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Impact of early phase COVID-19 precautionary behaviors on seasonal influenza in Hong Kong: A time-series modeling approach.
Lin, Chun-Pang; Dorigatti, Ilaria; Tsui, Kwok-Leung; Xie, Min; Ling, Man-Ho; Yuan, Hsiang-Yu.
Afiliación
  • Lin CP; School of Data Science, City University of Hong Kong, Kowloon, Hong Kong SAR, China.
  • Dorigatti I; Department of Statistics, School of Arts and Sciences, Rutgers University, New Brunswick, NJ, United States.
  • Tsui KL; MRC Centre for Global Infectious Disease Analysis, School of Public Health, Imperial College London, London, United Kingdom.
  • Xie M; Grado Department of Industrial and Systems Engineering, College of Engineering, Virginia Polytechnic Institute and State University, Blacksburg, VA, United States.
  • Ling MH; School of Data Science, City University of Hong Kong, Kowloon, Hong Kong SAR, China.
  • Yuan HY; Department of Mathematics and Information Technology, Faculty of Liberal Arts and Social Sciences, The Education University of Hong Kong, Tai Po, Hong Kong SAR, China.
Front Public Health ; 10: 992697, 2022.
Article en En | MEDLINE | ID: mdl-36504934
ABSTRACT

Background:

Before major non-pharmaceutical interventions were implemented, seasonal incidence of influenza in Hong Kong showed a rapid and unexpected reduction immediately following the early spread of COVID-19 in mainland China in January 2020. This decline was presumably associated with precautionary behavioral changes (e.g., wearing face masks and avoiding crowded places). Knowing their effectiveness on the transmissibility of seasonal influenza can inform future influenza prevention strategies.

Methods:

We estimated the effective reproduction number (R t ) of seasonal influenza in 2019/20 winter using a time-series susceptible-infectious-recovered (TS-SIR) model with a Bayesian inference by integrated nested Laplace approximation (INLA). After taking account of changes in underreporting and herd immunity, the individual effects of the behavioral changes were quantified.

Findings:

The model-estimated mean R t reduced from 1.29 (95%CI, 1.27-1.32) to 0.73 (95%CI, 0.73-0.74) after the COVID-19 community spread began. Wearing face masks protected 17.4% of people (95%CI, 16.3-18.3%) from infections, having about half of the effect as avoiding crowded places (44.1%, 95%CI, 43.5-44.7%). Within the current model, if more than 85% of people had adopted both behaviors, the initial R t could have been less than 1.

Conclusion:

Our model results indicate that wearing face masks and avoiding crowded places could have potentially significant suppressive impacts on influenza.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Gripe Humana / COVID-19 Tipo de estudio: Prognostic_studies Límite: Humans Idioma: En Revista: Front Public Health Año: 2022 Tipo del documento: Article País de afiliación: China

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Gripe Humana / COVID-19 Tipo de estudio: Prognostic_studies Límite: Humans Idioma: En Revista: Front Public Health Año: 2022 Tipo del documento: Article País de afiliación: China