The Power of Multiple Linear Regression Models to Predict Apparent Density of Glossina fuscipes fuscipes (Diptera: Glossinidae): A Case Study of Kajo-Keji County, Central Equatoria State, South Sudan

Glossina fuscipes fuscipes stretch to be the primary tsetse headings of Trypanosoma brucei gambiense, the cause of Human African Trypanosomiasis (HAT), in South Sudan, where the HAT Control Strategy does not contain a vector control component. Priority domains for vector control maybe determined utilizing data on flee apparent bulk/ trap/day. Insecurity and logistic problem create it impossible for heading control activities expected carried out, accordingly, there is a need for an alternative form to assess heading population outside having material presence in fields. What is wanted under these circumstances are the tangible parameters that influence heading population density in the study extent. Such variables are always usable in meteorological stations in the Country. The study aims at providing facts on Glossina fuscipes fuscipes apparent mass/trap/day in Kajo-Keji County by employing Multiple Linear Regression Models accompanying input from incidental variables (Atmospheric temperature, sleet, relative humidity and wind speed). Tsetse field surveys were administered along 8 streams in the study region from January to December 2012. To estimate fly seeming density/trap/era as a function of probable determinants for tsetse fly catches, duodecimal linear reversion models were created. The mated samples T-test in SPSS was used to investigate the conflict between the flee apparent densities presented by the models and the real densities from the survey. The top and lower limits of the model agreements were 5.97 and -11.65, respectively, and the guess values of the models showed the monthly styles of G. fuscipes fuscipes abundance. The model performs fit for the dossier and prediction of the flee apparent bulk from the various predictors (F (4,11) =14.321, P <0.02). The densities forecasted by the models acted not vary statistically (df=11; P = 0.69) from the real ones. This study take care of contribute to support information on the peaks of the heading abundance that grant permission guide strategic plans for tsetse and HAT control programmes in South Sudan. Multiple Linear Regression Models are strong and flexible and could find requests in the various facets of tsetse studies and provide beneficial information for tsetse and type of encephalitis control programmes in South Sudan.

Author(s) Details:

Yatta S. Lukou,
College of Natural Resources and Environmental Studies, University of Juba, P.O. Box-82 Juba, South Sudan.
Mubarak M. Abdelrahman,
Tropical Medicine Research Institute (TMRI), P.O. Box-1304, Khartoum, Sudan.

Yassir O. Mohammed,
Veterinary Research Institute (VRI), P.O. Box-8067, Khartoum, Sudan.

Loro G. L. Jumi,
College of Natural Resources and Environmental Studies, University of Juba, P.O. Box-82 Juba, South Sudan.

Erneo B. Ochi,
College of Natural Resources and Environmental Studies, University of Juba, P.O. Box-82 Juba, South Sudan.

Yousif R. Suliman,
Department of Breeding and Biotechnology, College of Animal Production, University of Bahri, P.O. Box-1660, Khartoum North, Sudan.

Intisar E. Elrayah,
Tropical Medicine Research Institute (TMRI), P.O. Box-1304, Khartoum, Sudan.

Please see the link here: https://stm.bookpi.org/CERB-V2/article/view/8890

Keywords: Glossina fuscipes fuscipes, apparent density, multiple linear regression models, environmental factors

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