One of the priorities of malaria elimination or at least reduction has been prevention methods. To apply these methods, understanding transmission influencing factors is crucial. The majority of driving factor of malaria transmission are environmental and climatic features. Plenty of regression analysis methods has been implemented to assess the relationship of malaria transmission and environmental and/or climatic features. Nonetheless, the majority of regression methods for the case lacked robustness. Recently circular regression analysis is gaining acceptance by many academicians to assess scenarios that have circularity in their nature. There are few circular regression models but their applicability and tractability are not fully assessed as for their linear counterpart. We assessed the applicability of these models on Malaria data collected for time series analysis titled “Association of climatic variability, vector population and malaria disease in the district of Visakhapatnam, India”: we used Fourier methods with ordinary linear regression. We applied different properties of circular distributions and assumptions. We transformed linear time in months to circular time and convert it to radian form since directional analysis is best suited to radian measures. We used malaria cases in months as a dependent linear variable and months in radian as acircular explanatory variable. We have assessed the correlation between these variables and found it right, so we apply circular regression on them and found avery sensible model with few drawbacks.