The purpose of this experiment is to derive an algorithm using data from past pandemics that can help to predict infection rate and morbidity of future pandemics. It was hypothesized that careful study of previous pandemics may be used in predicting infection and morbidity rate of future pandemics. In the experiment, websites such as CDC government webpages and History.com were browsed. This gave the researcher statistics from multiple past pandemics, which were studied in order to find patterns. These pandemics were the 1918 Spanish Influenza, the 1957 Asian Flu, the 1968 Hong Kong Flu, and the 2009 Swine Flu. Most of the facts came from these webpages, but the rest was inferred from related facts and given formulas. The facts included the amount of deaths in a certain area, the amount of people infected in the certain area, and the amount of time it took for that to happen. Graphs comparing statistics from certain areas and certain diseases were made, and no evident patterns were found. This effort seeked to establish a pandemic algorithm that will assist in predicting infection rate and morbidity rate based on the contributing factors’ such as social distancing, mask wearing, and  hand washing hygiene. It is proposed that this algorithm will capture the degree to which these social factors contribute to the infection and morbidity rate and by manipulating these factors it will be able to provide some predictability in how to better cope with diseases, prevent diseases, and survive in such an environment.