Real-time tracking and prediction of COVID-19 infection using digital proxies of population mobility and mixing

• Published in: Nature communications Vol. 12; no. 1; p. 1501
• Main Authors: Leung, Kathy, Wu, Joseph T., Leung, Gabriel M.
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 08-03-2021; Nature Publishing Group; Nature Portfolio
• Subjects: 631/114/2397; 631/114/2415; 631/326/596/4130; 692/308/174; Age; Basic Reproduction Number; Coronaviruses; COVID-19; COVID-19 - epidemiology; COVID-19 - transmission; Disease control; Disease transmission; Epidemic models; Epidemics; Epidemiological Monitoring; Forecasting; Hong Kong - epidemiology; Humanities and Social Sciences; Humans; Infections; Mobility; Models, Theoretical; multidisciplinary; Pandemics; Real time; Science; Science (multidisciplinary); Social distancing; Travel; Viral diseases; Hong Kong
• ISSN: 2041-1723; 2041-1723
• DOI: 10.1038/s41467-021-21776-2

Digital proxies of human mobility and physical mixing have been used to monitor viral transmissibility and effectiveness of social distancing interventions in the ongoing COVID-19 pandemic. We develop a new framework that parameterizes disease transmission models with age-specific digital mobility data. By fitting the model to case data in Hong Kong, we are able to accurately track the local effective reproduction number of COVID-19 in near real time (i.e., no longer constrained by the delay of around 9 days between infection and reporting of cases) which is essential for quick assessment of the effectiveness of interventions on reducing transmissibility. Our findings show that accurate nowcast and forecast of COVID-19 epidemics can be obtained by integrating valid digital proxies of physical mixing into conventional epidemic models.
Digital proxies of human mobility can be used to monitor social distancing, and therefore have potential to infer COVID-19 dynamics. Here, the authors integrate travel card data from Hong Kong into a transmission model and show that it can be used to track transmissibility in near real-time.