IRIS – Imaging-based Risk Identification System
IRIS
IRIS is an integrated technological platform designed to revolutionize epidemiological surveillance. The project’s core is a mobile application that acts as a vital bridge between citizens and public health authorities. Through the app, users report potential breeding sites by submitting photos and location data. This crowd-sourced intelligence feeds into a dashboard for health agents, enabling targeted, preventive interventions. Our roadmap includes integrating Computer Vision and IoT sensors to automate detection. By replacing manual monitoring with a digital ecosystem, IRIS optimizes resources, reduces reliance on chemical insecticides, and empowers communities to prevent outbreaks before they start.
Technical Elements
Entrepreneurship
Regional Pitch Events
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Learning Journey
Learning Journey
At first, our team struggled with React and technical issues using Expo Go, which slowed our progress. To overcome this, we pivoted to Expo Snack for better visualization, subdivided tasks, and prioritized the app’s core functions to meet our deadline. This Technovation Girls journey provided us with deep insights into research, programming, and debugging. Beyond technical skills, we gained invaluable experience in teamwork, organization, and project management. We transformed initial challenges into a structured learning process, successfully building a functional prototype while developing the professional mindset needed to solve complex problems through collaboration.
Information Legitimacy Description
To ensure the project's reliability, our team cross-referenced data from official public health sources, such as the Brazilian Ministry of Health and Fiocruz, specifically regarding epidemiological cycles and mosquito behavior. Technical feasibility was verified through academic papers on Computer Vision and Smart Cities to ensure the AI's accuracy. This multi-layered research approach, combining official statistics, scientific literature, and direct user feedback, ensures that IRIS is grounded in credible, evidence-based data for both its technology and public health strategy.
Bibliography
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Since the project involves images in public spaces, we ensure that the AI is going to be trained exclusively to identify objects (water and waste) and not people. We have implemented anonymization and encryption protocols so that no sensitive data from users or citizens is exposed. In addition, the system was designed to avoid “surveillance bias,” prioritizing areas historically neglected by public authorities. The goal is to ensure that technology promotes health equity, rather than serving only central neighborhoods. Within the app, users have full transparency about how their data is used. Moreover, we actively combat the excessive use of chemicals, protecting biodiversity and ensuring that technological intervention is always proportional and safe for the community.
