Combining Travel and Public Health Data to Examine Impacts of COVID-19 Shelter-in-Place Orders in Los Angeles County

Research Team: Caroline Rodier (lead) and Huajun Chai, UC Davis; Kay Nagel and Ihab Kaddoura, Technical University of Berlin; David V. Conte and Abigail Horn, University of Southern California; and Neil Maizlish, Public Health Institute

University: UC Davis, Technical University of Berlin, University of Southern California

Problem Statement: California is gradually beginning to relax shelter-in-place restrictions in response to the COVID-19 pandemic. However, little is known about the impacts of doing so. Models are a useful approach to gauge the effects of modifying shelter-in-place policies. Public health models of virus transmission focus on the nuances of viral infection dynamics; however, they treat the social activity over time and space that influence contact frequency, duration, and type in a highly simplified fashion. In contrast, agent-based travel models, used for transportation planning, focus on describing the activities of individuals in the context of family, employment, and the built environment.

Project Description: This study will use a recently calibrated open-source Multi-Agent Transport Simulation travel model for LA County (LA MATSim model) in combination with a COVID-19 virus infection dynamics (or CVID) model to test the effectiveness of non-pharmaceutical interventions (NPIs) to control the spread of COVID-19. The LA MATSim model capitalizes on the extensive financial investments in travel activity surveys, socio-economic data, and travel models developed by the State of California and by the Southern California Association of Governments (SCAG). TU Berlin researchers recently developed the CVID model for use with the MATSim model in Berlin. The CVID model will be revised and calibrated using local data and parameters by researchers at the USC’s Keck School of Medicine. USC researchers will also improve and locally calibrate their Predictive Epidemic Model for COVID-19 to help understand when epidemic peaks will occur, how spikes impact health care capacity, and effects on at-risk groups. The researchers will use these models to rapidly simulate new interventions to help separate NPI strategies that are effective from those that are not.

Status: In Progress

Budget: $101,377

Project Partner(s): Public Health Institute