A0981
Title: Seeding efficient large-scale public health interventions in diverse spatial-social networks
Authors: Xiaoyi Han - Xiamen University (China) [presenting]
Yilan Xu - University of Illinois at Urbana-Champaign (United States)
Yi Huang - Nanjing Audit University (China)
Linlin Fan - Penn State University (United States)
Minhong Xu - New York University (United States)
Song Gao - University of Wisconsin Madison (United States)
Abstract: The selection of target locations for large-scale public health interventions is complex when the take-up of such interventions has peer effects through social networks, and health outcomes have spillover effects through spatial networks. A threshold spatial dynamic panel data (TSDPD) model with endogenized human-virus interactions is developed to address this issue. The model is used to study the initial COVID-19 vaccination rollout in the United States from February 5 to April 15, 2021. Strong peer effects in state vaccination rates arising from the friendship network and strong COVID-19 virus transmission through the travel network are found. Vaccination decreased infections mainly through reduced transmissibility upon passing a full vaccination rate threshold, whereas its impact on the travel network slightly increased infections. Cumulative infections would have been 1.17 million or 24.85\% higher if vaccines were not available. Targeting the six most populated US states or most spatially connected is the most clinically effective in reducing cumulative infections by more than 343,000, and targeting the six most socially influential states is the most clinically effective in increasing the national vaccination rate by 3.5 ppts to 18.54\%. Targeting the six most socially influential states is more than 20 times as cost-effective as targeting the most populated or most spatially connected ones.
