When the COVID-19 outbreak first started in early 2020, no one, even the scientists, knew much about the virus and how it was transmitted. To analyse the transmission patterns of the disease among different populations, Dr. Yang Liu, Assistant Professor of the Department of Computer Science, HKBU, and his research team quickly initiated a modelling study to examine the spread of the disease through computational modelling and AI technology.
Launched in February 2020, the study is one of the first modelling studies on COVID-19 in the world. Dr. Liu and his team carried out the study by discussing with the experts from China CDC (Chinese Center for Disease Control and Prevention) and using the data collecting from governmental websites, such as The Health Commission of Hubei Province and the Beijing Municipal Health Commission, and developed a computational method to measure the contact intensity between people from spatial and temporal perspectives.
“Different from many existing disease modellings which focus on predicting the total number of cases in the future, what we are exploring is the intrinsic transmission pattern of the disease,” says Dr. Liu. The modelling can also simulate the situations when different intervention strategies applied. “With such simulated data, the public health authorities and decision makers can choose the most appropriate strategy to control the disease or stop its spread.”
The result of their study was published on The Lancet’s EClinialMedicine, being the first paper ever published on the journal by a research team from the city and one of the most cited articles on the journal in the past year. The study was later generalized and deepened using the data from Hong Kong. “We are using the experience that we have learned in Hong Kong, to adapt and enhance the model for some other cities and countries, such as the US, Canada, Japan and some European countries,” says Dr. Liu.
The codes and programme of the modelling are also available for people to access and download. “If there’s any researcher wants to repeat our analysis or input their own data for similar analysis, they can do that,” he says.
Computational epidemiology and infectious disease modelling for prevention and control have been Dr. Liu’s research interests. “My mother is a doctor in a mainland hospital. That’s why I’ve been always interested in health-related issues, people and stories. And my father is an engineer. Born in such a science-oriented family, I want to know why about everything. And the best way to know why is conducting scientific research,” says Dr. Liu, who was a Post-Doctoral Research Associate with the Department of Statistics at Yale University and a visiting scholar of the Robotics Institute at Carnegie Mellon University.
Since 2018, Dr. Liu has been engaged in research in malaria control and prevention in China and Southeast Asia. The research project, first started back in 2010, aims to utilise AI and machine learning technology to help low-income countries and remote areas to solve their malaria infection problems.
Tengchong county, which used to be one of the most malaria-infected areas in the China-Myanmar border, was the area that Dr. Liu worked on. “Originally, we thought that it was just another regular data mining or data feeding problem. After we visited the area, we found that one of the most critical challenges was that there were more than 200 villages in the county. From village to village, it generally takes about two hours by car. But there were only four to five CDC staff who possessed the know-how to perform complete disease diagnosis, case investigation, and epidemic treatment,” he recalls.
The AI-enabled infectious disease control and prevention developed by Dr. Liu’s team estimated the spatio-temporal mobility patterns of the people, enabling the public health authority to decide which were the key places that they should allocate their anti-malaria resources. Tengchong later successfully achieved malaria elimination and became one of the most successful examples.
Currently, Dr. Liu is working with Ministry of Health of Cambodia to apply new AI technologies to assess the malaria transmission risks in six provinces in Cambodia. “Compared to Tengchong, there are more forest in Cambodia. The patterns of human mobility as well as the receptivity of the area are totally different,” he says. “When we are building the model, we have to take that kind of spatial and temporal heterogeneity into consideration to make the model work.”
With more public health institutions intensively collaborating with different kinds of modelling groups, computer science is surely playing a more significant role in today’s public health system. “For human being, we have limited capacity to memorise all the data, but if we use the computer and design the program properly, then we will have the capacity to analyse the data for us,” says Dr. Liu. By using the historical data together with the knowledge of the domain experts, he explains, computer scientists help to make scientific grounded and accurate analysis in advance, so that interventions can be carried out in a timelier manner.
“I really hope that my research results and outputs can be implemented and deployed by other domain experts or authorities as their guidelines to solve real world problems,” says Dr. Liu. “That’s what we are doing, collaborating with the China CDC, WHO, NGOs, ministry of health in Southeast Asia and African countries. I’m trying to make my research and scientific discoveries socially impactful and useful.”