Impact of urban heat island effect on dengue incidence: a remote sensing approach using thermal and high-resolution optical imagery

Abstract

Dengue fever, identified by the World Health Organization as a significant global health threat, is the fastest-spreading mosquito-borne viral disease transmitted by Aedes aegypti and Aedes albopictus mosquitoes. Annually, 100–400 million cases are reported, with over 14 million cases and 10,000 deaths in 2024 alone, highlighting the public health challenge of dengue, especially in tropical and subtropical urban areas. The Urban Heat Island (UHI) effect is a critical factor in dengue transmission, creating favorable conditions for Aedes mosquitoes. This study examines the impact of UHIs on dengue incidence at Thanjavur Municipal Corporation, Tamil Nadu, India, via remote sensing-derived Land Surface Temperature (LST) and epidemiological data from 2017 to 2023. High-resolution thermal and optical imagery was used to assess spatial variations in urban surface temperature, whereas dengue incidence patterns were analysed through hotspot detection and statistical analysis. The study also examines land use and land cover characteristics in modulating dengue risk. The findings revealed strong positive correlations between UHIs, built-up zones, and dengue hotspots. UHI analysis revealed that dengue incidence is highest in areas with moderate heat exposure, not in urban cores with extreme temperatures; only 30.2% of dengue cases occurred within UHI zones, suggesting that extreme temperatures (> 48 °C) may suppress Aedes mosquito survival. The land use analysis revealed that built-up areas (45.85%) were strongly correlated with dengue cases (?? = 0.822, p < 0.01), whereas vegetation (40.98%) and water bodies (2.82%) were negatively correlated, indicating their role in influencing dengue transmission. The findings underscore the critical influence of UHIs on dengue incidence and the need for targeted interventions, including dengue-sensitive urban planning focused on maintaining green spaces, ensuring proper drainage, and minimising water stagnation to balance vegetation benefits with vector control.

Background

Dengue fever has become a major global public health threat due to its rapid and widespread transmission. It is a mosquito-borne illness caused by the dengue virus (DENV) of the Flaviviridae family [1]. The World Health Organization (WHO) reports that nearly half the world’s population is at risk of dengue infection. From 2000 to 2022, reported dengue cases increased eightfold, now affecting approximately 129 countries. Urbanisation and global air travel have led to disease re-emergence in several regions, including Eastern Europe, where dengue has no historical presence [2,3,4,5,6]. In the 2000 s, dengue was perennial in only eight Indian states, but it is now prevalent year-round across all states [7, 8]. In 2024, over 14 million cases and 10,000 fatalities were reported globally(European Centre for Disease Prevention and Control, 2025). Dengue is considered an urban health issue, as its primary vectors, Aedes aegypti and Aedes albopictus mosquitoes. Aedes aegypti is adapted to anthropogenic environments and lives close to humans. Aedes albopictus can be found in vegetated and rural areas, where it feeds on animals and is an aggressive biter [4, 9]. Rapid urbanisation, climate and environmental changes, and increased global mobility have created suitable conditions for vector-borne diseases [10, 11]. High population density, land-use changes, and the urban heat island (UHI) phenomenon provide conducive environments for mosquito breeding and the spread of dengue [12,13,14].

Temperature is a crucial factor for dengue transmission dynamics and influences mosquito development, feeding behavior, and viral replication. The optimal temperature range for dengue transmission is 25 °C to 33 °C, increasing vector activity and virus propagation [8, 15]. Temperature variations impact the extrinsic incubation period (EIP), which decreases from 12 to 14 days to approximately 7 days under optimal conditions [16]. However, temperatures above 40 °C or below 10 °C inhibit mosquito survival. Aedes aegypti has broader thermal tolerance than Aedes albopictus and survives between 11 °C and 36 °C, although extreme temperatures negatively affect vector populations [15, 17]. These climatic factors contribute significantly to increased dengue risk in tropical and subtropical regions.

The effects of global warming have intensified the potential for transmission of dengue. A 1 °C increase in temperature leads to a 13% increase in relative infection risk, with tropical monsoon and humid subtropical climate zones being the most susceptible [18,19,20]. Urban heat islands exacerbate this risk by creating stable, elevated temperatures that sustain mosquito activity and viral spread.

Rainfall and temperature interactions play critical roles in transmission by increasing relative humidity, enhancing mosquito survival, feeding frequency, and replication [21, 22]. Viral replication peaks at 32 to 35 °C; however, mosquito longevity and feeding rates decline beyond this threshold, moderating transmission potential [23]. Evidence from tropical Malaysia suggests that minimum temperatures between 25.4 °C and 26.5 °C exhibit a strong, delayed correlation with increased dengue incidence, with pronounced effects after a 51-day lag [24].

India has four dengue virus types: DENV-1, DENV-2, DENV-3, and DENV-4 [7]. Individuals can be susceptible to dengue up to four times because of a lack of cross-immunity among serotypes. The second infection poses heightened risks due to an exaggerated antibody response from the primary infection. Recent years have seen DENV-2 strain dominance, while DENV-4 has established a niche in south India [25] Dengue remains a major public health concern in Tamil Nadu, with seasonal outbreaks and persistent risk, especially post-monsoon [26]. According to previous studies, all four serotypes of dengue virus are found in Thanjavur, recent studies indicate that DENV 1 and DENV 4 are more dominant in the region [27].

The combined effects of climate variability, urbanisation, and changing weather patterns underscore the need to study the spatiotemporal dynamics of dengue transmission and develop targeted mitigation strategies. As of the current decade, no specific vaccine or antibiotic has been identified for dengue prevention and control. This research is crucial for effective disease management and monitoring. Given the absence of definitive medical intervention, early detection and symptom recognition play pivotal roles in potentially reducing dengue mortality rates. Considering these complexities, exploring dengue dynamics according to regional climatological conditions in an urban context is critical.

Study area

Thanjavur is a rapidly developing Tier-2 city of Tamil Nadu with an administrative area covering an area of 36.33 sq. km. and a population of 2.3 lakhs as of 2011, which includes 51wards with a population density of 2,300/sq. km (Fig. 1 [28]).

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The city is a part of the Cauvery Delta and is situated at a distance of 314 km southwest of Chennai. The city is mainly a floodplain, and it is referred to as the Rice Bowl of Tamil Nadu owing to its fertile soil and high rice production. Characterised by a humid and tropical climate, Thanjavur experiences a mean maximum temperature that varies between 36.5 °C in June and 27.8 °C in May. The mean minimum temperature of the region varies between 22.1 °C and 27.1 °C in December. The relative humidity of the city usually ranges from 70 to 85%. The city has also come under the smart city program of the Government of India [29]. Most of the city’s annual rainfall is concentrated during the Southwest Monsoon (June–September) and Northeast Monsoon (October–December) seasons.

Data and Methodology

This study uses remote sensing data to examine UHIs and their potential link to dengue incidence (Table 1).

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Landsat 8 and 9 OLI data, with a 30 m resolution, are used to derive Land Surface Temperature (LST) and map UHI areas. NICFI data from PlanetScope, with a 4.77 m resolution, is employed for Land Use and Land Cover (LULC) classification using Object-Based Image Analysis (OBIA) in eCognition 9.4 software. The Google Earth Engine (GEE) code editor is utilised to analyse Landsat data for LST analysis and UHI detection. The air temperature of the study area collected during the time period of the analysis through ground monitoring stations was acquired from the Tamil Nadu Water Resources Department (WRD). Dengue incidence data acquired from the Directorate of Public Health and Preventive Medicine, Tamil Nadu (TNDPHPM), is integrated to assess the relationships among UHIs, land use, and dengue outbreaks, providing insights for climate-sensitive public health strategies.

Land surface temperature

The study utilised Landsat 8 and 9 satellite data to compute the LST and UHI over the study area. The analysis involved several key steps, including preprocessing, calculating indices, deriving emissivity, LST computation, and UHI classification.

Data acquisition and preprocessing

The Landsat 8 and Landsat 9 surface reflectance datasets were acquired for the study period between 2017 and 2023 and were extracted for the months of March to May. The datasets were filtered by date and study area geometry to include relevant images.

Radiometric corrections were applied by scaling factors to convert raw digital numbers to reflectance and temperature values. Scaling factors were applied for both the thermal and optical bands recommended by the USGS. We utilised band 10 for calculating the land surface temperature, as band 11, where out-of-field stray light is greater than that in the other band, needs absolute calibration; thus, band 11 is unreliable for LST calculations. The processed bands were clipped to the study area and combined to create median composite images.

Computation of NDVI

Normalised Difference Vegetation Index (NDVI) was calculated using the band 4 (Red) and band 5 (NIR) bands of the composite image.

$$\:\text{N}\text{D}\text{V}\text{I}=(\text{N}\text{I}\text{R}-\text{R}\text{E}\text{D})/(\text{N}\text{I}\text{R}+\text{R}\text{E}\text{D})\text{}$$

(1)

The computed NDVI values are used for correlation analysis with dengue cases in the study area.

Estimation of fractional vegetation cover (FVC)

To compute Fractional Vegetation Cover (FVC), the following equation was applied using the extracted minimum and maximum NDVI values:

$$\:FVC=(NDVImax-NDVIminNDVI-NDVImin)2$$

(2)

Calculation of emissivity

The emissivity was derived using the FVC values based on the empirical relationship:

$$\:Em=0.004\times\:FVC+0.986$$

(3)

This emissivity layer was crucial for correcting the thermal band and computing LST.

Land surface temperature calculation

The LST was computed from the thermal band and emissivity using the Planck’s law-based equation:

$$\:LST=1+\left(0.00115\times\:\:Tb/\:1.438\right)\times\:\text{ln}\left(Em\right)Tb-273.15$$

(4)

where Tb​ is the brightness temperature in Kelvin, Em is the emissivity, and the resulting LST is in Celsius.

UHI calculation

To assess UHI, the mean and standard deviation of LST across the study area were calculated [30]. UHIs and non-UHIs were identified by the range of LST determined by the following equations.

$$\:LST>\:\mu\:+0.5\times\:\delta\:$$

(5)

$$0<LST\leq\mu\;+\;0.5\;\times\;\delta$$

(6)

where \(\:\mu\:\) and \(\:\delta\:\) are the mean and standard deviation of LST in the study area, respectively.

The Land Surface Temperature and Urban Heat Island maps were exported in GeoTiff format from the Earth Engine platform, and then they were visualised using ArcMap 10.4 software. The UHI and LST metrics for each land cover class are calculated for further analysis.

Land use and land cover classification

LULC is a pivotal variable in analysing dengue risk within a region. Congested and mixed land use creates favorable conditions for Aedes aegypti growth, while increased greenery facilitates Aedes albopictus proliferation. LULC directly influences climatic and socioeconomic factors, impacting disease spread.

NICFI data with a 4.77-meter resolution were used for LULC analysis. OBIA technique was applied using eCognition Developer 9.4. Image segmentation was performed using the multiresolution segmentation algorithm, with shape and compactness criteria values of 0.6 and 0.7, respectively. Samples for five classes (Built-up, Vegetation, Barren, Agriculture, and Waterbody) were selected at the image object level to train the classification algorithm, producing the final classified image of Thanjavur city.

Geolocation of dengue incidence data

Dengue incidence data (2017–2023) were geocoded manually using Google Earth Pro. Geocoded locations were collected as kml files and converted to shapefile format for analysis in ArcMap 10.8.

Hotspot analysis

Spatial patterns of dengue incidence were analysed using the Getis-Ord Gi* method to identify hotspots and coldspots. Zscore is calculated using the Getis-Ord Gi* statistic. Significant hotspots have high case density and adjacent areas with high dengue cases. Coldspots have low case density with a -ve Z score [31]. Hotspot maps were interpolated using Inverse Distance Weighting (IDW) to depict spatial distribution of dengue cases.

Dengue and temperature Spatial-Statistical analysis

Spearman’s correlation coefficient examined the relationship between weekly ground-level temperature data and dengue cases. Overlay analysis of Land Surface Temperature (LST) and dengue case locations was performed using ArcMap 10.4. A 3 km resolution fishnet grid was generated, and minimum, maximum, and mean LST values were calculated for each cell to analyse spatial variations in temperature and their relationship with dengue cases. The same grid-based approach was applied to NDVI data. The UHI effect was visualised by overlaying dengue case locations on UHI maps.

These integrated statistical and geospatial analyses provided a comprehensive understanding of the interplay between temperature, vegetation, urbanisation, and dengue dynamics, offering valuable insights for public health interventions and urban planning.

Results

Dengue hotspots in Thanjavur City

The hotspot analysis (Fig. 2) of Thanjavur (2016–2023) illustrates the spatial distribution of dengue hotspots and coldspots across the district’s wards, providing insights into ward-level variations in dengue incidence. Hotspots, represented in red, are predominantly concentrated in the southern and southwestern wards (e.g., 36, 37, 38, 39, and 40), with areas of high statistical significance at 99% (dark red) and 95% (light red) confidence levels, indicating these as high-risk zones.

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Conversely, cold spots, represented in blue, dominate the northern region (e.g., 1, 2, 3, and 4), indicating areas with significantly lower dengue cases at 99% (dark blue), 95% (medium blue), and 90% (light blue) confidence levels. Non-significant areas, highlighted in yellow, suggest neutral or mixed patterns of dengue incidence. The overlay of dengue case locations further emphasised the correlation between high case densities and hotspot zones. This analysis, supported by ward-level demarcations, highlights the need for targeted interventions in high-risk zones, while cold spot areas may provide insights into effective control measures or natural environmental barriers limiting disease transmission.

LU/LC characteristics in the study area

As shown in Fig. 3, the land use analysis using the OBIA technique reveals that the built-up area constitutes the largest proportion of the total classified area, accounting for approximately 45.85% of the total 31.7336 sq. km. This indicates significant urbanisation within the region. Vegetation, which covers around 40.98%, represents the second-largest category, highlighting the presence of green spaces despite urban expansion.

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Agriculture occupies about 9.24%, reflecting the contribution of agricultural practices to the land use profile. Waterbodies cover a relatively small proportion at 2.82%, underscoring the limited surface water resources. Finally, barren land constituted the smallest share, at only 1.10%, suggesting minimal unused or degraded land in the area. These proportions highlight the dominance of urban and vegetative land cover, providing insights into a region’s landscape dynamics and potential areas for sustainable development and resource management.

Time series analysis of land surface temperature

The Land Surface Temperature for Thanjavur city from 2017 to 2023 shows a general increase in both maximum and minimum temperatures, as represented in Fig. 4.

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The maximum LST values rose from 53.03 °C in 2017 to a peak of 58.72 °C in 2020 before slightly decreasing to between 52.97 °C and 57.25 °C in the following years. The minimum LST values exhibited a fluctuating trend, ranging from 32.26 °C to 37.67 °C, with an overall upward trend. These results indicate that Thanjavur has experienced progressively warmer surface temperatures over the seven-year period.

The analysis reveals a strong relationship between temperature and dengue incidence, with the highest number of dengue cases observed between 44 °C and 48 °C (421 and 412 cases, respectively). The findings suggest that temperatures within this range are optimal for mosquito breeding and the transmission of the virus. Conversely, temperatures below 44 °C (22 cases) and above 48 °C (92 cases) show significantly fewer dengue cases. The 44 to 46 °C and 46 to 48 °C ranges, in particular, may facilitate ideal conditions for mosquito breeding, accelerating the mosquito’s lifecycle and increasing the rate of virus replication, leading to a higher incidence of dengue cases. These findings indicate that moderate warmth within this range plays a critical role in facilitating dengue outbreaks.

Surface air temperature dynamics

The maximum weekly temperature recorded in the study area between 2016 and 2023 was 40.82 °C, which was observed during the 17th week of 2017, whereas the minimum weekly temperature was 21.26 °C, which was recorded in the 3rd week of 2021 during the winter. The highest daily maximum temperature was 41.75 °C, recorded on April 25, 2017, whereas the lowest daily temperature was 19.60 °C on December 3, 2020. The greatest diurnal temperature variation was 12.80 °C on March 5, 2019, while the lowest variation was 1.20 °C on December 4, 2017.

Between 2016 and 2023, temperatures above 40 °C were recorded for a total of 67 days, indicating unsuitable conditions for dengue transmission owing to extreme heat limiting mosquito survival. Additionally, 903 days experienced temperatures between 36 °C and 39 °C, suggesting limited suitability; while the virus incubation period may shorten within this range, high temperatures could also reduce the survival and activity of dengue mosquitoes. On the remaining days, temperatures ranged between 25 °C and 36 °C, which falls within the optimal range for dengue transmission, providing optimal conditions for Aedes aegypti survival, virus replication, and disease spread.

Correlation analysis with factors of dengue transmission

Correlation analysis at a 500-meter grid resolution revealed significant associations between land use, vegetation indices, temperature, and dengue incidence. Built-up areas exhibited a strong positive correlation with dengue cases (?? = 0.822, ?? < 0.01), indicating urbanisation as a primary factor due to high population densities and artificial water storage systems facilitating mosquito breeding. Agricultural areas (?? = −0.558, ?? < 0.01) and water bodies (ρ = −0.213, ?? < 0.01) demonstrate negative correlations, suggesting rural and water-abundant regions may have lower dengue transmission. Barren land is moderately positively correlated (ρ = 0.455, ?? < 0.01), indicating potential contributions to mosquito migration from adjacent urban zones. NDVI metrics demonstrate significant negative correlations with dengue cases, particularly for minimum NDVI (ρ = −0.682, ?? < 0.01) and mean NDVI (ρ = −0.631, ?? < 0.01), suggesting areas with sparse vegetation tend to have higher dengue cases. The weaker negative correlation for maximum NDVI (ρ = −0.207, ?? < 0.01) implies dense vegetation in urban settings may still support transmission. LST metrics are positively correlated with dengue cases, particularly minimum LST (ρ = 0.328, ?? < 0.01) and mean LST (ρ = 0.347, ?? < 0.01), indicating sustained warm temperatures enhance mosquito activity and virus replication. Surface air temperature variables exhibit significant relationships, with a moderate negative correlation between maximum temperature and dengue cases (ρ = −0.293, p < 0.01), suggesting extreme heat suppresses Aedes aegypti activity, while weak negative correlations with minimum (ρ = −0.200, p < 0.01) and average temperatures (ρ = −0.256, p < 0.01) indicate moderate thermal conditions are more conducive to dengue transmission. These findings underscore the complex interplay between urbanisation, vegetation cover, and temperature in shaping dengue dynamics.

Transmission dynamics of dengue in association with urban heat island

The analysis of UHI effects in Thanjavur city reveals a significant spatial overlap between UHI zones and built-up areas, with important implications for vector-borne disease transmission.

Approximately 8.6 sq. km, accounting for 27% of the city’s total area, falls under UHI zones. Of these, 5.5 sq. km lies within built-up regions, representing 17% of the city’s area. Notably, the remaining UHI zones predominantly border built-up regions, suggesting a strong spatial association between urbanisation and elevated land surface temperatures (LST). The UHI zones exhibit LST values exceeding 48 °C, substantially higher than the average maximum surface air temperature of approximately 34 °C, a difference of 14 °C.

The association between UHIs and dengue incidence during 2017–2023 highlights intriguing dynamics. Out of 1,222 recorded dengue cases, only 369 cases (30.2%) were located within UHI zones, indicating that elevated temperatures in UHI areas may hinder the transmission of the dengue vector Aedes aegypti (Fig. 5). This finding aligns with existing research suggesting that extreme temperatures can reduce vector survival and reproductive rates. Conversely, warmer environments outside extreme heat thresholds, such as those neighboring UHI zones, appear more conducive to vector growth and disease transmission.

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Discussion

This study examines the UHI’s effect’s role in dengue incidence within Thanjavur City Corporation, Tamil Nadu. The findings elucidate the impact of urban-induced temperature variations on disease transmission, emphasising the associations among land surface temperature (LST), built-up areas, and dengue hotspots. Spatial analysis revealed a strong correlation, reinforcing that urban environments facilitate Aedes mosquito proliferation and dengue virus persistence, consistent with prior research [14, 32, 33].

Temperature is crucial in dengue transmission, with optimal conditions between 27 °C and 35 °C, while extreme heat above 36 °C limits vector survival and reproduction [15, 17]. The observed negative correlation between maximum temperature and dengue incidence supports this pattern. LST values range between 44 °C and 48 °C, exceeding conventional air temperature thresholds for dengue transmission [15]. This inconsistency arises from the divergence between LST and air temperature in urban environments, where heat retention by built surfaces, reduced vegetation, and limited evaporative cooling contribute to elevated LST [34,35,36].

While core UHI zones exhibit significantly elevated LST values, recording only 30.2% of dengue cases, areas bordering UHI zones, where temperatures remain elevated but below extreme levels, appear to foster conditions conducive to Aedes aegypti proliferation and dengue transmission. UHI zones may experience extreme heat, reducing mosquito survival, but localised microclimates can provide suitable conditions for vector persistence. These findings underscore the complex interplay between urbanisation, temperature, and disease dynamics. Incorporating localised temperature variations into vector control strategies can enhance disease risk mapping and improve targeted interventions in urban settings affected by the UHI effect.

Land use patterns shape dengue risk, with built-up areas exhibiting a strong positive correlation with dengue incidence in Thanjavur. Urban expansion contributes to elevated temperatures and fosters conditions conducive to Aedes mosquito breeding through artificial habitat proliferation. Spatial analysis of dengue hotspots indicates that the most affected areas are predominantly residential zones undergoing rapid urbanisation, characterised by newly developed built-up spaces and active construction sites. This trend aligns with the city’s growth trajectory, driven by major educational institutions and proximity to the urban core.

Despite the concentration of cases in expanding urban areas, the core city region also recorded significant dengue cases throughout the study period. This suggests that while urban growth and construction activities contribute to increased vector breeding opportunities, established urban centers remain vulnerable due to high population density, inadequate waste management, and water storage practices that facilitate mosquito proliferation. These findings are consistent with those of previous studies, which highlight the role of construction sites, household characteristics, and population density as key determinants of dengue transmission in urban settings [37,38,39].

Conversely, agricultural areas and water bodies exhibit negative correlations with dengue incidence, which is likely due to the ecological unsuitability of these land use types for Aedes mosquito breeding. Aedes aegypti, the primary dengue vector, favours artificial containers and shaded habitats typically found in urban environments. While Aedes albopictus, a secondary dengue vector can breed in vegetated and peri-urban areas, it generally avoids large agricultural fields and open water bodies [40]. The moderate correlation observed with barren land suggests that such areas, often situated near urban built-up zones or at urban fringes, may provide conducive conditions for mosquito breeding due to the presence of water-holding containers or other artificial habitats.

The role of vegetation in dengue transmission highlights complex interactions between urbanisation, land use, and vector ecology. The negative correlation between the NDVI and dengue incidence aligns with the literature suggesting that areas with sparse vegetation favor mosquito breeding and human‒mosquito interactions [31, 41]. However, dengue cases in higher NDVI regions indicate vegetation is not a definitive protective factor [42]. Urban greenery can create conditions supporting Aedes mosquito survival, especially with artificial water storage and inadequate waste disposal [43, 44] High vegetation in peri-urban areas may facilitate mosquito dispersion. Aedes albopictus adaptability to vegetated environments allows it to thrive in shaded, humid green spaces, sustaining transmission in higher NDVI areas.

Moreover, rainfall patterns influence mosquito breeding habitats by increasing stagnant water availability. The rainfall data collected from the Department of Water Resources, Government of Tamil Nadu, indicate that, between 2017 and 2023, rainfall trends in the region showed notable interannual variability, with significantly high precipitation observed in 2021 and 2022. While 2023 also recorded substantial rainfall, the corresponding decline in dengue cases suggests the influence of other moderating factors such as improved public health interventions (Fig. 6). Although rainfall was not directly analysed in this study, it remains a key environmental determinant of mosquito breeding conditions, particularly through the creation of stagnant water habitats during wet weeks.

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In addition to the above factors, the city also generates about 105 tons of municipal solid waste (MSW) daily, managed by the Thanjavur City Municipal Corporation (TMC) across 14 zones and 51 wards. Waste is transported to a designated disposal site at Srinivasapuram, approximately 5 km from the city center. Although routine waste collection systems are in place, current practices largely involve mixed waste collection with limited segregation at source [45]. This leads to accumulation of non-biodegradable materials such as plastic containers, metal cans, and other discarded items in the urban environment, particularly during the monsoon season. When exposed to rainfall, these materials can retain stagnant water and create favorable microhabitats for Aedes mosquitoes, the primary vectors of dengue.

These findings have implications for climate change adaptation and mosquito control. As urban areas grow, UHI effects intensify, necessitating strategies to reduce high temperatures and mosquito breeding sites. Approaches include improving water and waste management, promoting heat-reflective buildings, and using remote sensing with public health monitoring to identify at-risk areas on the basis of temperature and land use patterns.

While increasing urban greenery can lower surface temperatures, careful implementation is crucial. Certain plants can create environments conducive to Aedes mosquito breeding. Poorly maintained green spaces, water-holding plants, and artificial containers can support mosquito populations, particularly Aedes albopictus. Dengue-sensitive urban planning should focus on effective green space maintenance, proper drainage, and minimising water stagnation to balance vegetation benefits with vector control.

Limitations of the study

This study’s limitations include the availability of satellite-derived LST data influenced by cloud cover and the absence of nighttime LST data, limiting the understanding of how diurnal temperature variations affect mosquito activity. Although air temperature data were incorporated, the lag effects of climatic variables on dengue transmission were not analysed. Socioeconomic factors, including population density, housing conditions, and healthcare access, were not explicitly examined despite their role in shaping dengue vulnerability. Ongoing research extends this study to explore the lag effects of climatic parameters along with socioeconomic and environmental factors, providing a more comprehensive understanding of dengue risk and supporting targeted intervention strategies.

Conclusion

This study underscores the critical influence of the UHI effect on dengue incidence, revealing a strong interlink between LST, urban expansion or built-up, and disease hotspots. While moderate urban warming promotes the proliferation of Aedes mosquitos, extreme temperatures in core UHI zones may suppress vector survival, leading to increased dengue incidence in fringe areas. The role of land use patterns is evident, with built-up and rapidly urbanising zones exhibiting a strong positive correlation with dengue cases, whereas agricultural areas and water bodies show negative associations. Although vegetation generally mitigates urban heat, its impact on mosquito habitats remains complex, as shaded, humid conditions in green spaces can sustain Aedes populations, particularly Aedes albopictus, in peri-urban settings.

These findings emphasise the need for integrated urban planning that balances heat mitigation with vector control. Strategies should prioritise effective water management, sustainable green infrastructure, and the use of remote sensing tools for risk mapping. While expanding urban greenery can contribute to cooling, maintaining vegetation in a dengue-sensitive manner is crucial to prevent inadvertent mosquito proliferation. In conclusion, future research on the relationship between urban heat and dengue should focus on analyses across diverse urban and rural areas, integrating high-resolution remote sensing with real-time epidemiological data to enhance predictive modeling and early warning systems. Interdisciplinary research, which combines climate science, socioeconomic assessments, and public health strategies, is essential for understanding the differences between rural and urban settings and developing sustainable, climate-resilient policies for effective vector control and disease prevention. These efforts will be useful in identifying region-specific risk factors, improving targeted interventions, and enabling proactive strategies to reduce dengue transmission in both urban and rural environments.

Data availability

The data that support the findings of this study are available from the Directorate of Public Health and Preventive Medicine, Tamil Nadu, but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. Data are however available from the authors upon reasonable request and with permission of the Directorate of Public Health and Preventive Medicine.

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