Implications of COVID-19 vaccination heterogeneity in mobility networks.

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. The inhomogeneity of vaccine uptake has many roots from social, political to economic and analysing the consequent impact on virus proliferation is complex. Here, using epidemiological models based on census block group level mobility data the authors demonstrate that a targeted increase of vaccinations for critical locations results in a greater reduction in COVID-19 cases. [ABSTRACT FROM AUTHOR]