A Gis-Based Mobile Application for Real-Time Disease Outbreak Monitoring and Prediction in Zambia

Abstract

Timely and effective response to disease outbreaks remains a persistent challenge, especially in resource-constrained environments such as Zambia. This study presents the development of a GIS-based mobile application that combines Geographic Information Systems (GIS) with artificial intelligence (AI) to enhance real-time outbreak monitoring and decision-making. The system integrates geospatial visualization with AI-driven analytics to identify potential hotspots, predict disease propagation trends, and generate actionable recommendations for public health stakeholders. Built using a full-stack architecture, the application leverages Flutter for the frontend, Firebase for backend services and cloud database, and the DeepSeek & Gemini API for AI-powered qualitative insights. While existing solutions in countries like India and China have shown the potential of merging AI with geospatial analysis for epidemic tracking and agricultural pest management [1], such integration is limited in Zambia’s public health landscape. This research addresses that gap through a scalable, mobile-centric solution designed specifically for localized deployment. A qualitative evaluation of the prototype indicates strong potential for improving epidemiological surveillance and informing data-driven interventions in Zambia and similar low-income settings.