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Kres, A.C., Schwarz, N.G., Kruger, A., Fobil, J., Nkrumah, B., Acquah, S., Loag,, W., Sarpong, N., Adu-Sarkodie, Y., Ranft, U., et al. (2011) Modeling the Relationship between Precipitation and Malaria Incidence in Children from a Holoendemic Area in Ghana. The American Journal of Tropical Medicine and Hygiene, 84, 285-291.
https://doi.org/10.4269/ajtmh.2011.10-0381

has been cited by the following article:

• TITLE: Statistical Modeling of Malaria Incidences in Apac District, Uganda

  AUTHORS: Ayo Eunice, Anthony Wanjoya, Livingstone Luboobi

  KEYWORDS: Malaria Incidence, Climate Variables, Poisson Regression, Negative Binomial Regression, Generalized Linear Model, Apac District

  JOURNAL NAME: Open Journal of Statistics, Vol.7 No.6, November 16, 2017

  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.