Real-time tracking and prediction of COVID-19 infection using digital proxies of population mobility and mixing
Digital proxies of human mobility and physical mixing have been used to monitor viral transmissibility and the effectiveness of social distancing interventions in the ongoing COVID-19 pandemic. We developed a new framework that parameterises disease transmission models with age-specific digital mobility data. By fitting the model to case data in Hong Kong, we were 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 showed that accurate nowcast and forecast of COVID-19 epidemics can be obtained by integrating valid digital proxies of physical mixing into conventional epidemic models.
About the speaker
Kathy Leung is a research assistant professor from the School of Public Health, The University of Hong Kong. She's a member of HKU's COVID-19 response team since Jan 2020. She has been involved in analysing the epidemiological data from mainland China and Hong Kong to provide suggestions about COVID-19 control strategies to the governments. Her primary research interest is in mathematical modelling of a wide range of communicable and non-communicable diseases, such as influenza, COVID-19, MERS, hand-foot-and-mouth disease, HPV, cervical cancer, colorectal cancer and breast cancer. She also conducts epidemiological and economic evaluations of intervention strategies against communicable and non-communicable diseases, such as HFMD vaccination and breast cancer screening programmes.
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