Predicting COVID-19 transmission trends in real time with population mobility data
HKU SPH researchers have developed a framework to predict the transmissibility of infectious diseases in near real time by incorporating digital information about population movements into existing epidemic prediction models. Using COVID-19 as an example, the researchers integrated age-specific digital transactions made through an electronic payment system commonly used for public transportation in Hong Kong with conventional epidemic models to track the local effective reproduction number (R¬t) of COVID-19. The R¬¬t is the average number of people one case is estimated to infect at any given time throughout a course of an epidemic. By fitting their model to case data in Hong Kong, the researchers were able to track the Rt estimated by conventional models without reporting delays of new cases. This indicates that population mobility data can be used to aid near real time predictions of infectious disease transmission in populations, which can be used to inform and assess public health measures for disease control.
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