Predicted versus observed epidemic curves over time. (copyright: Nature)
Our model aggregates population outflow from Wuhan from January 1 to 24, 2020 to provide a reference growth pattern (i.e. epidemic curves) for COVID-19's spread. Differences in the predicted and confirmed growth in confirmed cases can signal higher levels of COVID-19 community transmission.
An international research team led by the University of Hong Kong (HKU) developed a new method to accurately track the spread of COVID-19 using population flow data, and establishing a new risk assessment model to identify high-risk locales of COVID-19 at an early stage, which serves as a valuable toolkit to public health experts and policy makers in implementing infectious disease control during new outbreaks. The study findings have been published in the journal Nature today (April 29).
Dr. Jayson Jia, Associate Professor of Marketing at the Faculty of Business and Economics of HKU and lead author of the study, and his co-authors used nation-wide data provided by a major national carrier in China to track population movement out of Wuhan between 1 January and 24 January 2020, a period covering the annual Chunyun mass migration before the Chinese Lunar New Year to a lockdown of the city to contain the virus. The movement of over 11 million people travelling through Wuhan to 296 prefectures in 31 provinces and regions in China were tracked.
Differing from usual epidemiological models that rely on historical data or assumptions, the team used real-time data about actual movements focusing on aggregate population flow rather than individual tracking. The data include any mobile phone user who had spent at least 2 hours in Wuhan during the study period. Locations were detected once users had their phones on. As only aggregate data was used and no individual data was used, there was no threat to consumer privacy.
Combining the population flow data with the number and location of COVID-19 confirmed cases up to 19 February 2020 in China, Dr Jia's team established a strong correlation between people leaving Wuhan and the number of infections in each city. The relative quantity of human movement from the disease epicentre, in this case, Wuhan, directly predicted the relative frequency and geographic distribution of the number of COVID-19 cases across China. The researchers found that this model can explain 96% of the spread of COVID-19 across China statistically.
The research team then used this empirical relationship to build a new risk detection toolkit. Leveraging on the population flow data, the researchers created an "expected growth pattern" based on the number of people arriving from the risk source, i.e. the disease epicentre. The team thereby developed a new risk model by contrasting expected growth of cases against the actual number of confirmed cases for each city in China, the difference being the "community transmission risk".
"If there are more confirmed cases than expected ones, there is a higher risk of community spread. If there are fewer expected cases than reported, it means that the city's preventive measures are particularly effective or it can indicate that further investigation by central authorities is needed to eliminate possible risks from inaccurate measurement," explained Dr Jia.
"What is innovative about our approach is that we use misprediction to assess the level of community risk. Our model accurately tells us how many cases we should expect given travel data. We contrast this against the confirmed cases using the logic that what cannot be explained by imported cases and primary transmissions should be community spread." He added.
The approach is advantageous because it requires no assumptions or knowledge of how or why the virus spreads, is robust to data reporting inaccuracies, and only requires knowledge of relative distribution of human movement. It can be used by policy makers in any nation with available data to make rapid and accurate risk assessments and to plan allocation of limited resources ahead of ongoing disease outbreaks.
"Our research indicates that geographic flow of people outperforms other measures such as population size, wealth or distance from the risk source to indicate the gravity of an outbreak." said Dr Jia.
Dr Jia is currently exploring with fellow researchers the feasibility of applying this toolkit to other countries, and extending it to situations where there are multiple COVID-19 epicentres. The team is working with other national telecom carriers and seeking additional data partners.
The study's co-authors are Jianmin Jia, Presidential Chair Professor at the Chinese University of Hong Kong, Shenzhen (corresponding author); Nicholas A. Christakis, Sterling Professor of Social and Natural Science at Yale; Xin Lu, the National University of Defense Technology in Changsha, China, and the Karolinska Institutet in Stockholm, Sweden; Yun Yuan, Southwest Jiaotong University; Ge Xu, Hunan University of Technology and Business.
