Effects of Human Behavior Changes During the Coronavirus Disease 2019 (COVID-19) Pandemic on Influenza Spread in Hong Kong

Clin Infect Dis. 2021 Sep 7;73(5):e1142-e1150. doi: 10.1093/cid/ciaa1818.

Abstract

Background: Coronavirus disease 2019 (COVID-19) continues to threaten human life worldwide. We explored how human behaviors have been influenced by the COVID-19 pandemic in Hong Kong, and how the transmission of other respiratory diseases (eg, influenza) has been influenced by human behavior.

Methods: We focused on the spread of COVID-19 and influenza infections based on the reported COVID-19 cases and influenza surveillance data and investigated the changes in human behavior due to COVID-19 based on mass transit railway data and the data from a telephone survey. We did the simulation based on a susceptible-exposed-infected-recovered (SEIR) model to assess the risk reduction of influenza transmission caused by the changes in human behavior.

Results: During the COVID-19 pandemic, the number of passengers fell by 52.0% compared with the same period in 2019. Residents spent 32.2% more time at home. Each person, on average, came into close contact with 17.6 and 7.1 people per day during the normal and pandemic periods, respectively. Students, workers, and older people reduced their daily number of close contacts by 83.0%, 48.1%, and 40.3%, respectively. The close contact rates in residences, workplaces, places of study, restaurants, shopping centers, markets, and public transport decreased by 8.3%, 30.8%, 66.0%, 38.5%, 48.6%, 41.0%, and 36.1%, respectively. Based on the simulation, these changes in human behavior reduced the effective reproduction number of influenza by 63.1%.

Conclusions: Human behaviors were significantly influenced by the COVID-19 pandemic in Hong Kong. Close contact control contributed more than 47% to the reduction in infection risk of COVID-19.

Keywords: COVID-19; close contact; human behavior; influenza; nonpharmaceutical interventions.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Aged
  • COVID-19*
  • Hong Kong / epidemiology
  • Humans
  • Influenza, Human* / epidemiology
  • Pandemics
  • SARS-CoV-2