Abstract: Various studies have reported that global warming causes unstable climate and serious impact on physical environment and public health. The increasing incidence of dengue case is now a priority health issue and has become a health burden for Pakistan. In this study it has been investigated that spatial pattern of environment causes the emergence or increasing rate of dengue fever incidence that effects the population and its health. The climatic or environmental and the Dengue Fever (DF) case data was processed by coding, editing, tabulating, recoding and restructuring and finally applying different statistical methods, techniques and procedures for the analysis and interpretation. Five climatic variables which we have studied are precipitation (P), Maximum temperature (Mx), Minimum temperature (Mn), Humidity (H) and Wind speed (W) collected from 1980-2012. The data on Dengue Fever cases in Karachi for the period 2010 to 2012 are available and reported on weekly basis. Principal Component 1 (PC1) for all groups of the period can be interpreted as the General atmospheric condition. PC2 the second important climate factor for dengue period (2010-2012) comes out contrast between precipitation and wind speed. PC3 is the weighted difference between maximum temperature and wind speed. PC4 is the contrast between maximum and wind speed. Negative Binomial and Poisson regression model are used to correlate the dengue fever incidence to climatic variable and principal component (PC) score. Due to the problems of over dispersion the Poisson models are not useful for interpretation through Negative Binomial model we found that relative humidity causes an increase on the chances of dengue occurrence by 1.71% times. While maximum temperature positively influence on the chances dengue occurrence by 19.48% times. Minimum temperature affects on the chances of dengue occurrence by 11.51% times. Wind speed is effecting negatively on the weekly occurrence of dengue fever by 7.41%times.
Keywords: Principal component analysis, Dengue Fever, Negative Binomial Regression model, Poisson Regression model.