Journal of Mosquito Research 2024, Vol.14, No.3, 161-171 http://emtoscipublisher.com/index.php/jmr 167 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
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