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Autori principali: Pant, Piyush, Suntoro, Marcellius William, Siddiqua, Ayesha, Sharif, Muhammad Shehryaar, Ahmed, Daniyal
Natura: Preprint
Pubblicazione: 2025
Soggetti:
Accesso online:https://arxiv.org/abs/2510.10640
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author Pant, Piyush
Suntoro, Marcellius William
Siddiqua, Ayesha
Sharif, Muhammad Shehryaar
Ahmed, Daniyal
author_facet Pant, Piyush
Suntoro, Marcellius William
Siddiqua, Ayesha
Sharif, Muhammad Shehryaar
Ahmed, Daniyal
contents This paper presents EA-GeoAI, an integrated framework for demand forecasting and equitable hospital planning in Germany through 2030. We combine district-level demographic shifts, aging population density, and infrastructure balances into a unified Equity Index. An interpretable Agentic AI optimizer then allocates beds and identifies new facility sites to minimize unmet need under budget and travel-time constraints. This approach bridges GeoAI, long-term forecasting, and equity measurement to deliver actionable recommendations for policymakers.
format Preprint
id arxiv_https___arxiv_org_abs_2510_10640
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Equity-Aware Geospatial AI for Forecasting Demand-Driven Hospital Locations in Germany
Pant, Piyush
Suntoro, Marcellius William
Siddiqua, Ayesha
Sharif, Muhammad Shehryaar
Ahmed, Daniyal
Artificial Intelligence
This paper presents EA-GeoAI, an integrated framework for demand forecasting and equitable hospital planning in Germany through 2030. We combine district-level demographic shifts, aging population density, and infrastructure balances into a unified Equity Index. An interpretable Agentic AI optimizer then allocates beds and identifies new facility sites to minimize unmet need under budget and travel-time constraints. This approach bridges GeoAI, long-term forecasting, and equity measurement to deliver actionable recommendations for policymakers.
title Equity-Aware Geospatial AI for Forecasting Demand-Driven Hospital Locations in Germany
topic Artificial Intelligence
url https://arxiv.org/abs/2510.10640