Effects of individual variation and seasonal vaccination on disease risks

Estimates of the risk of a large infectious disease outbreak resulting from the introduction of a pathogen into a population are valuable for planning public health measures. Two key factors affecting outbreak risk estimates are variations in transmission between individuals (e.g., “superspreaders”) (1) and over time (e.g., through seasonality (2,3) or changing population immunity, such as through vaccination (4)). However, existing outbreak risk estimation frameworks do not consider both features simultaneously. Here we develop an approach for estimating outbreak risks that accounts for both individual heterogeneity and time dependence. To demonstrate the real-world application of our framework, we consider the design of annual COVID-19 booster vaccination campaigns, using a multi-scale approach that incorporates an individual-level model of vaccine-induced antibody dynamics (5–7). Our results indicate that a high outbreak risk is possible near the start of annual vaccine distribution, when population immunity is low, which can be mitigated by distributing vaccines over a longer period. A longer distribution period is particularly beneficial in scenarios with high vaccine coverage and/or effectiveness, and if seasonality in transmission is limited. These insights are applicable to routine vaccination campaigns against COVID-19 and other diseases, particularly in vulnerable populations in which outbreak prevention is critical.