Journal of Mosquito Research 2024, Vol.14, No.3, 161-171 http://emtoscipublisher.com/index.php/jmr 164 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
RkJQdWJsaXNoZXIy MjQ4ODY0NQ==