Going into the project I thought it was going to be pretty straightforward. Do a lit review, compare campaigns, and map the effectiveness of each distribution. However, I very quickly learned that it was a lot more complicated than that. First of all, during the lit review I realized that there was not a lot about individual campaigns. It wasn’t hard to find anything about vaccine inequality, but it was very hard to find analyses about efficiency of different campaigns. This led me to my next problem which was how to compare campaigns for different vaccines to each other. There was no accurate way to quantitatively compare each. At first, I thought I could just compare how much money was put into the campaign and how long it took to reduce the rate of infection. I quickly learned that there are a lot more factors that play a part. Each virus is different. It infects a different number of people, spreads at different rates, is transmitted in different ways, and the technology in the time period of the vaccination campaigns was very different. So, money and the reduction in infections over time was not a plausible way to analyze and compare. 

So, I had to rethink. I set up a meeting with Professor Wolaver and she helped me frame my research. Instead of comparing each campaign to one another, she helped me figure out that I should compare different factors in each campaign. Look at each campaign individually to see any correlation between different factors and the reduction in cases. For example, analyzing if the number of previous pandemics influences how well the country was able to respond to, for example, COVID. Besides the number of previous pandemics, Professor Wolaver also mentioned that I could look at factors like how democratic the country is, who colonized the country, and how much funding. Then, I could do qualitative analysis to see which factors influence the vaccination rate and compare the same factor for different campaigns. Then I could speculate why, and implement my reasoning for future/current campaigns. 

I think running across problems like this seems to be how research goes. Having to rethink your strategy and work with the data available is necessary. I think this was a good learning experience to be able to problem solve and find different ways to answer the same underlying question. Next time a problem like this comes up, I think I will have an easier time finding a solution. This also helped me realize the importance of collaboration with others during research. While I was stuck, Professor Wolaver was able to see things in a different way and help me get back on track.