Expand Campus Reopen to a School System by Considering Population Density and Human Dynamics


  • YEEFAY LI Aspiring Scientists' Summer Internship Program Intern
  • Chaowei Yang Aspiring Scientists' Summer Internship Program Mentor
  • Hai Lan Aspiring Scientists' Summer Internship Program Mentor
  • Yen Bui Aspiring Scientists' Summer Internship Program Co-mentor
  • Daler Khamidov Aspiring Scientists' Summer Internship Program Co-mentor




With the return to in-person school in the fall, one of the most important concerns is how COVID-19 may spread within schools operating at full capacity. A main factor to consider in modeling the spread trajectory of COVID-19 is the movement and activity of students. To this end, we sought to simulate the movement of students on and off campus in order to model the spread of COVID-19 within Fairfax County Public Schools. Using ArcMap, we modeled the distance between homes and schools in Fairfax County, and considered walkability, how likely a student will walk/bike to school, and population density to analyze the student dynamics within a school pyramid. We then wrote Python code to collect road traffic data in order to model student movement outside of school, which is useful in modeling student movement on the weekends. We have so far collected a week's worth of speed data at road intersections throughout Fairfax County from Here Traffic API. We were able to incorporate insights from reviewed studies to determine factors in deciding methods of student commute. Future steps include using the collected data to model student population dynamics within certain areas of Fairfax County. This study will help aid Fairfax County School Health Services to make decisions in mitigating virus hotspots and map out possible virus spread within a campus community. In addition, this prediction model will build safety precautions based on historical and current data and aid health services in assessing existing COVID-19 control strategies.





College of Science: Department of Environmental Science and Policy