Fitting Probability Distribution Function to Malaria Incidence Data

Abstract

Abstract: Malaria remains a major infectious disease that affects millions of people. Once infected with Plasmodium parasites, a host can develop a broad range of clinical presentations, which result from complex interactions between factors derived from the host, the parasite and the environment. Malaria has historically been a very serious health problem and currently poses the most significant threat to the health of Masinde Muliro University of Science and Technology students, data shows that more than 70% percent of pediatric cases are due to malaria. Methodology: Hence, the study aimed to fit malaria incidences dataset for the period 1st January, 2013 to 31st December, 2015. Data on monthly malaria incidence was obtained from the Masinde Muliro University of Science and Technology health service. Gamma, Weibull and Lognormal Distributions were employed to fit the malaria incidence dataset using R-software. Results: High malaria incidences were observed in the months of August, September and November. AIC values results showed that lognormal distribution had the lowest AIC value of 185.9875 followed by the Gamma distribution with a value of 187.8815 and then the Weibull distribution with a value of 188.7271. This confirmed the lognormal distribution to be the best fitting distribution for the malaria incidence dataset Conclusion: The Poisson regression model did not accurately fit the data on malaria incidences due to over dispersion in the data but lognormal distribution was a better fit compared to gamma and Weibull distribution.