Spatial analysis of G.f.fuscipes abundance in Uganda using Poisson and Zero-Inflated Poisson regression models - Albert Mugenyi; Dennis Muhanguzi; Guy Hendrickx; Gaëlle Nicolas; Charles Waiswa; Steve Torr; Susan Christina Welburn; Peter M. Atkinson (Public Library of Science (PLoS), 2021)
Background Tsetse flies are the major vectors of human trypanosomiasis of the form Trypanosoma brucei rhodesiense and T.b.gambiense. They are widely spread across the sub-Saharan Africa and rendering a lot of challenges to both human and animal health. This stresses effective agricultural production and productivity in Africa. Delimiting the extent and magnitude of tsetse coverage has been a challenge over decades due to limited resources and unsatisfactory technology. In a bid to overcome these limitations, this study attempted to explore modelling skills that can be applied to spatially estimate tsetse abundance in the country using limited tsetse data and a set of remote-sensed environmental variables. Methodology Entomological data for the period 2008-2018 as used in the model were obtained from various sources and systematically assembled using a structured protocol. Data harmonisation for the purposes of responsiveness and matching was carried out. The key tool for tsetse trapping was itemized as pyramidal trap in many instances and biconical trap in others. Based on the spatially explicit assembled data, we ran two regression models; standard Poisson and Zero-Inflated Poisson (ZIP), to explore the associations between tsetse abundance in Uganda and several environmental and climatic covariates. The covariate data were constituted largely by satellite sensor data in form of meteorological and vegetation surrogates in association with elevation and land cover data. We finally used the Zero-Inflated Poisson (ZIP) regression model to predict tsetse abundance due to its superiority over the standard Poisson after model fitting and testing using the Vuong Non-Nested statistic. Results A total of 1,187 tsetse sampling points were identified and considered as representative for the country. The model results indicated the significance and level of responsiveness of each covariate in influencing tsetse abundance across the study area. Woodland vegetation, elevation, temperature, rainfall, and dry season normalised difference vegetation index (NDVI) were important in determining tsetse abundance and spatial distribution at varied scales. The resultant prediction map shows scaled tsetse abundance with estimated fitted numbers ranging from 0 to 59 flies per trap per day (FTD). Tsetse abundance was found to be largest at low elevations, in areas of high vegetative activity, in game parks, forests and shrubs during the dry season. There was very limited responsiveness of selected predictors to tsetse abundance during the wet season, matching the known fact that tsetse disperse most significantly during wet season. Conclusions A methodology was advanced to enable compilation of entomological data for 10 years, which supported the generation of tsetse abundance maps for Uganda through modelling. Our findings indicate the spatial distribution of the G. f. fuscipes as; low 0-5 FTD (48%), medium 5.1-35 FTD (18%) and high 35.1-60 FTD (34%) grounded on seasonality. This approach, amidst entomological data shortages due to limited resources and absence of expertise, can be adopted to enable mapping of the vector to provide better decision support towards designing and implementing targeted tsetse and tsetse-transmitted African trypanosomiasis control strategies. Author summary Tsetse flies are the major vectors of human and animal trypanosomiasis. To support control efforts, there is need to understand the location of these vectors in terms of distribution and abundance. Due to limited resources and unsatisfactory technology, delimiting the extent and magnitude of tsetse coverage remains a challenge. In a bid to overcome these limitations, this study attempted to explore modelling skills that can be applied to inform decision makers about the status of tsetse abundance using limited historical tsetse data and a set of remote-sensed environmental variables. Two regression models; standard Poisson and Zero-Inflated Poisson (ZIP) were fitted and evaluated for superiority. The results indicated the level of responsiveness of each covariate in influencing the vector across the study area. Tsetse abundance was found to be largest at low elevations in areas of high vegetative activity, in game parks, forests and permanently flooded shrubs during the dry season. This approach can be adopted to enable mapping of any tsetse species to provide better decision support towards designing and implementing targeted tsetse and tsetse-transmitted African trypanosomiasis control strategies amidst entomological data shortages due to limited resources and absence of expertise.