African Journal of Malaria and Tropical Diseases

African Journal of Malaria and Tropical Diseases ISSN 2736-173X Vol. 12 (5), pp. 001-007, May, 2024. Available online at www.internationalscholarsjournals.org © International Scholars Journals

Full Length Research Paper

Predictive Modeling of Glossina fuscipes fuscipes Density Using Linear Regression Techniques in Kajo-keji County, South Sudan

Yatta S. Lukou¹*, Mubarak M. Abdelrahman², Yassir O. Mohammed³, Loro G. L. Jumi¹, Erneo B. Ochi¹, Yousif R. Suliman⁴ and Intisar E. Elrayah²

¹College of Natural Resources and Environmental Studies, University of Juba, P. O. Box 82 Juba, South Sudan.

²Tropical Medicine Research Institute (TMRI), P. O. Box 1304, Khartoum, Sudan.

³Veterinary Research Institute (VRI), P. O. Box 8067, Khartoum, Sudan.

⁴Department of Breeding and Biotechnology, College of Animal Production, University of Bahri, P. O. Box 1660, Khartoum North, Sudan.

Received 9 October, 2023; Accepted 9 April, 2024

Abstract
Glossina fuscipes fuscipes remain the main tsetse vectors of Trypanosoma brucei gambiense that causes Human African Trypanosomiasis (HAT) in South Sudan, where HAT Control Strategy does not involve vector control component. Information on the fly apparent density/trap/day helps identify priority areas for vector control. Insecurity and logistic problem makes it impossible for vector control to be carried out. Fly-human contacts might be reduced in areas where the fly infestation may contribute to the disease transmission. This study employs Linear Regression Analysis to predict adult G. f. fuscipes apparent density/trap/day in Kajo-keji County. Tsetse field surveys were carried out along 8 streams in the study area 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 appears fit for the data and prediction of the fly apparent density from the various predictors (F (4,11) =14.321, P <0.02). The densities predicted by the model did not statistically (df=11; P = 0.69) vary from the actual ones. This study could contribute to predict the peaks of the vector abundance that guide strategic plans for tsetse and HAT control programmes in South Sudan.

Key words: Glossina fuscipes fuscipes, apparent density, regression models, environmental factors.