Musah, Anwar;
Browning, Ella;
Aldosery, Aisha;
Valerio Graciano Borges, Iuri;
Ambrizzi, Tercio;
Tunali, Merve;
Başibüyük, Selma;
... Kostkova, Patty; + view all
(2023)
Coalescing disparate data sources for the geospatial prediction of mosquito abundance, using Brazil as a motivating case study.
Frontiers in Tropical Diseases
, 4
, Article 1039735. 10.3389/fitd.2023.1039735.
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Abstract
One of the barriers to performing geospatial surveillance of mosquito occupancy or infestation anywhere in the world is the paucity of primary entomologic survey data geolocated at a residential property level and matched to important risk factor information (e.g., anthropogenic, environmental, and climate) that enables the spatial risk prediction of mosquito occupancy or infestation. Such data are invaluable pieces of information for academics, policy makers, and public health program managers operating in low-resource settings in Africa, Latin America, and Southeast Asia, where mosquitoes are typically endemic. The reality is that such data remain elusive in these low-resource settings and, where available, high-quality data that include both individual and spatial characteristics to inform the geospatial description and risk patterning of infestation remain rare. There are many online sources of open-source spatial data that are reliable and can be used to address such data paucity in this context. Therefore, the aims of this article are threefold: (1) to highlight where these reliable open-source data can be acquired and how they can be used as risk factors for making spatial predictions for mosquito occupancy in general; (2) to use Brazil as a case study to demonstrate how these datasets can be combined to predict the presence of arboviruses through the use of ecological niche modeling using the maximum entropy algorithm; and (3) to discuss the benefits of using bespoke applications beyond these open-source online data sources, demonstrating for how they can be the new “gold-standard” approach for gathering primary entomologic survey data. The scope of this article was mainly limited to a Brazilian context because it builds on an existing partnership with academics and stakeholders from environmental surveillance agencies in the states of Pernambuco and Paraiba. The analysis presented in this article was also limited to a specific mosquito species, i.e., Aedes aegypti, due to its endemic status in Brazil.
| Type: | Article |
|---|---|
| Title: | Coalescing disparate data sources for the geospatial prediction of mosquito abundance, using Brazil as a motivating case study |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.3389/fitd.2023.1039735 |
| Publisher version: | https://doi.org/10.3389/fitd.2023.1039735 |
| Language: | English |
| Additional information: | Copyright © 2023 Musah, Browning, Aldosery, Valerio Graciano Borges, Ambrizzi, Tunali, Başibüyük, Yenigün, Moreno, de Lima, da Silva, dos Santos, Massoni, Campos and Kostkova. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
| Keywords: | maximum entropy (MAXENT), GIS, mosquito occupancy, environmental suitability, Aedes aegypti, Brazil |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL SLASH UCL > Provost and Vice Provost Offices > UCL SLASH > Faculty of S&HS UCL > Provost and Vice Provost Offices > UCL SLASH > Faculty of S&HS > Dept of Geography |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10170887 |
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