Quantifying geographic accessibility to improve efficiency of entomological monitoring.
<h4>Background</h4>Vector-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, includ...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2020-03-01
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| Series: | PLoS Neglected Tropical Diseases |
| Online Access: | https://doi.org/10.1371/journal.pntd.0008096 |
| _version_ | 1797737325145358336 |
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| author | Joshua Longbottom Ana Krause Stephen J Torr Michelle C Stanton |
| author_facet | Joshua Longbottom Ana Krause Stephen J Torr Michelle C Stanton |
| author_sort | Joshua Longbottom |
| collection | DOAJ |
| description | <h4>Background</h4>Vector-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.<h4>Methodology/principal findings</h4>We 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.<h4>Conclusions/significance</h4>By 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. |
| first_indexed | 2024-03-12T13:27:02Z |
| format | Article |
| id | doaj.art-fb2c77fe476544b48ed745f8188f9e15 |
| institution | Directory Open Access Journal |
| issn | 1935-2727 1935-2735 |
| language | English |
| last_indexed | 2024-03-12T13:27:02Z |
| publishDate | 2020-03-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Neglected Tropical Diseases |
| spelling | doaj.art-fb2c77fe476544b48ed745f8188f9e152023-08-25T05:31:41ZengPublic Library of Science (PLoS)PLoS Neglected Tropical Diseases1935-27271935-27352020-03-01143e000809610.1371/journal.pntd.0008096Quantifying geographic accessibility to improve efficiency of entomological monitoring.Joshua LongbottomAna KrauseStephen J TorrMichelle C Stanton<h4>Background</h4>Vector-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.<h4>Methodology/principal findings</h4>We 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.<h4>Conclusions/significance</h4>By 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.https://doi.org/10.1371/journal.pntd.0008096 |
| spellingShingle | Joshua Longbottom Ana Krause Stephen J Torr Michelle C Stanton Quantifying geographic accessibility to improve efficiency of entomological monitoring. PLoS Neglected Tropical Diseases |
| title | Quantifying geographic accessibility to improve efficiency of entomological monitoring. |
| title_full | Quantifying geographic accessibility to improve efficiency of entomological monitoring. |
| title_fullStr | Quantifying geographic accessibility to improve efficiency of entomological monitoring. |
| title_full_unstemmed | Quantifying geographic accessibility to improve efficiency of entomological monitoring. |
| title_short | Quantifying geographic accessibility to improve efficiency of entomological monitoring. |
| title_sort | quantifying geographic accessibility to improve efficiency of entomological monitoring |
| url | https://doi.org/10.1371/journal.pntd.0008096 |
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