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COVID-19 Vaccination Rollout

This project was developed by Toby Lee as a tutoring initiative from Data XP.

The scenario for the project was as follows: a fictional company, QuickJab, was involved in the distributions of vaccines worldwide, but had leeway in selecting the new contries to roll-out. In a way to optimize this selection, the company hires a team of analysts to process the data available and suggest what country would be most cost effective for the next roll-out.

The "cost-effectiviness" in itself was an open parameter to be designed by the analyst team, and must incorporate the data submited by Quickjab. At the end of the 4 week period, the teams would present their findings in a presentation.

Initially, my team, comprised of 5 people, develop a tracker tool for controling the time spent on the project as well as for billing purposes. The company data was explored for correlations and cleaned up throught SQL. Addicional data from the web was inserted, namely HDI, GDP, population and the logistics performance index (LPI) for each country. As a team, we develop a machine learning model to predict the daily vaccination rates for new countries. All this data was combined into a score for each nation. After a sensitivite analysis, the final recommendations were presented to the company.