Project 2 examines the effect of movement-based restrictions on mobility in Nigeria and South Africa, comparing pre-pandemic and post-pandemic patterns in cell phone mobility data and connecting specific movement patterns with COVID-19 risk. This model incorporates state-of-the-art mobility analytics from the transportation sector, applying them to the African context, possibly for the first time.
Chenfeng Xiong is an assistant Professor at the Department of Civil and Environmental Engineering, Villanova University. He holds a Bachelor’s degree in Civil Engineering from Tsinghua University, and an M.S degree in Civil Engineering, an M.A in Economics and a Ph.D in Transportation Engineering, from the University of Maryland. Chenfeng’s areas of interest include, transportation big data, human mobility and travel behavior modeling, transportation economics, carbon neutralization, large-scale agent-based analysis modeling and simulation (AMS). As a researcher, Chenfeng has published over 80 peer reviewed articles.
The Project 2 Research Team employed mobile device location data and developed in-house big-data and data-driven algorithms to analyse human mobility, activities, and population density in South Africa and Nigeria during the SARS-CoV-2 pandemic. The team’s cloud-based big-data computing infrastructure ingests terabytes of data daily and produces those afore-mentioned high-resolution human mobility measurements.
Density Plot of 14 Days of Human Activities in South Africa
The mobility measurements produced by the team are new and have filled a major data gap in understanding the travel behaviour change during the SARS-CoV-2 in Nigeria and South Africa. The team assessed the effectiveness of mobility restricting policies as key lessons learned from the pandemic and found that travel bans, and federal lockdown policies failed to restrict trip-making behaviour but had a significant impact on distance travelled.
Daily trips per person and daily distance travelled per person measured using smartphone location data collected in Nigeria (January 01, 2020 – April 25, 2020).
As the next step, the team will work on developing integrated agent-based models to explain and predict human interactions and its indications to disease transmission dynamics. High-resolution measurements of social distancing, transmissions process will be modelled, integrated, and validated for a variety of public-health applications, such as policy implications and identification of vulnerable communities.
In addition, high-resolution human mobility analytics are being linked with climate change events, such as extreme weather records, flooding data, high/low temperature, rainfalls, and humidity information. The below figure illustrates our data integration effort, where the precipitation data represents the fifth generation European Centre for Medium-Range Weather Forecasts (ECMWF) atmospheric reanalysis of the global climate. Such data integration allows us to explore the relationship between the mobility in the flooding area and various climate factors, and potentially pinpoint evidences on how climatic events may exacerbate impacts of the pandemic.