%0 Journal Article
%@ 2369-2960
%I JMIR Publications
%V 9
%N 
%P e44251
%T Dining-Out Behavior as a Proxy for the Superspreading Potential of SARS-CoV-2 Infections: Modeling Analysis
%A Chong,Ka Chun
%A Li,Kehang
%A Guo,Zihao
%A Jia,Katherine Min
%A Leung,Eman Yee Man
%A Zhao,Shi
%A Hung,Chi Tim
%A Yam,Carrie Ho Kwan
%A Chow,Tsz Yu
%A Dong,Dong
%A Wang,Huwen
%A Wei,Yuchen
%A Yeoh,Eng Kiong
%+ Centre for Health Systems and Policy Research, Jockey Club School of Public Health and Primary Care, The Chinese University of Hong Kong, Room 415, School of Public Health Building, Prince of Wales Hospital, Shatin, New Territories, Hong Kong, 852 22528702, yeoh_ek@cuhk.edu.hk
%K COVID-19
%K contact tracing
%K unlinked
%K superspreading
%K dispersion
%K public health
%K surveillance
%K digital health surveillance
%K digital surveillance
%K disease spread
%D 2023
%7 7.3.2023
%9 Original Paper
%J JMIR Public Health Surveill
%G English
%X Background: While many studies evaluated the reliability of digital mobility metrics as a proxy of SARS-CoV-2 transmission potential, none examined the relationship between dining-out behavior and the superspreading potential of COVID-19. Objective: We employed the mobility proxy of dining out in eateries to examine this association in Hong Kong with COVID-19 outbreaks highly characterized by superspreading events. Methods: We retrieved the illness onset date and contact-tracing history of all laboratory-confirmed cases of COVID-19 from February 16, 2020, to April 30, 2021. We estimated the time-varying reproduction number (Rt) and dispersion parameter (k), a measure of superspreading potential, and related them to the mobility proxy of dining out in eateries. We compared the relative contribution to the superspreading potential with other common proxies derived by Google LLC and Apple Inc. Results: A total of 6391 clusters involving 8375 cases were used in the estimation. A high correlation between dining-out mobility and superspreading potential was observed. Compared to other mobility proxies derived by Google and Apple, the mobility of dining-out behavior explained the highest variability of k (ΔR-sq=9.7%, 95% credible interval: 5.7% to 13.2%) and Rt (ΔR-sq=15.7%, 95% credible interval: 13.6% to 17.7%). Conclusions: We demonstrated that there was a strong link between dining-out behaviors and the superspreading potential of COVID-19. The methodological innovation suggests a further development using digital mobility proxies of dining-out patterns to generate early warnings of superspreading events. 
%M 36811849
%R 10.2196/44251
%U https://publichealth.jmir.org/2023/1/e44251
%U https://doi.org/10.2196/44251
%U http://www.ncbi.nlm.nih.gov/pubmed/36811849