Predicting the Tide of the PandemicAn In-Depth Analysis of Forecasting Models for COVID-19 in Cantabria

  1. Alberto Lezcano Lastra 1
  2. Gonzalo Llamosas García 2
  3. Alejandro López Cagigas 1
  4. Francisco Javier Parra Rodríguez 3
  1. 1 Government of Cantabria
  2. 2 Universidad de Málaga
    info

    Universidad de Málaga

    Málaga, España

    ROR https://ror.org/036b2ww28

  3. 3 Universidad Nacional de Educación a Distancia
    info

    Universidad Nacional de Educación a Distancia

    Madrid, España

    ROR https://ror.org/02msb5n36

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Revista:
BEIO, Boletín de Estadística e Investigación Operativa

ISSN: 1889-3805

Año de publicación: 2023

Volumen: 39

Número: 2

Páginas: 36-49

Tipo: Artículo

DIALNET GOOGLE SCHOLAR lock_openAcceso abierto editor

Otras publicaciones en: BEIO, Boletín de Estadística e Investigación Operativa

Resumen

Amidst the COVID-19 pandemic, astute public health interventions, including mobility constraints, are paramount. The bedrock of such strategies lies in the precision of forecasting models. Harnessing data from the Cantabrian Health Service, this study critically evaluates and contrasts time series analysis and cutting-edge machine learning techniques in predicting 30-day COVID-19 case trajectories. Additionally, it demystifies the technological scaffolding and methodologies of the Cantabrian Institute of Statistics’ web portal for streamlined collation and display of socio-health indicators. The analysis underscores the indispensability and acumen of predictive modeling in steering agile responses to public health crises.

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Fuente de los datos: Dialnet