key: cord-1048873-1wncvno9 authors: Zhang, Nan; Jia, Wei; Lei, Hao; Wang, Peihua; Zhao, Pengcheng; Guo, Yong; Dung, Chung-Hin; Bu, Zhongming; Xue, Peng; Xie, Jingchao; Zhang, Yingping; Cheng, Reynold; Li, Yuguo title: Effects of human behaviour changes during the COVID-19 pandemic on influenza spread in Hong Kong date: 2020-12-04 journal: Clin Infect Dis DOI: 10.1093/cid/ciaa1818 sha: f3604a779c6aeb5085733eb0d3ff1cf26133d698 doc_id: 1048873 cord_uid: 1wncvno9 BACKGROUND: COVID-19 continues to threaten human life worldwide. We explored how human behaviours have been influenced by the COVID-19 pandemic in Hong Kong, and how the transmission of other respiratory diseases (e.g. influenza) has been influenced by human behaviour. METHODS: We focused on the spread of COVID-19 and influenza infections based on reported COVID-19 cases and influenza surveillance data, and investigated the changes in human behaviour due to COVID-19 based on mass transit railway data and the data from a telephone survey. We did the simulation based on SEIR model to assess the risk reduction of influenza transmission caused by the changes in human behaviour. 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 centres, 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 behaviours reduced the effective reproduction number of influenza by 63.1%. CONCLUSIONS: Human behaviours 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. The SARS coronavirus 2 (SARS-CoV-2) is believed to be mainly transmitted via the close contact route 6, 7 . The infection risk in indoor environments is much higher than that in outdoor environments because of possible insufficient ventilation, long periods spent indoors, high close contact rate, and many frequently touched public surfaces [8] [9] [10] [11] . Many nonpharmaceutical interventions, which aim to encourage social distancing and reduce the exposure time and the risk of infection during close contact, have been implemented for infection prevention and control [12] [13] [14] [15] [16] [17] . Human behaviour change is crucial to prevent transmission in the absence of pharmaceutical interventions 18 . However, data on the relevant human behaviours are lacking. In the study, we analysed how human behaviours including local travel, indoor-stay, close contacts, mask wearing, and behaviours during the symptom onset period were influenced by the COVID-19 pandemic in Hong Kong based on more than one billion records of smart card data of the mass transit railway (MTR) and 1,021 data points from a telephone survey. We also analysed how human behaviours influenced the spread of respiratory infections based on Data on the occurrence of COVID-19 and sentinel surveillance data on influenza-like illness were obtained from Hong Kong Centre for Health Protection 5, 19 . Centre for Health Protection (CHP) in Hong Kong provides a definition of cases of COVID-19. In brief, confirmation of a case required the detection (e.g., by reverse transcription-polymerase chain reaction (RT-PCR)) of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in a clinical specimen. Demographic data were obtained from the Census and Statistics Department of Hong Kong 20 . We obtained local travel data from Hong Kong Mass Transit Railway Corporation covered the period from 1 January to 30 April of both 2019 and 2020 (Appendix A for detailed description). A cross-sectional telephone survey was also performed from 22 May to 7 June 2020 to collect Hong Kong residents' information including general personal information (e.g. age and sex) and their infection-related behaviour. The specific survey methods are described in Appendix B; 1,021 surveys were finally completed. We considered five groups of human behaviours: local travel, indoor-stay, close contact, mask wearing, and visiting the doctor or other public places during the symptom onset period. The local travel behaviour was analysed based on the MTR data, with passengers divided into five categories (adults, children, students, older people, and others) based on the type of their smart card. The other four behaviour types were analysed based on the A c c e p t e d M a n u s c r i p t 7 telephone-survey data (Appendix C). In the survey, respondents answered questions on various behaviours during the normal period (26 March to 1 April 2019) and the COVID-19 pandemic (26 March to 1 April 2020). We divided all respondents into four categories: workers, students, older people, and others (all people excluding workers, students, and older people). All indoor environments were divided into eight categories 12 : residence (e.g. home, dormitory, and hotel), workplace (only indoors), place of study, restaurant (including dining rooms in workplaces and places of study), shopping centre, market (including supermarkets), public transport, and others. In the survey, a close contact was defined as either a two-way conversation involving five or more words in the physical presence of another person, or a direct physical contact (e.g. a handshake or a hug) 21 . The close contact rate was defined as the ratio of close contact time to total indoor time (time spent asleep in one's residence is not counted in total indoor time). The effective reproduction number (R t ) was calculated (Appendix D). In the study, we used the susceptible-exposed-infected-recovered (SEIR) model to simulate the transmission of both COVID-19 and influenza (Appendix E for detailed calculation and parameter setting). Because Hong Kong had a strict strategy on mandatory quarantine for imported population and the arrival population was reduced by more than 97% after March (Appendix F), we did not consider the influence by imported and exported population. As of 31 May 2020, Hong Kong had 1,085 confirmed COVID-19 cases, including 178 imported cases without 14-day mandatory quarantine, 487 cases with 14-day mandatory quarantine, 327 local cases, and 93 unidentified cases (possible imported and local infections). Among the 1,085 cases, 234 were asymptomatic and 851 were symptomatic. and 31.0%, respectively (Table 2 ). Specifically, time spent in shopping centres, restaurants, public transport, and markets decreased by 73.5%, 67.8%, 51.1%, and 41.3%, respectively. (4) Mask-wearing behaviour during symptom onset period During the pandemic, almost all people (98%-100%) responded that they would wear a mask in all public indoor environments if they had the symptom (Table 3) . Restaurants had a relatively low mask-wearing rate (only non-eating time was considered). Few people (6.9%) A c c e p t e d M a n u s c r i p t 11 wore a mask in residences. Students had the highest mask-wearing rate when ill. The detailed time-variant mask-wearing rate is shown in Appendix I. During the normal period, 4.6% of residents would not see doctor if they had a fever and cough. Older people on average delayed for 0.87 days before visiting a doctor upon the onset of these symptoms, which is shorter than workers (1.04 days) and students (1.07 days). However, during the pandemic period, the overall delay for all types of residents fell to 0.38-0.39 days, a reduction of 63.2%, and only 2.0% of residents reported that they would not see a doctor under any circumstance. Compared with the normal period, residents' probability of visiting public indoor environments during the pandemic period was reduced by at least 90%. (Appendix J for detailed values). during the pandemic period, and the total contact duration was reduced by 10%. Due to these behavioural changes, the total infected population according to the SEIR simulation was reduced by 47%. The peak value of the infected percentage was reduced from 49.6% to 6.8%. The R t for COVID-19 was reduced from 2.5 to 1.2 due to the changes in close contact behaviours. 25 . In other words, the human behaviour changes in those cities were much more profound than those in Hong Kong. Some Hong Kong residents had close contact with more than 30 people per day in public indoor environments during the pandemic. If any of these were superspreaders, the cross-infection risk would be very high 26 The number of daily contacts and the close contact rate had a significant association with resident type (workers, students, older people, and others) and pandemic status (normal/pandemic) (Tables S12 to S16). For all resident groups, the daily number of close contacts and the close contact rate significantly decreased due to the pandemic (p < .001). Among all people, students had the largest reduction rate in the number of daily contacts, followed by workers, others, and older people. In addition, the daily number of contacts decreased with increasing age. During the pandemic, workers had many more daily contacts than those in the other three groups. The reduction in close contact rate was the greatest in places of study and the smallest in residences. The close contact time in different indoor environments also showed a significant difference. Places of study, workplaces, and residences had high close contact rates during the normal period, and residences, workplaces, and restaurants had high close contact rates during the pandemic. In the normal period, a previous study showed that the ratio of close contact rates in homes, schools, workplaces, and shopping malls was 12:6:6:1 27 All authors declare no competing interests. A c c e p t e d M a n u s c r i p t Table Table 1 . Indoor-stay time on weekdays and weekends during the normal and pandemic periods by respondent categories (workers, students, and older people) in different indoor environments (more detailed data are listed in Table S3 ). M a n u s c r i p t 23 A c c e p t e d M a n u s c r i p t World Health Organization. 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