February 8, 2021
Prioritizing Allocation of COVID-19 Vaccines Based on Social Contacts Increases Vaccination Effectiveness
Category: Article Summary
Topic: Modeling and Prediction
Keywords (Tags): modeling prediction, vaccines
[Pre-print, not peer-reviewed] Using an agent-based modeling approach integrating social contact networks in Virginia, spatiotemporal surveillance data on COVID-19 cases, and models of within- and between-host disease dynamics, Chen et al. showed that vaccine allocation based on the number of an individuals’ social contacts and total social proximity time was significantly more effective at reducing the number of infections, hospitalizations, and deaths than the current age-based strategy. The model suggests that by March 31, 2021, compared to age-based allocation, the proposed degree-based strategy could reduce an additional 56–110k infections, 3.2–5.4k hospitalizations, and 700–900 deaths in Virginia, or 3–6 million fewer infections, 181–306k fewer hospitalizations, and 51–62k fewer for the entire US. The strategy was robust even if social contacts were not estimated correctly, vaccine efficacy was lower than expected, or only a single dose was given, or if there was a delay in vaccine production and deployment.
Chen et al. (Feb 6, 2021). Prioritizing Allocation of COVID-19 Vaccines Based on Social Contacts Increases Vaccination Effectiveness. Pre-print downloaded Feb 8 from https://doi.org/10.1101/2021.02.04.21251012