Authors: Pranav Dhondibhau Gawade, Sarthak Vivek Sagare, Sujan Anna Kambale, Piyush Vinod Chaudhary, Neelam N Kavale
Abstract: The proposed Smart Community Health Monitoring and Early Warning System for rural areas offers a transformative, cost-effective alternative to expensive, sensor-dependent technologies by prioritizing syndromic surveillance and community-led data collection. Recognizing that traditional IoT infrastructure often fails in remote regions due to high maintenance costs and power instability, this model empowers community health workers to act as "human sensors," manually reporting clinical symptoms like fever and diarrhea via an offline-capable mobile interface. By integrating these health reports with periodic, low-cost chemical water testing, the system utilizes a centralized analytical engine to run statistical aberration detection algorithms that compare real-time trends against historical baselines. This proactive framework identifies potential pathogenic outbreaks at their nascent stage, triggering a tiered Early Warning System (EWS) that alerts local authorities through automated SMS and voice calls. Ultimately, this research demonstrates that public health resilience is not solely dependent on high-tech hardware but can be achieved through strategic data management, community participation, and smart analytics, providing a scalable and sustainable blueprint for disease prevention in resource-constrained environments globally.
