-Moreno, Antoni Alegre-Martinez, Victor Martin-Gorgojo, Jose Luis Alfonso-Sanchez, Ferran Torres and Vicente Pallares-Carratala Subject: 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. 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