Implications of COVID-19 vaccination heterogeneity in mobility networks

Abstract

Our study utilizes network science to examine how uneven vaccine distribution affects mass vaccination strategies in the United States. Using mobility network data and epidemiological models, we find that distributing a fixed quantity of additional vaccines across Census Block Groups (CBGs) can vary case count reductions by up to 200%. This highlights the impact of vaccination heterogeneity in mobility networks on epidemic outcomes. Our efficient algorithm identifies optimal vaccine distribution for maximum case reduction. Simulations show a possible 9.5% decrease in case numbers with just a 1% increase in the national vaccination rate if vaccines are optimally distributed. This result surpasses those from other vaccine distribution models. Our findings underline the need for policymakers to understand the interaction between vaccination patterns and mobility networks, suggesting that grasping geographical vaccine uptake variations could be as crucial as raising the overall vaccination rate.