Skip directly to site content Skip directly to page options Skip directly to A-Z link Skip directly to A-Z link Skip directly to A-Z link
Volume 28, Number 3—March 2022
Research Letter

Restaurant-Based Measures to Control Community Transmission of COVID-19, Hong Kong

Author affiliations: World Health Organization Collaborating Centre for Infectious Disease Epidemiology and Control, University of Hong Kong, Hong Kong, China (F. Ho, T.K. Tsang, H. Gao, J. Xiao, E.H.Y. Lau, J.Y. Wong, P. Wu, G.M. Leung, B.J. Cowling); Hong Kong Science and Technology Park, Hong Kong (E.H.Y. Lau, P. Wu, G.M. Leung, B.J. Cowling)

Cite This Article

Abstract

Controlling transmission in restaurants is an important component of public health and social measures for coronavirus disease. We examined the effects of restaurant measures in Hong Kong. Our findings indicate that shortening operating hours did not have an effect on time-varying effective reproduction number when capacity was already reduced.

As of April 14, 2021, a total of 11,608 cases and 207 deaths from coronavirus disease (COVID-19) had been reported in Hong Kong (1). A series of community epidemics have occurred, the largest of which have been the third wave in June–October 2020, which had 3,978 cases, and the fourth wave in November 2020–March 2021, which had 6,048 cases. To suppress local transmission of COVID-19, the government implemented a combination of public health and social measures (PHSMs): bar closures, restaurant capacity restrictions and opening hour restrictions, bans on live music performances and dancing, and work-from-home advisories (2). Ongoing assessment of the effect of these measures on transmission can guide evidence-based policy. One type of location in which COVID-19 transmission is known to occur is restaurants (3). Earlier studies have evaluated the impact of PHSMs, including restrictions on large group gatherings (46), but the specific effect of restaurant measures was not studied. Here we focus on the effect of restaurant measures on transmission in Hong Kong.

Figure

Use of public health and social measures (PHSMs) to reduce transmission of coronavirus disease in 2 waves of the epidemic, Hong Kong, 2020–2021. A) Incidence and implementation of PHSMs during wave 3, June 15–September 30, 2020. B) Incidence and implementation of PHSMs during wave 4, November 1, 2020–March 20, 2021. Dark and light gray bars represent the incidence of unlinked local cases and linked local cases of coronavirus disease in Hong Kong. Linked local cases are cases that are linked initially or after epidemiological investigation. Effective periods of PHSMs related to restaurants are shown in shaded areas in different colors.

Figure. Use of public health and social measures (PHSMs) to reduce transmission of coronavirus disease in 2 waves of the epidemic, Hong Kong, 2020–2021. A) Incidence and implementation of PHSMs during...

We collected details and time of implementation of each intervention of all the PHSMs applied during the third and fourth waves from the official reports of the Hong Kong government (7) (Appendix Table 1). In wave 3, a ban on dine-in service after 6:00 pm was in force during July 15–August 27, 2020 (Figure, panel A). Other PHSMs were implemented on the same day and kept in place for longer. Wave 4 was initiated by multiple superspreading events in a network of dancing venues. A ban on dine-in service after 6:00 pm was implemented on December 10, 2020, which was a week to a month later than the implementation of other PHSMs (Figure, panel B). Hence, we could disentangle the effect of shortened dine-in hours from other measures. No other PHSMs were implemented before the study period.

To determine the effect of the ban on dine-in services after 6:00 pm, we applied a previous approach to estimate time-varying reproduction number (Rt) (8,9). Then, we fitted LASSO regression models to log(Rt) to assess the effect of the ban on dine-in services after 6:00 pm on Rt, accounting for the effect from other PHSMs (10). We allowed for a 7-day lag between implementation of a measure and its effect on incidence, to account for the incubation period. In both waves, we grouped the PHSMs other than ban on dine-in services after 6:00 pm into a single variable to indicate the period when >3 of these other PHSMs were in place.

We estimated that the ban on dine-in services after 6:00 pm did not reduce Rt in both waves, but other PHSMs were associated with substantial reductions in Rt. In wave 3, Rt rose rapidly to 4.5 on June 27, 2020, but ≈1 week after measures were applied it was <1.0 (Appendix Figure, panel A). Implementation of >3 other PHSMs was associated with a 53% (95% CI 44%–59%) decrease in Rt (Table).

In wave 4, Rt increased to 3.1 on November 16, 2020, and then decreased to ≈1.0 after PHSMs began (Appendix Figure, panel B). Implementation of >3 other PHSMs was associated with a 40% (95% CI 28%–47%) decrease in Rt. Another model that excluded basic civil service arrangement in other PHSMs showed that a ban on dine-in service beginning at 6:00 pm did not have an effect (Table). We performed sensitivity analysis to remove the effect of superspreading in wave 3 by changing the start date to July 1, 2020; we found the ban on dine-in service from 6:00 pm did not have an effect (Appendix Table 2).

Our analysis suggested that the PHSMs were critical for suppressing the third and fourth waves of COVID-19 in Hong Kong. However, we found that a ban on dine-in hours after 6:00 pm might not have had an effect in both waves when capacity was already reduced. A complete closure of restaurants in Hong Kong would have considerable social impact because dining out is very common. We hypothesize that encouraging restaurants to extend dine-in hours, but with capacity restrictions to reduce crowding, could be a reasonable approach to reduce transmission.

A limitation of our analysis is that we cannot distinguish the effect of some PHSMs because they began simultaneously. We cannot rule out that a ban on dine-in service after 6:00 pm might have an effect if it began earlier than other PHSMs or in regions with high incidences. In addition, changes in Rt are a consequence of individual behavioral changes such as avoiding crowded areas; increasing incidence and implementation of multiple PHSMs could raise the public’s perception of risk. Determining the effectiveness of alternative PHSMs would provide evidence-based guidance on control strategies.

Ms. Ho is a research postgraduate student at the School of Public Health, University of Hong Kong. Her research interest is the transmission and control of emerging infections.

Top

Acknowledgment

This project was supported by the Health and Medical Research Fund, Food and Health Bureau, Government of the Hong Kong Special Administrative Region (grant no. COVID190118) and the Collaborative Research Fund (project no. C7123-20G), and by the general research fund (project no. 17110221) of the Research Grants Council of the Hong Kong SAR Government. B.J.C. and P.W. are supported by the AIR@innoHK program of the Innovation and Technology Commission of the Hong Kong SAR Government.

Top

References

  1. The Centre for Health Protection (CHP) of the Department of Health. (DH) of Hong Kong. CHP investigates 13 additional confirmed cases of COVID-19. 2021 [cited 2021 Apr 15]. https://www.info.gov.hk/gia/general/202104/13/P2021041300746.htm
  2. The Government of the Hong Kong Special Administrative Region. Government further tightens social distancing measures. 2020 [cited 2021 Mar 29]. https://www.info.gov.hk/gia/general/202007/14/P2020071400010.htm
  3. Lu  J, Gu  J, Li  K, Xu  C, Su  W, Lai  Z, et al. COVID-19 outbreak associated with air conditioning in restaurant, Guangzhou, China, 2020. Emerg Infect Dis. 2020;26:162831. DOIPubMedGoogle Scholar
  4. Flaxman  S, Mishra  S, Gandy  A, Unwin  HJT, Mellan  TA, Coupland  H, et al.; Imperial College COVID-19 Response Team. Estimating the effects of non-pharmaceutical interventions on COVID-19 in Europe. Nature. 2020;584:25761. DOIPubMedGoogle Scholar
  5. Islam  N, Sharp  SJ, Chowell  G, Shabnam  S, Kawachi  I, Lacey  B, et al. Physical distancing interventions and incidence of coronavirus disease 2019: natural experiment in 149 countries. BMJ. 2020;370:m2743. DOIPubMedGoogle Scholar
  6. Brauner  JM, Mindermann  S, Sharma  M, Johnston  D, Salvatier  J, Gavenčiak  T, et al. Inferring the effectiveness of government interventions against COVID-19. Science. 2021;371:6531. DOIPubMedGoogle Scholar
  7. The Government of the Hong Kong Special Administrative Region. Press releases. 2020 [cited 2021 Mar 21]. https://www.info.gov.hk/gia/general/today.htm
  8. Cori  A, Ferguson  NM, Fraser  C, Cauchemez  S. A new framework and software to estimate time-varying reproduction numbers during epidemics. Am J Epidemiol. 2013;178:150512. DOIPubMedGoogle Scholar
  9. Tsang  TK, Wu  P, Lau  EHY, Cowling  BJ. Accounting for imported cases in estimating the time-varying reproductive number of coronavirus disease 2019 in Hong Kong. J Infect Dis. 2021;224:7837. DOIPubMedGoogle Scholar
  10. Friedman  J, Hastie  T, Tibshirani  R. Regularization paths for generalized linear models via coordinate descent. J Stat Softw. 2010;33:122. DOIPubMedGoogle Scholar

Top

Figure
Tables

Top

Cite This Article

DOI: 10.3201/eid2803.211015

Original Publication Date: February 07, 2022

Table of Contents – Volume 28, Number 3—March 2022

EID Search Options
presentation_01 Advanced Article Search – Search articles by author and/or keyword.
presentation_01 Articles by Country Search – Search articles by the topic country.
presentation_01 Article Type Search – Search articles by article type and issue.

Top

Comments

Please use the form below to submit correspondence to the authors or contact them at the following address:

Tim K. Tsang, University of Hong Kong—WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, Patrick Manson Building, 7 Sassoon Rd, Pokfulam, Hong Kong School of Public Health, Hong Kong

Send To

10000 character(s) remaining.

Top

Page created: January 11, 2022
Page updated: February 21, 2022
Page reviewed: February 21, 2022
The conclusions, findings, and opinions expressed by authors contributing to this journal do not necessarily reflect the official position of the U.S. Department of Health and Human Services, the Public Health Service, the Centers for Disease Control and Prevention, or the authors' affiliated institutions. Use of trade names is for identification only and does not imply endorsement by any of the groups named above.
file_external