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.

Neglected tropical diseases (NTDs) affect more than 1,000,000,000 poor and marginalized people worldwide [1]. NTDs are caused by diverse pathogens with differing modes of transmission and a range of vectors and intermediate hosts, which have their own ecological peculiarities. While there is considerable overlap in the geographical distribution of different NTDs at a national level [1], epidemiological differences of individual NTDs give rise to marked geographical variation at local levels. Since cost-effectiveness of intervention is greatest when targeted to areas having a high burden of multiple diseases, maps of the distribution of the different NTDs are essential for planning and implementing NTD interventions, as well as for providing visualization of program progress, so important for advocacy. In recent years there have been concerted, and very successful, efforts to develop detailed information resources on the geographical distribution of different NTDs (Table 1). Table 1 Currently available resources on the geographical distribution of NTDs. An important element of targeted NTD intervention is the delivery of mass drug administration (MDA) for treating the five major “preventive chemotherapy” NTDs, including lymphatic filariasis (LF), onchocerciasis, schistosomiasis, soil-transmitted helminths (STH), and trachoma [2]. MDAs targeting these NTDs are implemented alongside improvements in water and sanitation and hygienic behavior, as well as vector control. To help galvanize such global health efforts, the World Health Organization (WHO) and the NTD community defined targets to be achieved by 2020 and strategies to reach these targets (Table 2). As countries make progress towards the 2020 goals with an ever-increasing amount of data being collected, it is important to develop readily accessible tools that policymakers and program staff and partners can use to access, visualize, and compare data. Table 2 The five main NTDs and the drugs and strategies used to target them programmatically. In this Innovation to Application article, we describe the creation of an innovative NTD mapping tool (www.ntdmap.org) developed by a consortium of research and program partners for use particularly by program implementers. Its functionality and accessibility have been designed specifically to meet the needs of national programs and international partners. This tool provides an online resource allowing users to visualize and manipulate geographical data on a range of variables for the planning and managing of integrated NTD programs.