Coalescing disparate data sources for the geospatial prediction of mosquito abundance, using Brazil as a motivating case study - Musah, Anwar; Browning, Ella; Aldosery, Aisha; Valerio Graciano Borges, Iuri; Ambrizzi, Tercio; Tunali, Merve; Başibüyük, Selma; Yenigün, Orhan; Moreno, Giselle Machado Magalhaes; de Lima, Clarisse Lins; da Silva, Ana Clara Gomes; dos Santos, Wellington Pinheiro; et al. (Frontiers in Tropical Diseases, 2023)
One of the barriers for performing geospatial surveillance for mosquito occupancy or infestation anywhere in the world is the paucity of primary entomologic survey data geolocated at a residential property-level 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 programmatic 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 illusive in these low-resource settings, and where available, such high quality data that contains both individual and spatial characteristics to inform the geospatial descriptive and risk patterning of infestation remains rare. There are many online open-source spatial data which are reliable and can be used to address such data paucity in this context. Therefore, the aims of this article are three-way, we wish: 1.) to point out to readers where these reliable open-source data can be acquired and how they can be used as risk factors for making spatial prediction for mosquito occupancy in general; 2.) use Brazil as a case study to demonstrate how these datasets can be brought together to predict the presence of arboviruses through the use of Ecological Niche Modelling using the Maximum Entropy algorithm; and 3.) we discussed the benefits of using bespoke applications beyond these open-source online options so as to build a case to explain how it can be the new “gold-standard” approach for gathering primary entomologic survey data. Readers should bear in mind that the scope of this article was mainly limited to a Brazilian context due to the fact that it builds on an existing partnership with academics and stakeholders from environmental surveillance agencies in the State of Pernambuco and Paraiba. The analysis presented in this paper was also limited to a specific mosquito species i.e., Aedes agypti due to its endemicity status in Brazil.