Mosquito-borne diseases positively correlate with water bodies and low-lying areas as they provide suitable platforms for breeding and transmission 48 of mosquito populations such as dengue and malaria. The change in land use types also provides shelter to the mosquito population's potential for mosquito-borne disease transmission 49 from one location to a new site. The land use / land cover map of Muktsar districts 30 m spatial resolution of Landsat 8 data classified into five land use/land cover classes based on the supervised classification in the GIS environment (Figure 2).

Several researchers have underlined the normalized difference vegetation index (NDVI) in the study of diseases spread by mosquitoes because mosquito-borne infections were shown to be more prevalent in areas with low terrain and forest cover 48. It is possible to describe the habitat suitability for various mosquito species using vegetation indices, particularly those based on the normalized difference vegetation index 50, 51, 52. Greenery is associated with more precipitation, resulting in a suitable environment 51 for mosquito habitats.

The relationship between normalized differential vegetation index 53 and mosquito 54 population is positively associated. Numerous remote-sensing satellites have bands in the red (R) and near-infrared (NIR) to calculate the normalized difference between vegetation index (NDVI), and most crucially, the NIR to red wavelength ratio is connected with absorbed photo-synthetically active light (APAR).The NDVI determines the vegetation index (VI) of the Muktsar district (Figure 3) employed to measure the amount of greenery. The near-infrared leaf scattering and the red chlorophyll absorption of the green leaf are normalized by NDVI. Red and near-infrared bands are, therefore, necessary.

Values -0.1 and 0.143 generally correspond to settlements; low yet positive values ranging between 0.144 to 0.264 reflect shrub and grassland, and high positive values, ranging between 0.387 to 0.507, depict dense vegetation.

Figure 3

Figure 4

The growth of mosquito larvae is usually greatly influenced by the surface temperature. The mosquito's feeding behavior is influenced by the air temperature and the land surface, as the land surface temperature impacts the vectors' ability to survive 55 and complete the life cycle. Extreme temperatures (too high above 40°C or too low temperature below 5°C) are not conducive for mosquitoes to survive, and so, in temperatures between 15°Cto 32°C 56, mosquitoes can be found suitable.

The internal structure of the leaf influences the reflectance in the NIR band, and the SWIR band reflects changes in the water content of the vegetation. NIR is useful for classifying vegetation, and SWIR is useful for assessing the moisture content of soil and vegetation. A darker location highlights an area with more water content in SWIR. The normalized differential moisture index ranges from - 1 to 1, with - 1 denoting extremely low/deficient moisture levels and 1 (one) denoting extremely high moisture levels.

Spatiotemporal fluctuations of water bodies can accumulate diseases related to vectors borne 57 and diffuse to nearby areas. Based on the reflectance bands, the normalized difference water index (NDWI) for identifying open water bodies 58, 59.

Normalized difference water index values over zero or below zero indicate built-up areas, while high positive values indicate the presence of water bodies (Figure 6).

The decomposed leaves from the vegetation form litter can produce high moisture content. They may provide an ideal habitat for mosquitoes 39 breeding, larval growth, and pupal development. Using the Landsat 8 dataset, the percentage of vegetation or PV value is determined (Figure 7). We used the following equation for the identical Landsat 8 NIR and RED Bands picture-

The normalized difference vegetation index's minimum and maximum values are denoted by NDVImin and NDVImax, respectively. PV ranges from 0 to 1 and has a positive value, where 0 or near 0 indicates an area not covered by vegetation. A value of 1 or close to 1 denotes an area heavily covered by vegetation.

The slope of an area also determines mosquito larval habitats 8, 60, 39 as water stagnant are a feature of less slope area. The locations with slopes of 0-1 depict water bodies and other flat lands. These regions have very gentle slopes and are suitable for mosquito behavior. Less than 1 to more than 17-degree variations exist in the district slope.

Low air temperature and high moisture in shadowed locations 39 are crucial in transmitting mosquito-borne diseases 8. The global digital elevation model SRTM 1 arc's 30 m resolution DEM reveals that the shadow areas in the northern-eastern to south-west directions do not receive direct sunshine and have excessive moisture content and a relatively high proportion of plants compared to other aspects.

Figure 8

Figure 9

Figure 10

Figure 11

Figure 12

Female Anopheles mosquitoes (the primary vector of malaria) can fly to 2 km, and Aedes mosquitoes (the primary vector of dengue virus) that reproduce in fresh stagnant water 61 can soar to 100–200 m, maximum 300–500 m which increases risk in urban areas due to the presence of wastewater points 55. Mosquito-borne illnesses like dengue are more common in places close to water bodies and areas with standing water 62. So, the risk zones for mosquito-borne disease are identified by considering the presence of water bodies. Accordingly, for proximate analysis, we identified the water bodies (small to large ponds, canals, and lakes) in the study area (Figure 11, Figure 12).

The Consistency Index (CI) and Consistency Ratio (CR): The Consistency Index (CI) and Consistency Ratio (CR) are considered to assess the outcome, decision-making error, and empirical bias during the process of assigning ranks. The summary of the weights of all 10 factors, along with CI and CR is given in Table 4. If the CI/index ratio for the resulting random matrix is found to be (0.1), the decision-making process and rank selection may be viewed as inconsistent 47. The consistency ratio of all the factors is found 0.0470. The outcome demonstrates that the estimated consistency ratio value is consistently smaller than 0.1 for all of the chosen parameters. Therefore, when a pairwise comparison matrix was created, it may be assumed that the rank selection was always an appropriate decision.

The moisture index, water index, and distance from water bodies have been providing the most significant risk of mosquito-borne diseases because these ranges of values have always been considered for offering suitable sites for mosquito breeding and growth, according to weightage calculated from pairwise comparison as shown in Table 4. A percentage of vegetation with a value of 0.387–0.507 and a vegetation index of 0.5907–1.068 is regarded as areas with a high risk of mosquito-borne diseases. Low land or land with no elevation was also discovered to be a significant risk factor for MBD outbreaks.

Comparison matrix of the chosen 10 parameters is built together with weightage to overlay (Table 4). Each component is not equally significant and does not provide an equally favorable environment for breeding. To decide to assign a risk rank to each element, prior studies and the environmental factors affecting the growth of Aedes and Anopheles were taken into account. The outcome indicates that, of all the chosen causal factors, proximity to water bodies carries the highest risk (0.2297 weight value). This indicates that proximity to water bodies is more at fault than other chosen factors for the development of mosquito-borne disease. With weights of 0.1593 and 0.1394, respectively, the next most important components are NDMI and NDWI. Since mosquitoes always need a comfortable temperature to thrive, land surface temperature (LST) has a substantial weight value of 0.1146. In accordance, the prevalence of mosquito-borne diseases in Muktsar district is significantly influenced by LULC, NDVI, vegetation proportion, land elevation, slope degree, and aspect, with weights of 0.0689, 0.401, 0.0435, and 0.0522, respectively. The consistency ratio was determined to be 0.0470, which is accepted for the comparison matrix.

A weighted overlay of the selected parameters is employed using the GIS tool to extract the mosquito-borne disease susceptible zones (Figure 13). The outcome demonstrates that there are only a few very high and very low vulnerable zones and that the majority of places fall into the intermediate MBD vulnerable zones. The northern portions of Muktsar, which cover Chak Shere Wala health block and areas along with Sirhind Feeder and Rajasthan Feeder canals covering Alamwala and Lambi health blocks, fall under a very high-risk zone for mosquito-borne diseases (areas shown (Figure 13) in circles).

 Mosquitoes' maturation times are influenced directly and indirectly by a comfortable temperature and humidity 6, 63. Various types of land cover, including waterlogged areas, water bodies, agricultural land that has become stagnant, and densely populated areas, have been identified as sensitive locations for mosquito-borne diseases 5. Numerous mosquito-endemic sites might have similar studies done using the same methods. Similar studies would aid researchers in selecting various local environmental, climatologically, and socioeconomic aspects as decision-making criteria.

Southeast and central parts have a very low incidence of MBDs, and some areas have no cases of these diseases, but the northeastern and southwestern areas have moderate to high susceptibility to MBDs. Therefore, several regions with a very high and high prevalence of these diseases were plotted to blend with the MBDs' final result (Figure 13). The outcome demonstrates that these reported areas are more uniform with the final layer and frequently merged with very high to high zones as derived from spatial analysis of multi-criteria based choice factors.

The susceptibility of areas and reported MBD’s cases are positively correlated with each other’s as shown in Figure 14.

To estimate the population at risk of mosquito-borne diseases in the Muktsar district, the study multiplied the derived mosquito-borne disease risk map raster and the district population density for the year 2011, using zonal statistics in ArcGIS environment to determine the population vulnerable to mosquito-borne disease in the district. The computation is also done at the health block level to get a clear distribution of the mosquito-borne disease risk.

Figure 15

The results show that over 60 percent (451100) of people in the district are actually at high risk of mosquito-borne disease infections, as illustrated in Table 5 and Figure 15. From the results, Chak Shere Wala and Alamwala health blocks have the highest number of population categorized as being at high risk of mosquito-borne disease at approximately 186464 (72 percent) and 113249 (66 percent) persons followed by Doda and Lambi health blocks with estimates of 89907 (49 percent) and 61480 (48 percent) persons. When looking towards the population having a moderate risk of mosquito-borne diseases, it is 67802 (37 percent) in Doda, 32506 (25 percent) in Lambi, 41403 (16 percent) in Chak Shere Wala, and 28161 (16 percent) in Alamwala health block. Apart from this, Chak Shere Wala has 30653 (12 percent) people, followed by Doda Health Block, which has a 27032 (14 percent) population characterized by low risk of mosquito-borne diseases.

AS contributed in methods used, GIS work, and writing, LTSG contributed conceptualization, methodology, framework of the paper and writing.

The author acknowledges Central University of Punjab, Bathinda, India for providing lab for carrying out the research.