Precision implementation guided by simulation derived clusters of regional immunity

The COVID-19 pandemic revealed the need for context-sensitive public health strategies reflecting demographic and behavioral heterogeneity across communities. We developed a high-resolution, individual-based microsimulation to model population-level immunity in the New River Valley (NRV), a socio-demographically diverse region in Southwest Virginia marked by aging rural populations with elevated comorbidity burdens and university communities under early vaccination mandates. Our model integrated empirical infection and vaccination data across 27 ZIP codes, parameterizing immune responses using real-world vaccine effectiveness estimates. Weekly immunity dynamics over 103 weeks were analyzed using dynamic time warping and hierarchical clustering to uncover structurally coherent patterns. Results revealed that immunity progression was highly heterogeneous and shaped by structural vulnerability, behavioral variation, and institutional policy. Areas with strong early mandates exhibited rapid vaccine-induced immunity and lower infection rates, diverging significantly from surrounding rural areas that depended more heavily on natural infection. Correlation and clustering analyses identified persistent disparities tied to socioeconomic and comorbidity profiles and revealed key intervention time windows. These findings demonstrate that “one-size-fits-all” approaches are inadequate, supporting the need for “precision implementation strategies” informed by cluster-based immunological behavior to address localized immunity gaps. This framework offers a robust decision-support tool for adaptive, equity-focused epidemic response.