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.
【저자키워드】 SARS-CoV-2, Epidemiology, Computational models, Statistical methods, 【초록키워드】 COVID-19, social distancing, COVID-19 pandemic, Infection, Epidemics, Intervention, Local, Epidemic, Transmissibility, Digital, Reproduction number, Travel, Effectiveness, Transmission model, Hong Kong, disease transmission, real time, MONITOR, physical, effective, develop, can be used, reducing, 【제목키워드】 COVID-19 infection,