Coronavirus disease (Covid-19) is known as an infectious disease that has a bigger impact on urban areas with high-density populations. In New York City, however, there are huge differences in the impacts of Covid-19 at the neighborhood level. In addition, there is a negative correlation between the Covid Case Rate and the Vaccination Rate, and areas that are affected the most have poor socio-economic conditions. To better address the equitable distribution of Covid-19 vaccines, I collected social and economic data from Community Health Profiles as well as Covid-19 data from NYC Health. Using this data I created visualizations in Tableau to identify the key factors that contribute to the disproportionate impacts of Covid-19 on different neighborhoods. From the key factors, I developed a Binary Integer Programming model in Excel to determine the optimal locations for Community-based Pop-up Vaccination Centers while minimizing costs. The results support that these vaccine sites can serve the underrepresented neighborhoods.
Wang, Zhennan, "Equitable Distribution of Covid-19 Vaccines: Can Data Visualization and Optimization Help?" (2021). Honors College Theses. 328.