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Article: Forecasting of influenza activity and associated hospital admission burden and estimating the impact of COVID-19 pandemic on 2019/20 winter season in Hong Kong

TitleForecasting of influenza activity and associated hospital admission burden and estimating the impact of COVID-19 pandemic on 2019/20 winter season in Hong Kong
Authors
Issue Date31-Jul-2024
PublisherPublic Library of Science
Citation
PLoS Computational Biology, 2024, v. 20, n. 7 July How to Cite?
AbstractLike other tropical and subtropical regions, influenza viruses can circulate year-round in Hong Kong. However, during the COVID-19 pandemic, there was a significant decrease in influenza activity. The objective of this study was to retrospectively forecast influenza activity during the year 2020 and assess the impact of COVID-19 public health social measures (PHSMs) on influenza activity and hospital admissions in Hong Kong. Using weekly surveillance data on influenza virus activity in Hong Kong from 2010 to 2019, we developed a statistical modeling framework to forecast influenza virus activity and associated hospital admissions. We conducted short-term forecasts (1–4 weeks ahead) and medium-term forecasts (1–13 weeks ahead) for the year 2020, assuming no PHSMs were implemented against COVID-19. We estimated the reduction in transmissibility, peak magnitude, attack rates, and influenza-associated hospitalization rate resulting from these PHSMs. For short-term forecasts, mean ambient ozone concentration and school holidays were found to contribute to better prediction performance, while absolute humidity and ozone concentration improved the accuracy of medium-term forecasts. We observed a maximum reduction of 44.6% (95% CI: 38.6% - 51.9%) in transmissibility, 75.5% (95% CI: 73.0% - 77.6%) in attack rate, 41.5% (95% CI: 13.9% - 55.7%) in peak magnitude, and 63.1% (95% CI: 59.3% - 66.3%) in cumulative influenza-associated hospitalizations during the winter-spring period of the 2019/2020 season in Hong Kong. The implementation of PHSMs to control COVID-19 had a substantial impact on influenza transmission and associated burden in Hong Kong. Incorporating information on factors influencing influenza transmission improved the accuracy of our predictions.
Persistent Identifierhttp://hdl.handle.net/10722/350804
ISSN
2023 Impact Factor: 3.8
2023 SCImago Journal Rankings: 1.652

 

DC FieldValueLanguage
dc.contributor.authorLau, Yiu Chung-
dc.contributor.authorShan, Songwei-
dc.contributor.authorWang, Dong-
dc.contributor.authorChen, Dongxuan-
dc.contributor.authorDu, Zhanwei-
dc.contributor.authorLau, Eric H.Y.-
dc.contributor.authorHe, Daihai-
dc.contributor.authorTian, Linwei-
dc.contributor.authorWu, Peng-
dc.contributor.authorCowling, Benjamin J.-
dc.contributor.authorAli, Sheikh Taslim-
dc.date.accessioned2024-11-03T00:30:30Z-
dc.date.available2024-11-03T00:30:30Z-
dc.date.issued2024-07-31-
dc.identifier.citationPLoS Computational Biology, 2024, v. 20, n. 7 July-
dc.identifier.issn1553-734X-
dc.identifier.urihttp://hdl.handle.net/10722/350804-
dc.description.abstractLike other tropical and subtropical regions, influenza viruses can circulate year-round in Hong Kong. However, during the COVID-19 pandemic, there was a significant decrease in influenza activity. The objective of this study was to retrospectively forecast influenza activity during the year 2020 and assess the impact of COVID-19 public health social measures (PHSMs) on influenza activity and hospital admissions in Hong Kong. Using weekly surveillance data on influenza virus activity in Hong Kong from 2010 to 2019, we developed a statistical modeling framework to forecast influenza virus activity and associated hospital admissions. We conducted short-term forecasts (1–4 weeks ahead) and medium-term forecasts (1–13 weeks ahead) for the year 2020, assuming no PHSMs were implemented against COVID-19. We estimated the reduction in transmissibility, peak magnitude, attack rates, and influenza-associated hospitalization rate resulting from these PHSMs. For short-term forecasts, mean ambient ozone concentration and school holidays were found to contribute to better prediction performance, while absolute humidity and ozone concentration improved the accuracy of medium-term forecasts. We observed a maximum reduction of 44.6% (95% CI: 38.6% - 51.9%) in transmissibility, 75.5% (95% CI: 73.0% - 77.6%) in attack rate, 41.5% (95% CI: 13.9% - 55.7%) in peak magnitude, and 63.1% (95% CI: 59.3% - 66.3%) in cumulative influenza-associated hospitalizations during the winter-spring period of the 2019/2020 season in Hong Kong. The implementation of PHSMs to control COVID-19 had a substantial impact on influenza transmission and associated burden in Hong Kong. Incorporating information on factors influencing influenza transmission improved the accuracy of our predictions.-
dc.languageeng-
dc.publisherPublic Library of Science-
dc.relation.ispartofPLoS Computational Biology-
dc.titleForecasting of influenza activity and associated hospital admission burden and estimating the impact of COVID-19 pandemic on 2019/20 winter season in Hong Kong-
dc.typeArticle-
dc.identifier.doi10.1371/journal.pcbi.1012311-
dc.identifier.pmid39083536-
dc.identifier.scopuseid_2-s2.0-85200158057-
dc.identifier.volume20-
dc.identifier.issue7 July-
dc.identifier.eissn1553-7358-
dc.identifier.issnl1553-734X-

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