Enhancing Influenza-Like Illness forecasting: An ensemble approach combining mathematical and deep learning models amidst the COVID-19 pandemic

These findings underscore the power of hybrid forecasting approaches to improve epidemic preparedness and response, providing a flexible data-driven framework that can evolve with changing transmission dynamics and extend to other emerging infectious threats.

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Publication Details

Journal
Epidemics
Published
2026-02-27
DOI
https://doi.org/10.1016/j.epidem.2026.100901
Primary Topic
COVID-19 epidemiological studies
Type
article
Field-Weighted Citation Impact
0.00
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article

Enhancing Influenza-Like Illness forecasting: An ensemble approach combining mathematical and deep learning models amidst the COVID-19 pandemic

Amanda Bleichrodt, Kang Hyun Yoon, Sunmi Lee, Gerardo Chowell
Epidemics
COVID-19 epidemiological studies
article

Enhancing Influenza-Like Illness forecasting: An ensemble approach combining mathematical and deep learning models amidst the COVID-19 pandemic

Amanda Bleichrodt, Kang Hyun Yoon, Sunmi Lee, Gerardo Chowell
article en

Abstract

These findings underscore the power of hybrid forecasting approaches to improve epidemic preparedness and response, providing a flexible data-driven framework that can evolve with changing transmission dynamics and extend to other emerging infectious threats.

EpidemicsVol. 54
Georgia State University (US), Kyung Hee University (KR)
Good health and well-being
Openalex Percentile: Top 47%
COVID-19 epidemiological studies
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