The Rise of Malaria and upper Respiratory Tract Infections during the El Nino Season Using Logistics Regression Analysis and Bayes Theorem

  • Mnazi Samson Tsuma 89445, Mombasa 80100, Kenya
Keywords: Bayes Theorem, Communicable Disease, El Niño, Endemic Areas, Epidemic, Pandemic Areas, Posterior probability, Prevalence, Prior probability, Prognostic Factors.

Abstract

Malaria and URTI’S are known to be caused by different factors that range from environmental patterns to immunity of individuals. The prevalence of these two diseases usually increase from time to time due to situations such as lack of medical care, poor living standards and many other related factors. The abnormal increase of cases for such diseases can be caused by abnormal weather conditions like a rapid increase in precipitation. In this study, a scientific approach was employed in defining the epidemic of malaria and URTI’S by the use of effect size measures and probability models. This was based on the fact that there was a prediction of the El Nino condition by the meteorological department of Kenya and National Oceanic and Atmospheric Administration in America that was to affect the east African region between October 2015 to early 2016. The objective of this project was in line with the strategy of WHO/IDSR launched since 1998 and that is to provide a logistics probability model that investigates on the epidemics of URTI’s and Malaria occurring during the El Niño season. The project was conducted in Maasai Mara University; Narok using secondary data from the University health unit and it encompassed the number of Malaria and URTI cases reported during the year 2015. The incorporation of Bayesian analysis was reasonable to provide a clear picture of the effects of El Nino in the health sector and the remedies available.

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Published
2019-01-27
Section
Articles