AIdeas: Predict-Epidem
Predict-Epidem is an epidemiological intelligence system designed to monitor and predict disease outbreaks in Latin America, with initial focus on Mexico. The system provides real-time data visualization, risk prediction, and alert capabilities through a futuristic web-based dashboard interface. The system integrates climate data, clinical reports, and social media signals to identify disease outbreak patterns and high-risk zones.
Team Members: @luciomsp & @fernandosilvot
App Category
Social Impact
Social Impact
Our Vision
Predict-Epidem: Weeks of advantage, lives of difference
Latin America is experiencing an unprecedented epidemiological crisis. In 2023 alone, Mexico reported more than 54,000 cases of dengue, a 230% increase compared to previous years, and the upward trend continued in 2024 with the resurgence of the DENV-3 serotype, which had not predominated for years, causing massive hospitalizations in critical states such as Veracruz (where 4 serotypes circulate) and Guerrero. This scenario reveals a structural flaw: the major gap in public health is not the lack of data, but the inability to act in time. Currently, hospitals operate under a reactive model, only mobilizing when the wards are already full and contagion is exponential. As the literature in The Lancet Digital Health points out, the real obstacle is not Artificial Intelligence, but rather that our systems were designed to archive past records and not to feed models that predict the future in real time.
Predict-Epidem was created to break this inertia, transforming crisis management into a precision strategy through a deep learning architecture on AWS. By analyzing four decades of epidemiological data from SINAVE (Sistema Nacional de Vigilancia Epidemiológica de México, 1984-2025), our model cross-references this information with climate patterns, biological cycles, and, crucially, social media signals. These signals act as an immediate response sensor, detecting mentions of symptoms and outbreaks in specific communities long before patients arrive at an official clinic.
This multivariate integration allows us to identify early signals invisible to traditional analyses, projecting outbreaks with a window of 2 to 4 weeks' notice. We are aware of the technical challenges: AI must learn from fragmented data due to underreporting in homes and private clinics. However, by converting this digital and clinical "noise" into predictive intelligence, we enable the healthcare system to move from collapse to proactive logistics, ensuring timely access to medications, personnel, and ICU beds. Our vision begins in Mexico, but it aims at the entire region: we want to ensure that no vulnerable community in Latin America is ever again taken by surprise by a predictable epidemic.
Why this matters
Mexico and Latin America face a cyclical threat from arboviruses such as dengue, Zika, chikungunya, and the emerging Oropouche virus (the first significant outbreak in the region in 2024), among others. In regions with high climate vulnerability, these outbreaks are not only a medical challenge but also a driver of systemic collapse. Currently, the health response is predominantly reactive: interventions are mobilized only when cases are already exponential and hospitals are operating under unsustainable pressure. This saturation paralyzes the entire hospital, forcing the postponement of scheduled surgeries, overwhelming obstetrics wards, and depleting critical supplies for unrelated emergencies.
Predict-Epidem breaks this cycle through a dual-temporal approach that responds to the real needs of public administration. First, it enables annual strategic budget planning by identifying high-risk years based on long-term climate cycles, facilitating year-over-year resource allocation. Second, it triggers an operational alert two to four weeks in advance—the critical window needed for institutions to implement preventative measures before an outbreak reaches its peak.
By anticipating risk weeks ahead, our solution reduces hospital overcrowding and minimizes costly emergency responses, closing preparedness gaps that have historically cost lives. Scientific evidence supports this view: a global systematic review published in BMC Public Health confirms that the use of Early Warning Systems (EWS) is highly effective for the timely detection of outbreaks, strengthening the resilience and responsiveness of healthcare systems by 15% to 25% (Meckawy et al., 2022).
This solution goes beyond data analysis; it leverages the scalability of cloud technology to build a pillar of resilience at the heart of the healthcare system. By processing and converting the "noise" of fragmented data, weather variables, and real-time social media signals into high-fidelity predictive intelligence, Predict-Epidem ensures the continuity of essential medical services. Our proposal guarantees that hospital infrastructure not only survives epidemiological peaks but also maintains its full operational capacity, protecting both outbreak patients and those who depend on uninterrupted critical care. Ultimately, we transform data-driven prevention into a safeguard for regional health stability.
How We Built This
The construction of Predict-Epidem was based on a fundamental principle: technology must adapt to the urgency of public health. We implemented a 100% serverless, event-driven architecture on AWS, designed to scale instantly during outbreak peaks without the operational burden of managing servers.
The Tech Stack
| Layer | Tool | Critical Function |
|---|---|---|
| Core & AI IDE | Kiro AI IDE | Development orchestration, data pre-processing assistance, and medical context management. |
| Machine Learning | Amazon SageMaker Canvas | Time-series prediction with high-fidelity algorithms and automatic training. |
| Compute & Logic | AWS Lambda | Serverless processing, data normalization, and alert trigger execution. |
| Data Lake | Amazon S3 | Central repository for historical epidemiological and climate data (2020–2025). |
| Automation | Amazon EventBridge | Daily automation of climate data ingestion and event coordination. |
| Visualization | Amazon QuickSight | Interactive geographic risk dashboards and BI reports for decision-making. |
| Communication | Amazon SNS | Automated SMS alerts to medical personnel in areas with low connectivity. |
Critical Architecture Decisions
Why not use a relational database (RDS)?
- S3 + Parquet offers better cost-efficiency for time series analysis.
- Compliance with Free Tier (RDS consumes credits quickly).
Why SageMaker Canvas over Custom Models?
- Time-to-market: Canvas reduces development by 3 weeks.
- Pre-optimized algorithms (DeepAR+ & Prophet) with WAPE <0.15. No-code allows scalability without dedicated DevOps.
Architecture Diagram
Ingestion and Processing: Data comes from diverse sources (databases, APIs, or files) and is processed by AWS Lambda functions, which normalize and transform the information.
Storage and Notifications: Processed data is stored in Amazon S3 as the central data lake. Simultaneously, Lambda sends events to Amazon SNS for automatic notifications and alerts.
Predictive Analytics (No-Code ML): Amazon SageMaker Canvas connects to S3 to generate no-code machine learning models and deliver business predictions.
Business Intelligence and Visualization: Amazon QuickSight uses data stored in S3 to generate interactive dashboards and detailed visualization reports.
Methodology: Spec-Driven Development with Kiro AI
To accelerate development and ensure technical consistency, we used the Kiro AI IDE. This approach allowed us to prioritize architecture over code:
- Spec Definition: We structured the project using detailed specifications (Data Pipeline, ML Pipeline, and Alert System). Kiro transformed these business rules into robust AWS Lambda functions with integrated error handling and preprocessing logic.
- Continuous Epidemiological Context: By using Steering Files, we ensured that Kiro maintained context regarding dengue's biological cycles and climate seasonality. This was crucial for the AI to generate the data cleaning code (report parsing and normalization) necessary to feed the machine learning model.
Predictive Intelligence: Precision over Intuition
During the design phase, we selected Amazon SageMaker Canvas as the project's predictive core, discarding traditional architectures in favor of a high-speed, visual machine learning approach. Given the maturity of AWS solutions in 2026, Canvas emerged as the logical choice due to its ability to efficiently process complex time series.
During the design phase, we selected Amazon SageMaker Canvas as the project's predictive core, discarding traditional architectures in favor of a high-speed, visual machine learning approach. Given the maturity of AWS solutions in 2026, Canvas emerged as the logical choice due to its ability to efficiently process complex time series.
- High-Precision Modeling: The system automatically evaluated and selected the most robust algorithms (DeepAR+ and Prophet), integrating 40 years of SINAVE data and climate variables in less than 3 hours of training.
- Technical Validation: We obtained a WAPE of 0.12 and a MAPE of 0.15, metrics that validate our ability to project epidemiological peaks 4 weeks in advance, allowing the results to be visible in real time using Amazon QuickSight.
Why is this approach disruptive?
By combining the speed of Kiro AI with the power of SageMaker Canvas, we've created a system that is not only technically advanced but also economically viable. The architecture ensures that resources reach the highest-risk areas weeks before the healthcare system reaches its operational limit, transforming data-driven prevention into a reality for Latin America.
The result is a robust platform, designed for medical trust and real-world usability, where every technical decision is supported by a functional specification.
DEMO
You can explore a preview of our platform's interface here:
Feature Highlights
- User-Centric UX: Light and Dark Modes
- Support for Spanish and English
ScreenShots
What we have Learned
The development of Predict-Epidem provided us with critical lessons on how cloud architecture must adapt to the complexity of public health:
Technical Lessons and Data Engineering
Unlike traditional software projects, the challenge here was not only the code, but also the preparation and quality of the data.
Kiro as a Data Architect: We learned that the value of Kiro Steering Files was crucial in the pre-processing phase. Kiro allowed us to code epidemiological business rules—such as calculating moving windows, handling seasonality, and normalizing incidence rates—during the creation of the training and validation datasets. Without this context injected into the Lambda functions, the raw data would have lacked the necessary structure for SageMaker Canvas to detect significant geographic patterns.
SageMaker Canvas Effectiveness in 2026: We confirmed that for high-fidelity prototyping, SageMaker Canvas completed training on 40 years of data in 2.8 hours, versus the 15-20 hours estimated with a custom approach in EC2, saving approximately $45 in compute costs. By delegating algorithm selection, we were able to focus our efforts on feature engineering (identifying that relative humidity and minimum temperature are more powerful predictors than rainfall itself).
Cloud Viability: We validated that a 100% serverless architecture allows us to operate a nationally accessible MVP under the AWS Free Tier, eliminating the economic barrier to innovation in public health.
Epidemiological Domain and Real-World Challenges
The Critical Lead Time: We confirmed that climate variables offer a prediction window of 4 to 8 weeks. This learning is vital, as a hospital requires a minimum margin of 14 days to reconfigure its logistics of beds and critical supplies.
Data Ingestion Challenges: The biggest technical obstacle is the nature of the public data; many historical SINAVE reports still reside in unstructured formats (PDF), requiring robust parsing processes to avoid bias in the model. We also learned to manage the limitations of mass communication, optimizing the use of Amazon SNS to send alerts only when risk thresholds exceed 80%.
Roadmap: Next 6–12 Months
Phase 1 (Q2 2026): Field Validation
- Pilot with 3 hospitals in Veracruz
- Integration with local HIS (Hospital Information System) systems
Phase 2 (Q3 2026): Signal Expansion
- Google Trends API for early detection
- Integration with X for self-reported symptom analysis
Phase 3 (Q4 2026): Multi-Pathology
- Extension to Zika, Chikungunya, and Oropouche
- Unified model of arboviruses transmitted by Aedes aegypti
Phase 4 (2027): Regional Scale-Up
- Expansion to Colombia, Chile, and Argentina
- Collaboration with OPS/OMS for data standardization
Try It Yourself
Live Platform: Predict-Epidem Web
References
Meckawy, R., Stuckler, D., Mehta, A., Al-Ahdal, T., & Doebbeling, B. N. (2022). Effectiveness of early warning systems in the detection of infectious diseases outbreaks: a systematic review. BMC public health, 22(1), 2216.
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