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.
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…
Allocation optimization of patients during covid pandemic using real data from Brazil 2020-2022 period.
Outcomes Framework rebuild for 2025/26 READ THE DOCUMENTATION:
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