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Dining-out behavior as a proxy for superspreading potential of SARS-CoV-2 infections: Modelling analysis
Ka Chun Chong;
Kehang Li;
Zihao Guo;
Katherine Jia;
Eman Leung;
Shi Zhao;
Chi Tim Hung;
Carrie Yam;
Tsz Yu Chow;
Dong Dong;
Huwen Wang;
Yuchen Wei;
Eng Kiong Yeoh
ABSTRACT
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 the dining-out behaviour 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 the 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 and Apple Inc.
Results:
A total of 6,391 clusters involving 8,375 cases was 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 Inc, the mobility of dining-out behavior explained the highest variability of k (ΔR-sq=9.7%, 95% CrI: 5.7% to 13.2%) and Rt (ΔR-sq=15.7%, 95% CrI: 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 utilizing digital mobility proxies of dining-out patterns to generate early warnings of superspreading events. Clinical Trial: NA
Citation
Please cite as:
Chong KC, Li K, Guo Z, Jia K, Leung E, Zhao S, Hung CT, Yam C, Chow TY, Dong D, Wang H, Wei Y, Yeoh EK
Dining-Out Behavior as a Proxy for the Superspreading Potential of SARS-CoV-2 Infections: Modeling Analysis