MODELING THE CLIMATIC DETERMINANTS OF MALARIA CASES USING POISSON SWITCHING REGRESSION MODEL

Abstract

Malaria has become a common disease that affects the health of every household. The canker is expected to continue with its devastating effects for a long period given the absence of sufficient interventions to mitigate it. Several researches have been undertaken to investigate the pattern of malaria cases in Ghana. This study modeled the effects of climatic variables on monthly malaria cases in Wenchi Municipal using Poisson Switching Regression Model. Secondary data on malaria from January, 2013 to December, 2020 were obtained from the municipal health directorate. Rainfall, temperature and relative humidity data in the area for the same period were also obtained from the meteorological office. The data was analyzed using Poisson switching regression model. Using the climatic variables in the data collected the malaria cases were modeled in two and three different states. The three states were low, moderate and high states. The three-regime Poisson regression was preferred over the two–regime Poisson as it had better statistics (AIC, BIC and log-likelihood). The results revealed that, low malaria cases were recorded in the period between 2015 and 2016, moderate cases were between 2013 and 2014 and midyear 2020. In general terms, this study revealed positive relationship between malaria incidence and the climatic factors in low state while depicting a positive relation with rainfall and temperature during the moderate state and positive for only relative humidity in the high regime. Proactive measures spearheaded by the Ghana Health Service to sensitize the public on the need to practice safe malaria habits to avoid malaria transmission during periods of high malaria incidence is highly recommended.