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. High malaria incidences were observed in the
months of August, September and November. 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.
