Table 5_Reducing rural health inequity through drone-based delivery: a spatial multi-criteria decision framework prioritizing epidemiological vulnerability in hub location.xls

All results are derived from a calibrated simulation environment and should be interpreted as model-based projections rather than empirical observations.</p>Results<p>Simulation results indicated that the “Digital Inverse Care Law” emerged naturally under traditional logistics models, which allocated 85% of hubs to low-vulnerability urban centers. In contrast, the EVI-driven framework strategically anchored 65% of hubs in deep rural pockets.

Under the baseline parameter assumptions, this reallocation of UAV hubs reduced the simulated average rural emergency response time by 74.8% (from 45.2 to 11.4 min) in the model. The 11.4-min figure represents UAV delivery time to the incident location, not complete emergency intervention time. This modeled improvement brought an estimated 88.4% of the highly vulnerable aging population into the “Golden 15-Minute” survival window within the simulation.</p>Conclusion<p>By embedding epidemiological metrics into spatial planning, this framework provides a decision-support tool that is traceable and reproducible for health policymakers, based on model outputs rather than empirical field validation.

It illustrates how targeted digital health infrastructure deployment could, under the model assumptions, contribute to narrowing the rural–urban emergency health divide. These findings are contingent on the specific model assumptions and require field validation and formal health-economic evaluation before real-world implementation.</p>