Modeling the Impact of Climatic Variables on Malaria Incidences: A Case Study of Apac District, Northern Uganda

Abstract

Malaria is a major cause of morbidity and mortality in Apac District, Northern Uganda. Hence, the study aimed to model malaria incidences with respect to climate variables for the period 2007 to 2016 in Apac District. Data on monthly Malaria incidence in Apac District for the period January 2007 to December 2016 was obtained from the Ministry of Health, Uganda whereas climate data was obtained from Uganda National Meteorological Authority. Generalized linear models, Poisson and negative binomial regression models were employed to analyze the data. These models were used to fit monthly malaria incidences as a function of monthly rainfall and average temperature. Negative binomial model provided a better fit as compared to the Poisson regression model as indicated by the residual plots and residual deviances. The Pearson correlation test indicated a strong positive association between rainfall and Malaria incidences. The Autoregressive integrated moving average, ARIMA (1; 0; 0)(1; 1; 0)12 was found to be the best fit model for the malaria time series data. ARIMA models for time series analysis was found to be a simple and reliable tool for producing relaible forecasts for malaria incidences in Apac District, Uganda. This study showed a significant association between monthly malaria incidence and climate variables that is rainfall and temperature. This study provided useful information for predicting malaria incidence and developing the future warning system. This is an important tool for policy makers to put in place effective control measures for malaria early enough. Malaria still remains a public health concern in Uganda, in particular Apac District.