Circular Statistical Approach to Study the Occurrence of Seasonal Diseases

Abstract

In the present study, we have developed new circular descriptive statistics for Censored circular sample and attempted to analyse the occurrence of seasonal diseases, both month-wise and season-wise.  The Rayleigh Uniformity Test has also been proposed for the same, using which the presence of seasonal effect in both the cases. Finally, a regression model for predicting binary response from circular predictor has been proposed. The months being of unequal length, have been adjusted accordingly so as to make them of equal lengths. But since the seasons differ by a significant length and making them equal in length will mislead the analysis, we propose to group the cases in unequal intervals, the width of the intervals being proportional to the length of the seasons. That the season-wise analysis using circular statistical tools has not been attempted before is the main motivation behind our study. The data has been taken from the project entitled Statistical Modeling in Circular Statistics: An Application to Health Science, sponsored by the UGC, India, where diseases have been reported for the Kamrup (rural) district of Assam, India. It is revealed that the occurrence of seasonal diseases is highest in the months of March or equivalently, during the Pre-monsoon season. The distribution of occurrence of seasonal diseases both month-wise and season-wise is found to be marginally positively skewed and platykurtic.  The regression analysis suggests that seasonal diseases is least likely to occur in April as compared to December and in Winter in comparison to Post-monsoon.