In an age when global viruses such as COVID-19 are an urgent public health priority for research, overshadowing vector-borne diseases, the World Health Organization (WHO) has emphasized the critical need for continued efforts to prevent the transmission of vector-borne diseases, such as malaria, dengue or Zika, which are spread by mosquitoes. While focusing on digital solutions for pandemics, the WHO has implored the global community to not relent nor allow the current pandemic to eclipse the global agenda of reducing the burden of vector-borne diseases (1).

In the context of vector-borne disease surveillance and research, residential entomologic survey data are essential for understanding the geographical and temporal variability in mosquito occupancy in residential locations. Information gathered from entomologic surveys can be used to formulate control strategies for combating mosquito populations effectively. The spatially precise and fine-scale data collected under these surveys are one of the most sought-after pieces of information by health researchers and key policy makers in the field of overlooked tropical disease epidemiology. Such data can be utilized for supporting the decision-making process when determining which high-priority areas are in need of an intervention (i.e., mosquito/larvicidal campaigns or bed nets) (2). From a Global South perspective, although many surveys have been conducted (which have been diligently documented in notable open source websites, e.g., the Malaria Atlas Project (3) and the Global Aedes Aegypti & Albopictus Compendium (4)), such residential property-level survey data with information on physical and environmental characteristics are hard to come by. Such data in most cases remain inaccessible to health researchers, policy makers, and public health program managers. This problem of data paucity is due to the lack of a systematic approach for standardizing the collation of information into a digital format that was initially recorded on paper. The timely entry of data into an electronic registry is a challenge, and this issue is especially true for many low-resource settings in countries in sub-Saharan Africa (5–7) and Latin America (8) which is a hinderance in developing a more satisfactory dashboard for infectious disease surveillance.

Mosquitoes are sensitive to changes in environment and climate that impact their movement potential and survivability. In addition, the abundance of surface water in the form of a stagnated reservoir can have a positive or adverse effect on breeding habits and populations. These serve as risk factors that can have either a positive or negative influence on mosquito abundance in an environment. For example, many cities in the northeastern region of Brazil were hit hard by the Zika virus outbreak in 2015 (9–11). Zika virus, an arboviral infection, is transmitted by the Aedes mosquito genus (i.e., via two common species known as the Aedes aegypti and Aedes albopictus), which are endemic to that region. Their increased abundance in the northeast of Brazil is typically associated with standing water, which serves as a reservoir hotspot for breeding. Apart from the presence of standing water in human dwellings, a restricted set of climatic conditions such as land-surface temperature, humidity, precipitation, and seasonality, in addition to area-level socioeconomic deprivation risk factors, interact with each other to create an environment that is tenable for the mosquito’s survival (12).

To predict the spatial and spatiotemporal distribution of illnesses such as dengue, Zika, and chikungunya, in two northeasten Brazilian cities, Recife and Campina Grande, local environmental health authorities routinely carry out surveys on a bimestrial basis. The community health workers (CHWs) from these cities are deployed to high-risk neighborhoods to visit residential properties to inspect seven different types of breeding sources to detect the presence of the Aedes mosquito and its larvae (13–17). The CHWs collect other important information that describes the property’s physical characteristics (e.g., type of building structure and presence of a garden) and waste management practices (e.g., mode of waste disposal, presence of landfill, etc.) that contribute to the proliferation of mosquitoes (18, 19). A key challenge faced by these CHWs is the use of paper-based tools to document “thousands upon thousands” of pages of entomological information collected directly from the field. The data recorded on the paper forms must then be manually input into an electronic database by the CHWs; and ultimately increases the risk of passing incomplete data to policy makers and public health managers. This problem occurs in Recife and Campina Grande and was addressed by Aldosery et al. (18), who have developed a system for handling primary data with state-of-the-art bespoke smartphone applications (18). Considering these aforementioned issues (i.e., paucity of survey data due to how they are collected at the residential premises level), the CHWs are faced with challenges of linking primary information with other broader secondary sources of data that may contain wider indicators for water sanitation and hygiene (WASH) and weather parameters which may contribute to mosquito occupancy in residential premises (4, 20, 21).

A significant number of highly reliable open-source datasets are available. These open-source data can be linked to spatially referenced survey records to augment the mosquito surveillance effort. The authors argue that these sources remain elusive to many researchers in this line of research. Therefore, the primary objectives of this research article are threefold: (1) to point out to readers where these reliable open-source data can be acquired and explain 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 brought together to predict the presence of arboviruses through ecological niche modeling using the maximum entropy algorithm; and (3) to discuss the benefits of using bespoke technologies (smartphone applications, the Internet of Things, etc.), and to explain how these can be the new “gold-standard” approach for gathering primary entomologic survey data. The scope of this paper was limited to a Brazilian context because the research builds on an existing partnership with academics and stakeholders from environmental surveillance agencies in Recife (State of Pernambuco) and Campina Grande (State of Paraiba). We also chose the Aedes genus as the mosquito of focus due to its endemic status in Brazil and restricted the analysis specifically to the Aedes aegypti mosquito, since it is more commonly found than Aedes albopictus. Nevertheless, this article was written to build capacity for and awareness of data sources and methods, and thus it is applicable to different mosquito species and other areas in the Global South with similar circumstances.

In this section, we highlight the various sources of data that can be obtained online. We have provided a detailed description of how to use the data for causal inference and predictive analytics for mosquito occupancy. This included shapefiles for countries as well as point and raster grids for weather and environmental data, respectively. For raster data, we particularly highlighted the most reliable and updated sources available at a high spatial resolution.

In this section, we describe the implementation of a population-based ecological study design using spatially referenced point survey records on presence-only mosquito data, using Brazil as a case study for this demonstration. We will discuss the implementation of the maximum entropy model (MAXENT) for predicting the probability of mosquito occurrence across the whole of Brazil while accounting for other environmental attributes that impact mosquito habitats.

In this article, we described a broad range of open data sources that can be harnessed for the spatial prediction of mosquito populations. We used the whole of Brazil as a motivational case study to demonstrate how these datasets can be brought together for predicting the intensity of mosquito occupancy for Aedes aegypti in a data-sparse context using the MAXENT algorithm, which showed that population density, natural lighting, and precipitation made the biggest contributions to mosquito occupancy. While this approach was rigorous and should be used when data remains elusive, the author(s) concede that there are flaws in this approach. First, the research design of this case study was retrospective, using open entomologic data that were mostly available for 2013. The predictions shown in Figure 3 are not at all representative of the current climate situation when this research article was written (i.e., 2023). However, it would be possible to use the projected climate and population-based data for 2023 (which could have been done here, but this article is simply a demonstration), which could be fed into our trained MAXENT model and would have produced the predicted probability values for a future scenario. Second, the study design itself relies upon an ecological study design within a retrospective cross-sectional framework. In this study, the data used were a combination of both point and gridded information that is at a high geographic resolution but not at an individual level, e.g., at the level of household or property. This meant that the interpretation of the predictions needed to be done with the ecological fallacy in mind. These biases limit the research’s ability to achieve both internal and external validity. To combat these biases, we argue the case for using bespoke applications for acquiring accurate entomologic data.

Our case study demonstrated the combining of open-source data to crudely map areas of mosquito habitat suitability, analytically and in an unsupervised scenario (and where data paucity is an issue). However, the authors stand by the opinion that point-level mosquito surveillance data that recorded observations at either a residential premise or property level would be the “gold standard” approach for collecting primary data at a granular resolution. This provides ample opportunity to collect more detailed information describing property characteristics that promote infestation, which was absent from our case study. In addition, it provides point-level data used in point-process models for making spatial predictions regarding infestation burden, which in turn can be integrated into an early warning system for outbreaks (44–46). We propose that the use of smartphone applications that are developed for the main purpose of collecting surveillance data to show the infestation risk and at the same time the geographic burden of such infestations is the best way to support vector control campaigns. There is now a shift toward using such applications for this purpose, especially in Central and South America, with three examples given here. First, VectorPoint is an excellent mobile surveillance application for reporting Chagas disease and infestations in Arequipa, Peru (47). The application provides a risk map based on data collected during fieldwork. Second, Chaak is a smartphone-based application system interlinked with a dashboard (for managers) dedicated to mosquito-borne disease surveillance that captures data related to the immature stages of dengue virus mosquito vectors in Mérida, Mexico (48). Third, VazaDengue is akin to VectorPoint and Chaak; however, it is only a smartphone-based system that integrates social media with citizen science to guide surveillance agents in controlling mosquitoes (49).

The authors have developed a robust surveillance system, which is cloud based, that improves the surveillance of mosquitoes by providing timely and geolocated reports regarding the presence and absence of mosquitoes in properties along with other entomological characteristics (i.e., eggs or larvae) in north-east Brazilian cities, limited specifically to Recife and Campina Grande. This was done by taking everything the CHWs use in their mosquito control campaign [i.e., surveillance reporting cards (data collection sheets) and the spatial configuration of block areas as scanned maps] and digitizing them into a format supported by the MEWAR application (a full description of its development is provided by Aldosery et al. (18) (see Figure 5 ). The application seeks to collect general parameters linked to infestation, ranging from spatial information to property-level characteristics such as land use, waste disposal practices, and key entomologic infestation and treatment indicators (see Table 1 ).

The list of data sources that are raster based is malleable for area-level analysis—most mosquito surveillance data tend to be released at an area level; hence, these pixels can be aggregated within areal boundaries and matched to observed units to be used as risk factors. Appropriate rasters that are good candidates for aggregation include population density, climate data from WorldClim, and aridity—these can produce proxy measures for area units such as totals and other useful summary statistics (medians, averages, etc.) that can be implemented in a variety of spatial risk models at an area level [notable examples of spatial models for risk prediction include Bayesian hierarchical modeling (46, 50, 51) and machine learning (e.g., boosted regressions (52)]. Users rendering their data to this level and implementing this kind of approach should keep in mind the various biases that can occur when reporting results, and hence a pronounced bias, such as the ecological fallacy, should therefore be considered carefully. However, the use of individual-level data (i.e., residential premises) collected through primary surveys can be augmented with the open-source environmental spatial data highlighted in this article by linking their GPS coordinates by using a spatial join, overlapping the raster pixels with the surveillance points and assigning the raster values to the survey points to facilitate a much more precise and granular modeling approach. This would be invaluable, as the point-process approach for mapping is the gold standard for making spatial risk predictions for households, and the results can be further interpolated over a grid for risk coverage. There are many models that enable this kind of analysis—notable examples include Bayesian modeling frameworks such as stochastic partial differential equations using integrated nested Laplace approximations (SPDE-INLA) (53–55). These are valuable options for risk prediction and creating an early warning system for mosquito outbreaks; whereby such information can be fed into a dashboard to provide digital solutions for surveillance manager and policy makers (56, 57).

To conclude, we have identified a broad range of open-source data sources that can be harnessed as risk factors for the spatial prediction of mosquito occupancy or infestation, and we have demonstrated in a reproducible way how they can be brought together and implemented using the MAXENT algorithm within a Brazilian context. We explicitly note that this approach should be utilized within a data-sparse context. However, we also discussed the use of novel bespoke technologies, such as smartphone applications, that should be considered the better method for collecting primary entomologic data, to address the problems of data paucity and avoid potential biases that are typically found in studies using open source datasets—doing so will improve a study’s internal and external validity. This article was written to build capacity for and awareness of various data sources, demonstrating their use with reproducible methods, and thus it is applicable to different mosquito species and other areas in the Global South with similar environmental and socioeconomic conditions.

The original contributions presented in the study are included in the article. All datasets, script files and instructions for reproducing the analysis shown in this original article are made freely available through our GitHub repository (https://github.com/UCLPG-MSC-SGDS/Data-Sources).

Conceptualization: AM and PK; data management, extraction, and analysis: AM; original draft: AM; supervision and funding acquisition: PK, WPdS, TM, LCC, OY, and TA; revision of manuscript: PK, WPdS, TM, LCC, OY, TA, IVGB, MT, SB, EB, GMMM, ACGdS, CLdL, and AA. All authors contributed to the article and approved the submitted version.