paper titled “Population Flow Drives Spatio-Temporal Distribution of COVID-19 in China” was published on Nature on Wednesday. it used mobile phone data, it trace the population flow in China and predicting the geographical and spread of Coronavirus in the country.
The study analyzed the distribution of the out going population from Wuhan at the beginning of the outbreak in January 2020, showing distribution of population out going can accurately predict the geographical locations of Coronavirus infections in China up to two weeks in advance.
The research team consists of researchers from Chinese universities in Hong Kong, Changsha, Chengdu and Shenzhen, and from Yale University in the United States.
The Study was Submitted on February 18, the study used anonymous mobile phone data based counts of 11,478,484 people leaving or transiting through Wuhan between January 1 and January 24, 2020, as they moved to 296 prefectures throughout China.
According to the data, researchers developed a spatio-temporal “risk source” model that holds population flow data to not only forecast confirmed cases of but also to identify high-transmission-risk locales at an early stage. The model was used to statistically obtain the geographic spread of the Coronavirus and the growth pattern based on the population out going from Wuhan.
According to the researcher in the paper “The logic of our population-flow-based ‘risk source’ model differs from classic epidemiological models that rely on assumptions regarding population mixing, population compartment sizes, and viral properties.”
According to the researchers, the model can help countries to make rapid and accurate risk assessments and to plan allocation of limited resources ahead of ongoing outbreaks.