COVID-19 Risk Analysis in Metallurgical Plants
When COVID-19 first struck, the occupational medicine team at a large metallurgical company realized their Excel tracking system would not be able to keep up. To assist them, I collaborated with the chief doctor to define the necessary data and how we would process their data. Using Python, I built a data cleaning pipeline to fix errors in their private dataset of symptomatic employees and automated the daily retrieval and processing of public COVID-19 data from the Brasil.io API, storing it in their database with Airflow handling the scheduling.
Working closely with the medical team, we developed a risk factor that combined variables with factory and city-level COVID-19 statistics such as moving averages of new cases. This risk factor guided their decisions on allocating testing kits, distributing safety equipment, and managing factory operations. I created a dashboard using Tableau that merged both private and public data, giving the medical team real-time insights into COVID-19 rates across all factories. They could compare company data with city case rates, assess testing efforts, and view historical trends—all in one place. The feedback from the medical team was overwhelmingly positive, with the dashboard becoming an essential tool in their daily operations.