The University of Southampton
University of Southampton Institutional Repository

Assessing the impact of COVID-19 interventions on influenza-like illness in Beijing and Hong Kong: an observational and modeling study

Assessing the impact of COVID-19 interventions on influenza-like illness in Beijing and Hong Kong: an observational and modeling study
Assessing the impact of COVID-19 interventions on influenza-like illness in Beijing and Hong Kong: an observational and modeling study
Background: The impact of coronavirus diseases 2019 (COVID-19) related non-pharmaceutical interventions (NPIs) on influenza activity in the presence of other known seasonal driving factors is unclear, especially at the municipal scale. This study aimed to assess the impact of NPIs on outpatient influenza-like illness (ILI) consultations in Beijing and the Hong Kong Special Administrative Region (SAR) of China.
Methods: We descriptively analyzed the temporal characteristics of the weekly ILI counts, nine NPI indicators, mean temperature, relative humidity, and absolute humidity from 2011 to 2021. Generalized additive models (GAM) using data in 2011–2019 were established to predict the weekly ILI counts under a counterfactual scenario of no COVID-19 interventions in Beijing and the Hong Kong SAR in 2020–2021, respectively. GAM models were further built to evaluate the potential impact of each individual or combined NPIs on weekly ILI counts in the presence of other seasonal driving factors in the above settings in 2020–2021.
Results: The weekly ILI counts in Beijing and the Hong Kong SAR fluctuated across years and months in 2011–2019, with an obvious winter-spring seasonality in Beijing. During the 2020–2021 season, the observed weekly ILI counts in both Beijing and the Hong Kong SAR were much lower than those of the past 9 flu seasons, with a 47.5% [95% confidence interval (CI): 42.3%, 52.2%) and 60.0% (95% CI: 58.6%, 61.1%) reduction, respectively. The observed numbers for these two cities also accounted for only 40.2% (95% CI: 35.4%, 45.3%) and 58.0% (95% CI: 54.1%, 61.5%) of the GAM model estimates in the absence of COVID-19 NPIs, respectively. Our study revealed that, “Cancelling public events” and “Restrictions on internal travel” measures played an important role in the reduction of ILI in Beijing, while the “restrictions on international travel” was statistically most associated with ILI reductions in the Hong Kong SAR. Conclusions: Our study suggests that COVID-19 NPIs had been reducing outpatient ILI consultations in the presence of other seasonal driving factors in Beijing and the Hong Kong SAR from 2020 to 2021. In cities with varying local circumstances, some NPIs with appropriate stringency may be tailored to reduce the burden of ILI caused by severe influenza strains or other respiratory infections in future. Graphical Abstract: [Figure not available: see fulltext.].
Beijing, COVID-19/prevention & control, China/epidemiology, Hong Kong/epidemiology, Humans, Influenza, Human/epidemiology, Seasons
2049-9957
Zhang, Xingxing
776de43a-c3b2-4e40-ad5b-610211017943
Du, Jing
7dce04bb-c7ce-498e-a369-a89c56c909c5
Li, Gang
8168c7cd-7b73-4002-8ae8-469720f4ada1
Chen, Teng
e4e940fa-0ccd-4634-9e2f-0871fae6b85d
Yang, Jin
1b0cbcf3-e97c-446b-8a96-59c726d1d681
Yang, Jiao
135abdc1-6a68-4493-9c81-ce7694575531
Zhang, Ting
ba3edc97-f2e0-4f9e-97b4-be0a039cb8b1
Wang, Qing
f891e0d3-d9b5-4865-b304-23bbed036d6e
Yang, Liuyang
e9874ffe-6130-46ea-b73c-7c6ce629afb8
Lai, Shengjie
b57a5fe8-cfb6-4fa7-b414-a98bb891b001
Feng, Luzhao
5842cd78-bfa7-40d1-ae76-92ca4bf70c4d
Yang, Weizhong
65d18fbc-d752-42a7-ac38-01534ceda15c
Zhang, Xingxing
776de43a-c3b2-4e40-ad5b-610211017943
Du, Jing
7dce04bb-c7ce-498e-a369-a89c56c909c5
Li, Gang
8168c7cd-7b73-4002-8ae8-469720f4ada1
Chen, Teng
e4e940fa-0ccd-4634-9e2f-0871fae6b85d
Yang, Jin
1b0cbcf3-e97c-446b-8a96-59c726d1d681
Yang, Jiao
135abdc1-6a68-4493-9c81-ce7694575531
Zhang, Ting
ba3edc97-f2e0-4f9e-97b4-be0a039cb8b1
Wang, Qing
f891e0d3-d9b5-4865-b304-23bbed036d6e
Yang, Liuyang
e9874ffe-6130-46ea-b73c-7c6ce629afb8
Lai, Shengjie
b57a5fe8-cfb6-4fa7-b414-a98bb891b001
Feng, Luzhao
5842cd78-bfa7-40d1-ae76-92ca4bf70c4d
Yang, Weizhong
65d18fbc-d752-42a7-ac38-01534ceda15c

Zhang, Xingxing, Du, Jing, Li, Gang, Chen, Teng, Yang, Jin, Yang, Jiao, Zhang, Ting, Wang, Qing, Yang, Liuyang, Lai, Shengjie, Feng, Luzhao and Yang, Weizhong (2023) Assessing the impact of COVID-19 interventions on influenza-like illness in Beijing and Hong Kong: an observational and modeling study. Infectious Diseases of Poverty, 12 (1), [11]. (doi:10.1186/s40249-023-01061-8).

Record type: Article

Abstract

Background: The impact of coronavirus diseases 2019 (COVID-19) related non-pharmaceutical interventions (NPIs) on influenza activity in the presence of other known seasonal driving factors is unclear, especially at the municipal scale. This study aimed to assess the impact of NPIs on outpatient influenza-like illness (ILI) consultations in Beijing and the Hong Kong Special Administrative Region (SAR) of China.
Methods: We descriptively analyzed the temporal characteristics of the weekly ILI counts, nine NPI indicators, mean temperature, relative humidity, and absolute humidity from 2011 to 2021. Generalized additive models (GAM) using data in 2011–2019 were established to predict the weekly ILI counts under a counterfactual scenario of no COVID-19 interventions in Beijing and the Hong Kong SAR in 2020–2021, respectively. GAM models were further built to evaluate the potential impact of each individual or combined NPIs on weekly ILI counts in the presence of other seasonal driving factors in the above settings in 2020–2021.
Results: The weekly ILI counts in Beijing and the Hong Kong SAR fluctuated across years and months in 2011–2019, with an obvious winter-spring seasonality in Beijing. During the 2020–2021 season, the observed weekly ILI counts in both Beijing and the Hong Kong SAR were much lower than those of the past 9 flu seasons, with a 47.5% [95% confidence interval (CI): 42.3%, 52.2%) and 60.0% (95% CI: 58.6%, 61.1%) reduction, respectively. The observed numbers for these two cities also accounted for only 40.2% (95% CI: 35.4%, 45.3%) and 58.0% (95% CI: 54.1%, 61.5%) of the GAM model estimates in the absence of COVID-19 NPIs, respectively. Our study revealed that, “Cancelling public events” and “Restrictions on internal travel” measures played an important role in the reduction of ILI in Beijing, while the “restrictions on international travel” was statistically most associated with ILI reductions in the Hong Kong SAR. Conclusions: Our study suggests that COVID-19 NPIs had been reducing outpatient ILI consultations in the presence of other seasonal driving factors in Beijing and the Hong Kong SAR from 2020 to 2021. In cities with varying local circumstances, some NPIs with appropriate stringency may be tailored to reduce the burden of ILI caused by severe influenza strains or other respiratory infections in future. Graphical Abstract: [Figure not available: see fulltext.].

Text
s40249-023-01061-8 - Version of Record
Available under License Creative Commons Attribution.
Download (2MB)

More information

Submitted date: 23 November 2022
Accepted/In Press date: 28 January 2023
Published date: 16 February 2023
Additional Information: © 2023. The Author(s).
Keywords: Beijing, COVID-19/prevention & control, China/epidemiology, Hong Kong/epidemiology, Humans, Influenza, Human/epidemiology, Seasons

Identifiers

Local EPrints ID: 475678
URI: http://eprints.soton.ac.uk/id/eprint/475678
ISSN: 2049-9957
PURE UUID: 797f0992-d565-4dc7-b15c-3571419a819e
ORCID for Shengjie Lai: ORCID iD orcid.org/0000-0001-9781-8148

Catalogue record

Date deposited: 24 Mar 2023 17:34
Last modified: 17 Mar 2024 03:52

Export record

Altmetrics

Contributors

Author: Xingxing Zhang
Author: Jing Du
Author: Gang Li
Author: Teng Chen
Author: Jin Yang
Author: Jiao Yang
Author: Ting Zhang
Author: Qing Wang
Author: Liuyang Yang
Author: Shengjie Lai ORCID iD
Author: Luzhao Feng
Author: Weizhong Yang

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×