1 Introduction

Mosquito monitoring is a critical component in the management and control of mosquito-borne diseases. It involves the systematic collection, analysis, and interpretation of data related to mosquito populations and their habitats. Effective mosquito monitoring helps in understanding the distribution, abundance, and behavior of mosquito species, which is essential for implementing targeted control measures and reducing the risk of disease transmission (Ahmad et al., 2011).

Accurate mosquito monitoring is vital for public health as it directly impacts the control and prevention of diseases such as malaria, dengue fever, chikungunya, and Zika virus. These diseases pose significant health risks globally, particularly in tropical and subtropical regions. By identifying high-risk areas and periods of increased mosquito activity, public health officials can allocate resources more efficiently, implement timely interventions, and ultimately reduce the incidence of mosquito-borne diseases (Rydzanicz et al., 2011; Uzair and Tariq, 2023). Precise monitoring allows for the evaluation of control measures' effectiveness, ensuring that strategies are adapted to changing environmental and epidemiological conditions (Nihei et al., 2016).

Geographic Information Systems (GIS) are powerful tools that integrate spatial and temporal data to analyze and visualize patterns and relationships in various fields, including public health and environmental studies. GIS technology enables the mapping and analysis of mosquito habitats, breeding sites, and disease transmission areas, providing a comprehensive understanding of the spatial dynamics of mosquito populations 18. The use of GIS in mosquito monitoring facilitates the identification of environmental factors influencing mosquito distribution, such as land use, vegetation, and climate, and supports the development of predictive models for disease risk assessment (Mueller et al., 2022).

This study reviews the current status of GIS applications in mosquito surveillance and control, demonstrates the effectiveness of GIS in identifying and managing mosquito breeding sites through case studies, discusses the integration of GIS with other technologies such as remote sensing and global positioning system (GPS) to improve mosquito surveillance and control efforts, and identifies challenges and future directions for using GIS for mosquito surveillance. By providing a comprehensive overview of GIS applications in mosquito surveillance, this study aims to highlight the importance of using advanced spatial analysis tools to effectively combat mosquito-borne diseases.

2 Fundamentals of Geographic Information Systems (GIS)

2.1 Definition and components of GIS

Geographic Information Systems (GIS) are powerful tools that allow for the collection, storage, analysis, and visualization of spatial and geographic data. GIS integrates various types of data, including cartographic, photographic, and digital data, to create layered maps and models that can be analyzed and manipulated interactively (Uzair and Tariq, 2023). The core components of GIS include hardware, software, data, people, and methods. Hardware refers to the physical devices used to run GIS software, while software encompasses the programs and applications that process spatial data. Data is the most critical component, consisting of geographic information and attribute data. People are the users who operate the GIS, and methods are the techniques and procedures used to analyze the data (Caputo et al., 2020).

2.2 Data types and sources used in GIS

GIS utilizes various data types and sources to create comprehensive spatial analyses. The primary data types include vector data, which represents geographic features as points, lines, and polygons, and raster data, which represents geographic features as a grid of cells or pixels (Kofidou et al., 2021). Data sources for GIS can be diverse, including satellite imagery, aerial photography, remote sensing data, and ground surveys. For instance, GIS can integrate data from cartography, earthbound surveys, and remote sensing to create overlapping layers that provide a detailed spatial structure. Additionally, GIS can incorporate data from surveillance and management activities, enhancing the precision of environmental and biological analyses (Lonc et al., 2010; Rydzanicz et al., 2011).

2.3 GIS software and tools

Several GIS software and tools are available to facilitate spatial analysis and mapping. Popular GIS software includes ArcGIS, QGIS, and GRASS GIS, each offering various functionalities for data manipulation, spatial analysis, and visualization (Rydzanicz et al., 2011). These tools allow users to create detailed maps, perform spatial queries, and analyze spatial relationships. For example, ArcView software has been used to prepare detailed maps delineating mosquito breeding sites, enhancing the efficacy of control programs. GIS software often integrates with other technologies such as Global Positioning Systems (GPS) and remote sensing to provide more accurate and comprehensive spatial data (Cull, 2021).

2.4 Advantages of GIS in environmental monitoring

GIS offers numerous advantages in environmental monitoring, particularly in the context of mosquito monitoring and control. One of the primary benefits is the ability to integrate and analyze diverse data sources, providing a comprehensive view of environmental conditions and potential risk factors (Uzair and Tariq, 2023). GIS enables precise mapping and analysis of mosquito breeding sites, flight ranges, and host distributions, which are crucial for effective control measures (Minakshi et al., 2020; Rano et al., 2022). Furthermore, GIS can enhance the spatial resolution of environmental data, allowing for more accurate predictions and targeted interventions. The technology also supports the identification of high-risk areas and the monitoring of control measures' effectiveness, ultimately contributing to more efficient and sustainable mosquito control programs.

GIS is a versatile and powerful tool that plays a critical role in environmental monitoring and mosquito control. By integrating various data types and sources, utilizing advanced software and tools, and offering significant advantages in data analysis and visualization, GIS enhances our ability to understand and manage mosquito populations and the diseases they transmit.

3 Role of GIS in Mosquito Monitoring

3.1 Spatial data collection and integration

GIS facilitates the collection and integration of spatial data from multiple sources, enhancing the ability to monitor mosquito populations and vector-borne diseases. By integrating data from field surveys, environmental monitoring, and health records, GIS provides a holistic view of mosquito distribution and disease incidence. For instance, GIS has been used to map the distribution of malaria and dengue fever in Pakistan, integrating data from hospitals, clinics, and laboratories to identify outbreaks and monitor control measures. Additionally, GIS can incorporate environmental data such as land use, vegetation, and weather patterns, which are crucial for understanding the drivers of mosquito distribution and disease transmission (Akindele et al., 2023; Uzair and Tariq, 2023).

3.2 Mapping and visualization of mosquito populations

One of the primary applications of GIS in mosquito monitoring is the mapping and visualization of mosquito populations. GIS allows for the creation of detailed maps that display the spatial distribution of mosquito breeding sites and disease cases. For example, in Malaysia, GIS was used to map larval breeding habitats and malaria transmission risk areas, combining field data with satellite imagery to visualize the distribution patterns of vector species (Ahmad et al., 2011). Similarly, GIS has been employed to generate risk maps for dengue virus exposure, aiding in the development of targeted vector control strategies (Eisen and Lozano-Fuentes, 2009).

3.3 Temporal analysis and trend prediction

GIS also enables temporal analysis and trend prediction, providing insights into the dynamics of mosquito populations and disease outbreaks over time. By analyzing spatial and temporal data, researchers can identify trends and predict future outbreaks, allowing for proactive control measures. For instance, GIS has been used to map the spatiotemporal distribution of malaria cases in Pakistan, identifying hotspots of transmission and guiding targeted interventions. The integration of remote sensing data with GIS further enhances the ability to monitor changes in land use and climate, which can impact mosquito distribution and disease incidence (Mueller et al., 2022).

3.4 Identification of high-risk areas

Identifying high-risk areas for mosquito-borne diseases is crucial for effective control and prevention. GIS provides the tools to analyze spatial data and identify clusters or hotspots of disease transmission. For example, GIS was used to map the distribution of dengue cases in Lahore, Pakistan, identifying high-risk areas for transmission and guiding control measures such as the removal of mosquito breeding sites and the distribution of larvicide (Uzair and Tariq, 2023). Similarly, GIS has been employed to map the distribution of Japanese Encephalitis in Pakistan, identifying risk factors such as land use and weather patterns to prioritize intervention areas (Uzair and Tariq, 2023).

3.5 Integration with other technologies

The integration of GIS with other technologies, such as remote sensing and drones, further enhances mosquito monitoring capabilities. Remote sensing provides high-resolution satellite imagery that can be used to identify and map mosquito breeding habitats, while drones offer a cost-effective means of conducting aerial surveys and collecting real-time data. For instance, GIS combined with remote sensing has been used to map the distribution of helminth infections, providing reliable estimates of populations at risk and guiding intervention strategies. The use of WebGIS and other geospatial tools has also been critical in visualizing data and informing public health decisions during the COVID-19 pandemic, demonstrating the potential for these technologies in future public health emergencies (Ahasan et al., 2020).

GIS plays a multifaceted role in mosquito monitoring, from data collection and integration to mapping, temporal analysis, and the identification of high-risk areas. The integration of GIS with other technologies further enhances its capabilities, making it an invaluable tool in the fight against mosquito-borne diseases.

4 Applications of GIS in Different Phases of Mosquito Monitoring

4.1 Surveillance and data collection

Geographic Information Systems (GIS) have revolutionized the surveillance and data collection phase of mosquito monitoring by providing a spatial and temporal perspective on disease occurrence. GIS can map the distribution of mosquito vectors and the pathogens they transmit, which is crucial for identifying high-risk areas and targeting control measures effectively. For instance, GIS has been used to map the spatiotemporal distribution of malaria cases in Pakistan, identifying hotspots and enabling targeted interventions such as bed net distribution and indoor residual spraying. Similarly, in Malaysia, GIS was employed to map larval breeding habitats and malaria transmission risk areas, facilitating strategic planning and management of control measures (Ahmad et al., 2011).

4.2 Data analysis and interpretation

The integration of GIS in data analysis and interpretation allows for a more comprehensive understanding of the ecological factors contributing to mosquito distribution and disease transmission. GIS can analyze surveillance data from multiple sources, such as hospitals and laboratories, to identify outbreaks and monitor the effectiveness of control measures. In environmental epidemiology, GIS has been used to estimate environmental levels of contaminants and design exposure metrics, enhancing the understanding of the association between environmental factors and disease. Additionally, GIS can be used to assess the spatial distribution of mosquitoes and their preferred hosts, aiding in the design of more efficient control programs (Minakshi et al., 2020).

4.3 Risk assessment and management

GIS plays a critical role in risk assessment and management by identifying areas of high risk for mosquito-borne disease transmission. For example, GIS was used to map mosquito habitats and human populations at risk in North Carolina, helping to prioritize areas for emergency mosquito control during post-disaster scenarios (Mueller et al., 2022). In Poland, GIS facilitated the identification and mapping of mosquito breeding sites in irrigation fields, enhancing the efficacy and sustainability of control programs (Rydzanicz et al., 2011). GIS can predict disease distributions in areas lacking baseline data, guiding intervention strategies and resource allocation.

4.4 Public health interventions and decision making

GIS technology supports public health interventions and decision-making by providing valuable information for informed resource allocation and control measures. During the COVID-19 pandemic, GIS was used to visualize data on a map, informing the public about the virus's spread and aiding policymakers in making informed decisions (Ahasan et al., 2020). In the context of mosquito-borne diseases, GIS can generate risk maps for exposure to viruses like dengue, develop priority area classifications for vector control, and explore socioeconomic associations with disease risk (Eisen and Lozano-Fuentes, 2009). This enables public health officials to target interventions more effectively and improve overall disease management.

4.5 Evaluation of control measures

The evaluation of control measures is enhanced by GIS through the monitoring of intervention effectiveness and the identification of areas needing further attention. GIS can track changes in land use and climate, which affect mosquito distribution and disease incidence, providing insights into the drivers of transmission and the success of control strategies. In Malaysia, GIS was used to map the distribution of mosquito breeding sites and assess the impact of control measures on reducing malaria transmission risk (Ahmad et al., 2011). Additionally, GIS can integrate environmental data to understand the factors influencing mosquito populations and disease spread, aiding in the continuous improvement of control programs (Nuckols et al., 2004; Uzair and Tariq, 2023). By leveraging the capabilities of GIS, researchers and public health officials can enhance the surveillance, analysis, risk assessment, intervention, and evaluation phases of mosquito monitoring, ultimately leading to more effective control and prevention of mosquito-borne diseases.

5 Case Studies

5.1 Case study 1: urban mosquito monitoring

In urban environments, Geographic Information Systems (GIS) have been effectively utilized to monitor and manage mosquito populations. A notable example is the study conducted in Tucson, Arizona, where high-resolution aerial multispectral and LiDAR data were integrated to classify urban land cover and identify potential mosquito habitats. This study developed eight urban land-cover classes focusing on features such as water ponds, residential structures, and irrigated lawns, which are critical for mosquito breeding. The fusion of multispectral and LiDAR data significantly improved the accuracy of land cover classification, achieving a Kappa value of 0.88. This enhanced classification allows for better identification and management of mosquito habitats in urban settings, thereby aiding public health efforts to control mosquito-borne diseases (Hartfield et al., 2011).

5.2 Case study 2: rural mosquito monitoring and control

In rural areas, the application of drones has shown promise in identifying and managing mosquito larval habitats. A study in Kasungu district, Malawi, utilized high-resolution drone mapping to identify water bodies and aquatic vegetation, which are key mosquito breeding sites. The study employed both manual methods and geographical object-based image analysis (GeoOBIA) to classify these habitats. The GeoOBIA approach demonstrated high accuracy (median accuracy=0.98, median kappa=0.96) but required more processing time and technical expertise. The study found a significant relationship between larval presence and vegetation type, highlighting the potential of drone-acquired imagery to support mosquito control efforts in rural areas where malaria is endemic (Figure 1) (Stanton et al., 2020).

Through high-resolution images captured by drones, researchers can analyze the environmental characteristics of mosquito larval habitats in detail, such as types of water bodies and vegetation coverage. This data is crucial for formulating effective mosquito control strategies, especially in rural areas with high incidences of malaria. The application of drone technology in rural areas not only significantly enhances the efficiency and accuracy of identifying mosquito larval habitats but also provides important technical support for mosquito control measures. This research achievement provides strong empirical evidence for using drones in malaria prevention and control.

5.3 Case study 3: integrating GIS with community-based monitoring

Integrating GIS with community-based monitoring can enhance the effectiveness of mosquito control programs. In Pos Lenjang, Kuala Lipis, Pahang, Malaysia, a study combined field data with satellite image analysis and GIS techniques to map larval breeding habitats and malaria transmission risk areas. The study digitized geographical features such as rivers, streams, and residential areas, and overlaid them with entomological data. The resulting maps showed that more than 80% of Anopheles maculatus s.s. immature habitats were within 400 m of human settlements. This integration of GIS and community-based monitoring provides a rational basis for strategic planning and management of mosquito control at the national level (Ahmad et al., 2011).

5.4 Lessons learned from case studies

The case studies highlight several key lessons in the application of GIS for mosquito monitoring and control. The integration of high-resolution data, such as multispectral and LiDAR, significantly improves the accuracy of habitat classification and mosquito monitoring in urban areas (Hartfield et al., 2011). Drone technology offers a practical solution for identifying mosquito larval habitats in rural settings, although it requires technical expertise and processing time (Stanton et al., 2020; Mukabana et al., 2022). Combining GIS with community-based monitoring can provide valuable insights and enhance the effectiveness of mosquito control programs, particularly in mapping and managing breeding sites (Ahmad et al., 2011; Deleon et al., 2017). GIS-based approaches facilitate strategic planning and resource allocation, making mosquito control efforts more efficient and targeted. These lessons underscore the importance of leveraging advanced technologies and community involvement in developing comprehensive mosquito control strategies.

6 Challenges and Limitations of GIS in Mosquito Monitoring

6.1 Data quality and availability

One of the primary challenges in utilizing Geographic Information Systems (GIS) for mosquito monitoring is the quality and availability of data. Accurate and up-to-date data are crucial for effective mapping and analysis. However, data collection can be inconsistent, and the quality of data can vary significantly. For instance, in the study conducted in Pos Lenjang, Malaysia, the integration of field data with satellite images was essential for developing accurate maps of larval breeding habitats and malaria transmission risk areas. Similarly, the review of GIS applications in schistosomiasis control in China highlighted the need for reliable baseline data to predict disease distributions effectively (Fletcher-Lartey and Caprarelli, 2016). The heterogeneity in data sources and the lack of standardized data collection methods can hinder the accuracy and reliability of GIS-based mosquito monitoring systems (Aldosery et al., 2021).

6.2 Technical and financial constraints

The implementation of GIS technology in mosquito monitoring also faces technical and financial constraints. High-resolution satellite imagery and advanced GIS software can be expensive, limiting their accessibility, especially in resource-constrained settings. The study on malaria control in Delhi and Gujarat, India, demonstrated the utility of remote sensing data in assessing mosquito abundance and malaria receptivity, but also underscored the need for significant financial investment in GIS infrastructure and training (Deleon et al., 2017). Additionally, the complexity of GIS technology requires specialized knowledge and skills, which may not be readily available in all regions (Duncombe et al., 2012).

6.3 Interdisciplinary collaboration

Effective mosquito monitoring using GIS requires collaboration across multiple disciplines, including entomology, epidemiology, geography, and information technology. The integration of diverse expertise is essential for comprehensive data analysis and interpretation. The study in Pos Lenjang, Malaysia, emphasized the importance of combining entomological data with geographical features to enhance the understanding of mosquito distribution patterns (Ahmad et al., 2011). However, fostering such interdisciplinary collaboration can be challenging due to differences in terminologies, methodologies, and research priorities among disciplines (Aldosery et al., 2021). Ensuring effective communication and collaboration among various stakeholders is crucial for the success of GIS-based mosquito monitoring programs.

6.4 Privacy and ethical considerations

The use of GIS in mosquito monitoring also raises privacy and ethical concerns. The collection and analysis of spatial data can potentially infringe on individuals' privacy, especially when data are collected from residential areas. The study in Pos Lenjang, Malaysia, involved mapping mosquito breeding sites near human settlements, which could raise concerns about the privacy of the residents (Ahmad et al., 2011). Additionally, ethical considerations must be taken into account when using GIS data for public health interventions, ensuring that the benefits of such interventions outweigh any potential risks to individuals' privacy and well-being (Yang et al., 2005). Addressing these privacy and ethical issues is essential to maintain public trust and support for GIS-based mosquito monitoring initiatives.

7 Future Directions and Innovations

7.1 Advances in GIS technologies

The future of Geographic Information Systems (GIS) in mosquito monitoring is promising, with continuous advancements enhancing their capabilities. Recent developments include the integration of remote sensing and spatial statistics, which allow for more precise mapping and analysis of vector distribution and disease incidence (Uzair and Tariq, 2023). Additionally, the use of web-based GIS platforms has made it easier for policymakers and the public to access and utilize spatial data for decision-making (Javaid et al., 2023). These advancements are crucial for identifying high-risk areas and implementing targeted control measures effectively.

7.2 Integration with machine learning and AI

The integration of machine learning (ML) and artificial intelligence (AI) with GIS is revolutionizing mosquito monitoring. Machine learning models, such as Random Forest and Support Vector Machine, have been successfully used to predict disease outbreaks based on climatic factors (Figure 2) (Javaid et al., 2023). Deep learning algorithms have also been applied to community-science-based mosquito monitoring, achieving high accuracy in species identification using smartphone recordings (Khalighifar et al., 2021). Furthermore, AI tools are being developed to automate the identification of mosquito species from citizen science data, enhancing the scalability and efficiency of surveillance systems (Pataki et al., 2021; Carney et al., 2022).

The GIS platform in the image provides users with an interactive map that displays the risk levels in different regions. Users can view the risk status of specific areas by entering place names, and the map will show current climate data and disease transmission risks. This visualization not only facilitates policymakers and the public in understanding current health risks but also provides a scientific basis for formulating mosquito control and disease prevention measures.

7.3 Potential for real-time monitoring and predictive modeling

Real-time monitoring and predictive modeling are becoming increasingly feasible with the integration of GIS, ML, and AI. WebGIS-based systems enable real-time surveillance and response, providing timely data to control vector-borne diseases (Javaid et al., 2023). Predictive models that incorporate climatic data can forecast disease outbreaks, allowing for proactive measures to be taken (Pley et al., 2021). These technologies not only improve the accuracy of predictions but also facilitate the rapid dissemination of information to stakeholders, enhancing the overall response to mosquito-borne diseases.

7.4 Expanding applications to other vector-borne diseases

While GIS has been extensively used for mosquito-borne diseases like malaria and dengue, its applications are expanding to other vector-borne diseases. For instance, GIS has been used to map the distribution of tick-borne Lyme borreliosis and flea-borne plague, demonstrating its versatility in vector surveillance (Eisen, 2011). The integration of GIS with other data sources, such as environmental and climatic data, can provide a comprehensive understanding of the factors driving the spread of various vector-borne diseases, thereby improving control strategies (Diptyanusa et al., 2020). This expansion highlights the potential of GIS to address a broader range of public health challenges.

8 Concluding Remarks

Geographic Information Systems (GIS) have proven to be invaluable tools in the monitoring and control of mosquito populations and the diseases they transmit. The integration of GIS with remote sensing technologies has enabled the precise mapping of mosquito breeding sites, facilitating targeted control measures and improving the efficiency of vector management programs. For instance, GIS has been used to map mosquito breeding sites in malaria-endemic areas, enhancing the strategic planning and management of control efforts. Additionally, GIS has been employed to track and predict the spread of vector-borne diseases, such as dengue and malaria, by mapping the distribution of vectors and identifying high-risk areas for transmission. The use of GIS in combination with other technologies, such as deep learning and citizen science, has further expanded the capabilities of mosquito monitoring systems, making them more scalable and effective.

The application of GIS in mosquito monitoring has significant implications for public health policy. By providing detailed spatial and temporal data on mosquito populations and their habitats, GIS enables public health officials to make informed decisions about where to allocate resources and implement control measures. This targeted approach can lead to more effective and efficient use of limited resources, ultimately reducing the incidence of mosquito-borne diseases. For example, the use of GIS to map mosquito habitats in irrigation fields in Poland has led to more precise and timely aerial applications of larvicides, resulting in better control of mosquito populations and reduced use of chemical agents. GIS can enhance disaster preparedness by identifying areas at risk for mosquito outbreaks following events such as hurricanes, allowing for proactive control measures to be implemented.

Future research should focus on further integrating GIS with emerging technologies to enhance mosquito monitoring and control efforts. One promising area is the use of deep learning algorithms to automate the identification and classification of mosquito species from citizen-submitted photos, as demonstrated by the Mosquito Alert system. Additionally, research should explore the potential of combining GIS with genetic engineering techniques to create mosquitoes that are resistant to disease pathogens, thereby reducing the transmission of vector-borne diseases. Another important area of research is the development of more sophisticated models that incorporate environmental data, such as land use and climate patterns, to predict changes in mosquito distribution and disease incidence. Finally, there is a need for studies that evaluate the cost-effectiveness of GIS-based mosquito control programs to provide evidence for their broader adoption in public health policy.

The integration of Geographic Information Systems in mosquito monitoring and control represents a significant advancement in the fight against vector-borne diseases. By providing detailed spatial data and enabling targeted interventions, GIS has the potential to greatly improve the efficiency and effectiveness of mosquito control programs. As technology continues to evolve, the capabilities of GIS in this field will only expand, offering new opportunities for research and innovation. It is crucial for public health agencies to embrace these advancements and incorporate GIS into their vector control strategies to protect communities from the growing threat of mosquito-borne diseases.

Acknowledgments

Thanks to the reviewing experts for their suggestions on the manuscript.

Conflict of Interest Disclosure

Authors affirm that this research was conducted without any commercial or financial relationships that could be construed as a potential conflict of interest.

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