During the spread of an infectious disease such as COVID-19, the identification of human factors that affect the spread is a really important area of research. These factors directly impact the spread of such a disease and are important in identifying the various regions that are at a higher risk than others. This allows for an optimal distribution of resources according to predicted demand. Models such as the SIR framework exist and are very good at representing the spread of diseases and can incorporate multiple factors that resemble real-life scenarios. The primary issue in this area is the identification of relevant variables. In this study, a residual analysis is presented to downsize the dataset available and shortlist the small number of variables classified as absolutely necessary for disease modeling. The performance of different datasets is evaluated using an Artificial Neural Network and regression analysis. The results show that the drop in performance is reasonable and this approach can be automated in the future as it offers a small dataset containing a few variables against a large dataset with possibly hundreds of variables.