Modelling Malaria Incidence in the Limpopo Province, South Africa: Comparison of Classical and Bayesian Methods of Estimation

Abstract

Malaria infects and kills millions of people in Africa, predominantly in hot regions where temperatures during the day and night are typically high. In South Africa, Limpopo Province is the hottest province in the country and therefore prone to malaria incidence. The districts of Vhembe, Mopani and Sekhukhune are the hottest districts in the province. Malaria cases in these districts are common and malaria is among the leading causes of illness and deaths in these districts. Factors contributing to malaria incidence in Limpopo Province have not been deeply investigated, aside from the general knowledge that the province is the hottest in South Africa. Bayesian and classical methods of estimation have been applied and compared on the effect of climatic factors on malaria incidence. Credible and confidence intervals from a negative binomial model estimated via Bayesian estimation and maximum likelihood estimation, respectively, were utilized in the comparison process. Overall assumptions underpinning each method were given. The Bayesian method appeared more robust than the classical method in analysing malaria incidence in Limpopo Province. The classical method identified rainfall and temperature during the night to be significant predictors of malaria incidence in Mopani, Vhembe and Waterberg districts. However, the Bayesian method found rainfall, normalised difference vegetation index, elevation, temperatures during the day and night to be the significant predictors of malaria incidence in Mopani, Sekhukhune and Vhembe districts of Limpopo Province. Both methods affirmed that Vhembe district is more susceptible to malaria incidence, followed by Mopani district. We recommend that the Department of Health and Malaria Control Programme of South Africa allocate more resources for malaria control, prevention and elimination to Vhembe and Mopani districts of Limpopo Province.

Generalized Linear Models to Forecast Malaria Incidence in Three Endemic Regions of Senegal

“…The rainfall is highly and positively correlated, meaning that if the rainfall increases, the malaria incidence will increase. These results are also found in [ 11 ], revealing that rainfall was a strong positive predictor of increasing the annual incidence rate. The bed-net distribution is weakly correlated in Dakar.…”

“…The Poisson distribution is probably the most used discrete distribution because of its simplicity, according to [ 11 ]. Its conditional probability mass function is defined as in [ 2 , 11 ] by …”

“…In conclusion, all models indicated that the mean annual temperature, rainfall, vapour pressure and normalized difference vegetation index (NDVI) were strong positive predictors of increased annual incidence rate. In [ 11 ], the authors applied and compared a Bayesian and classical methods of parameter estimation on the effect of climatic factors in the context of modelling malaria incidence in Limpopo Province, South Africa. In that work, the authors estimated the parameters from a negative binomial model by a Bayesian estimation and maximum likelihood estimation.…”

“…In that work, the authors estimated the parameters from a negative binomial model by a Bayesian estimation and maximum likelihood estimation. As result, in [ 11 ] the Bayesian method appears more robust than the classical method in analyzing malaria incidence. In [ 12 ], the authors include the link between CD4 cell count and influencing covariates of biometric and demographic factors from negative binomial mixed models.…”

Bayesian Analysis Used to Identify Clinical and Laboratory Variables Capable of Predicting Progression to Severe Dengue among Infected Pediatric Patients

“…Moreover, it was adopted by Aswi et al, to examine the relation between the red blood cell count and the length of the hospital stay for dengue patients [ 26 ], and those authors found a positive association. Finally, it was utilized to predict the incidence of malaria in African populations by analyzing precipitation, altitude, temperature, the vegetation index, and other factors [ 52 ].…”