Effectiveness of Nonpharmaceutical Interventions to Avert the Second COVID-19 Surge in Los Angeles County: A Simulation Study
Research Team: Caroline Rodier (lead), Abigail Horn, Yunwan Zhang, Ihab Kaddoura, Sebastian Müller
UC Campus(es): UC Davis
Problem Statement: Nonpharmaceutical interventions (NPIs) could be implemented early in a COVID-19 surge to avoid a large wave of infections, deaths, and an overwhelmed hospital system. Models are a useful approach to gauge the effects of NPIs by simulating their implementation. 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 examines the effectiveness of nonpharmaceutical interventions (NPIs) early in a COVID-19 surge by simulating their implementation. To do this, the research team integrated a dynamic agent-based travel model with an infection dynamic model. Both models were developed with and calibrated to local data from Los Angeles County (LAC), resulting in a synthetic population of 10 million agents with detailed socio-economic and activity-based characteristics representative of the County’s population. The study focused on the time of the second wave of COVID-19 in LAC (November 1, 2020, to February 10, 2021), before vaccines were introduced. The model accounted for mandated and self-imposed interventions at the time, by incorporating mobile device data providing observed reductions in activity patterns from pre-pandemic norm, and it represented multiple employment categories with literature-informed contact distributions. The combination of NPIs—such as masks, antigen testing, and reduced contact intensity—were the most effective, among the least restrictive, means to reduce infections. The findings may be relevant to public health policy interventions in the community and at the workplace. The study demonstrates that investments in activity-based travel models, including detailed individual-level socio-demographic characteristics and activity behaviors, can facilitate the evaluation of NPIs to reduce infectious disease epidemics, including COVID-19. The framework developed is generalizable across SARS-COV-2 variants, or even other viral infections, with minimal modifications to the modeling infrastructure.
Status: Completed
Budget: $101,377