Study uses population flow data to accurately track the spread of COVID-19
Using population flow data, new research showed a way of tracking the flow of new cases of infectious diseases with considerable accuracy and constructing risk assessments to predict high-risk areas at its early stages. The new method could aid in understanding implementations taken for future outbreaks.
The study, appearing in the peer-reviewed journal Nature, used nation-wide data in mainland China to track population movement throughout January of 2020 outside of the region believed to be the potential COVID-19 place of origin.
“Here, we use mobile-phone-data-based counts of 11,478,484 people egressing or transiting through the prefecture of Wuhan between 1 January and 24 January 2020 as they moved to 296 prefectures throughout China,” the findings say.
In the data examined by researchers, they tracked mobile phone users who spent a minimum of two hours in the purported COVID-19 place of origin. The co-authors noted that no breach of consumer privacy occurred during their study.
“First, we document the efficacy of quarantine in ceasing movement. Second, we show that the distribution of population outflow from Wuhan accurately predicts the relative frequency and geographic distribution of COVID-19 infections through February 19, 2020, across all of China,” according to the study’s co-authors.
“Third, we develop a spatio-temporal “risk source” model that leverages population flow data (which operationalizes risk emanating from epidemic epicenters) to not only forecast confirmed cases, but also to identify high-transmission-risk locales at an early stage. Fourth, we use this risk source model to statistically derive the geographic spread of COVID-19 and the growth pattern based on the population outflow from Wuhan,” the co-authors explained.
Overall, the new method for tracking population movement may be of use for international policy-makers in making swift and accurate risk assessments for future outbreaks of infectious diseases.