Allocation optimization of patients during covid pandemic using real data from Brazil 2020-2022 period.

Multi-source public health intelligence system for epidemiological surveillance and early warning

To explore and evaluate the application of crowdsourcing, in general, and AMT, in specific, for developing digital public health surveillance systems, we collected 296,166 crowd-generated labels for 98,722 tweets, labelled by 610 AMT workers, to develop machine learning (ML) models for detecting behaviours related to physical activity, sedentary…

This project provides an automated data pipeline for extracting, transforming, and analysing health data from a DHIS2 instance. It includes querying various health indicators at the facility level, enriching data with organisational details (districts and provinces), exporting results to structured CSV and loading to PostgreSQL.

Este proyecto explora datos de accidentes de tránsito en República Dominicana, con un enfoque en temporalidad mes y año y distribución por sexo. El objetivo es identificar patrones críticos y anomalías que puedan orientar políticas públicas y campañas de prevención.

🚨 Detect disease outbreaks in real-time with the Argus Platform, a public health intelligence system integrating clinical, social, and environmental data.

A cost-sensitive gradient boosting framework for defining performance bounds in multi-label cardiovascular risk prediction. Designed to extract state-of-the-art diagnosis and prognosis from 12-hour clinical data.

Real-time dengue clinical intelligence dashboard — severity×risk analysis, patient monitoring, and data quality tracking.

Outcomes Framework rebuild for 2025/26 READ THE DOCUMENTATION:

ATLAS is a modular, deployable system for multi-domain time-series intelligence that evaluates data stability, performs conditional forecasting, detects anomalous behavior, and supports uncertainty-aware scenario analysis.

Research component focused on identifying dengue breeding points using UAV imagery and YOLOv8 deep learning models.