Mobility, demographics, and pandemic spread: a framework for targeted policy interventions

Official URL: https://dx.doi.org/10.1016/j.hlpt.2026.101241

Background: Pandemics force governments to make rapid, high-stakes decisions on mobility restrictions and resource allocation, often under severe uncertainty. Conventional one-size-fits-all mobility policies overlook demographic heterogeneity, which leads to uneven effectiveness and significant economic costs. Objective: This study develops an integrated analytical framework for pandemic policy that combines epidemiological spread-speed estimation with socio-demographic mediation analysis to inform targeted interventions. Methods: Using U.S. county-level data, we estimate weekly spread speed via the Susceptible-Infectious-Recovered (SIR) model and combine this with mobility data from Google and Apple and demographic indicators from the American Community Survey. Structural Equation Modeling (SEM) is employed to test how demographic constructs (deprivation, non-car mobility, homogeneity, and work-alone behaviors) mediate the mobility–spread relationship. Results: Our findings indicate that the association between mobility and transmission is largely captured through demographic constructs in the SEM, with the direct mobility–spread association attenuating after deprivation, non-car mobility, homogeneity, and work-alone behavior are incorporated. These patterns suggest that mobility restrictions alone may be less effective unless they are aligned with local demographic conditions. Conclusions: These results highlight the need for demography-aware policies, such as remote work incentives, transit de-densification, and targeted support for disadvantaged populations. Our model enables policymakers to design region-specific strategies that balance epidemiological impact with economic sustainability by collapsing complex demographic variables into actionable constructs. This research advances pandemic governance by moving beyond descriptive dashboards to decision-ready insights to offer a scalable framework for tailoring interventions that are both effective and equitable. Public interest abstract: Governments often implement uniform mobility restrictions during pandemics, assuming that reducing movement alone curbs transmission. However, prior research shows that demographic factors such as poverty, household structure, and reliance on public transit significantly influence compliance and disease spread. This study introduces a generalizable framework that integrates epidemiological modeling with socio-demographic mediation analysis. Using U.S. county-level data, we show that the relationship between mobility and transmission is strongly shaped by demographic constructs such as deprivation, reliance on non-car mobility, homogeneity, and work-alone behavior. This suggests that policies should move beyond blanket mobility restrictions toward demography-aware interventions. Strategies such as remote work incentives, transit de-densification, and targeted support for disadvantaged populations can improve effectiveness and equity while reducing economic costs.