Journal of
Parasitology and Vector Biology

  • Abbreviation: J. Parasitol. Vector Biol.
  • Language: English
  • ISSN: 2141-2510
  • DOI: 10.5897/JPVB
  • Start Year: 2009
  • Published Articles: 189

Article in Press

Application of Linear Regression Models in Predicting the Density of Glossina fuscipes fuscipes (Diptera: Glossinidae) in Kajo-keji County, Central Equatoria State, South Sudan

Yatta S. Lukou, Mubarak M. Abdelrahman , Yassir O. Mohammed , Loro G. L. Jumi , Erneo B. Ochi ,Yousif R. Suliman and Intisar E. Elrayah

  •  Accepted: 09 April 2021
Glossina fuscipes fuscipes remains the main tsetse vectors of Trypanosoma brucei gambiense, the cause of Human African Trypanosomiasis (HAT) in South Sudan. South Sudan HAT Control Strategy does not involve vector control component. Information on G.f.fuscipes apparent density/trap/day helps in identifying priority areas of priorities for vector control. Insecurity and logistic problem make it impossible for vector control to be carried out in the country. In this situation, information on the fly apparent density/catch/day may be useful. Fly human contacts might be reduced in areas where the fly infestation may contribute in the disease transmission. The purpose of this study was to apply Linear Regression Analysis in the prediction of adult G.f.fuscipes apparent density/trap/day in Kajokeji County in South Sudan. Tsetse field surveys were carried out along 8 streams in the study area as from January 2012 to December 2012. Twelve linear regression models were developed to predict the apparent density /trap/day as function of potential predictors for tsetse fly catches. The difference between the fly apparent densities generated by the models and the actual densities from the survey was analyzed using paired samples T-test in SPSS. Models predictive values showed the monthly trends of G. f. fuscipes abundance with the upper and lower limits of the model agreements of 5.97 and -11.65, respectively. The model proved to be good and fit for the data and prediction of the fly apparent density from the various predictors (F (4,11) =14.321, P <0.02). The fly apparent densities predicted by the model did not statistically (df=11; P = 0.69) vary from the actual apparent densities from the survey. This study could contribute to predict the peaks of the vector abundance and subsequently guide strategic plans for tsetse control that facilitates location of priority areas for HAT control programmes in South Sudan.

Keywords: Glossina fuscipes fuscipes ,Apparent Density, Regression models, Environmental factors