Abstract

BackgroundVector-borne diseases are important causes of mortality and morbidity in humans and livestock, particularly for poorer communities and countries in the tropics. Large-scale programs against these diseases, for example malaria, dengue and African trypanosomiasis, include vector control, and assessing the impact of this intervention requires frequent and extensive monitoring of disease vector abundance. Such monitoring can be expensive, especially in the later stages of a successful program where numbers of vectors and cases are low.Methodology/Principal findingsWe developed a system that allows the identification of monitoring sites where pre-intervention densities of vectors are predicted to be high, and travel cost to sites is low, highlighting the most efficient locations for longitudinal monitoring. Using remotely sensed imagery and an image classification algorithm, we mapped landscape resistance associated with on- and off-road travel for every gridded location (3m and 0.5m grid cells) within Koboko district, Uganda. We combine the accessibility surface with pre-existing estimates of tsetse abundance and propose a stratified sampling approach to determine the most efficient locations for longitudinal data collection. Our modelled predictions were validated against empirical measurements of travel-time and existing maps of road networks. We applied this approach in northern Uganda where a large-scale vector control program is being implemented to control human African trypanosomiasis, a neglected tropical disease (NTD) caused by trypanosomes transmitted by tsetse flies. Our accessibility surfaces indicate a high performance when compared to empirical data, with remote sensing identifying a further ~70% of roads than existing networks.Conclusions/SignificanceBy integrating such estimates with predictions of tsetse abundance, we propose a methodology to determine the optimal placement of sentinel monitoring sites for evaluating control programme efficacy, moving from a nuanced, ad-hoc approach incorporating intuition, knowledge of vector ecology and local knowledge of geographic accessibility, to a reproducible, quantifiable one.

Highlights

  • Vector-borne diseases (VBDs) are important causes of mortality and morbidity in humans and livestock, for poorer communities and countries in the tropics, accounting for an estimated 17% of the global burden of all infectious diseases [1]

  • We used image classification and cost-distance algorithms to produce estimates of accessibility within Koboko district, Uganda, where vector control is contributing to the elimination of sleeping sickness, a neglected tropical disease (NTD)

  • By providing methods to ensure that vector control programmes operate at maximum efficiency, we can ensure that the limited funding associated with some of these NTDs has the largest impact

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Summary

Introduction

Vector-borne diseases (VBDs) are important causes of mortality and morbidity in humans and livestock, for poorer communities and countries in the tropics, accounting for an estimated 17% of the global burden of all infectious diseases [1]. The control of VBDs, or their elimination as a public health problem, is dependent upon effective vector management, which includes pre-intervention surveys and subsequent longitudinal monitoring of vector abundance to assess the effectiveness of an intervention. Such monitoring is an important component of the overall costs of control. A more rational method would involve combining information on vector abundance with estimates of geographical accessibility, to identify sites across operational areas where pre-intervention catches are high and sampling costs are low Towards this goal, we examined the utility of remotely sensed (RS) data to produce contemporary estimates of geographic accessibility to entomological sampling sites, using sleeping sickness control as an example application.

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