Geospatial Insights into Dengue: Climate Trends and Epidemic Clusters in Manipur

Geospatial Insights into Dengue: Climate Trends and Epidemic Clusters in Manipur | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Geospatial Insights into Dengue: Climate Trends and Epidemic Clusters in Manipur Romen Meitei Lourembam, Indira Devi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6274739/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Dengue fever, a vector-borne disease transmitted by Aedes mosquitoes, has become a growing public health concern in India, particularly in Manipur. The interplay between climate variables—temperature, precipitation, and vegetation cover—significantly influences dengue transmission dynamics. This study employs geospatial analytics and machine learning techniques to analyse the spatiotemporal trends of dengue outbreaks in Manipur from 2018 to 2024. Dengue case data, obtained from the National Center for Vector Borne Diseases Control, were integrated with climate variables extracted from Google Earth Engine (GEE). The administrative boundary of Manipur was validated using Folium, ensuring accurate geospatial analysis. Temperature, precipitation, and NDVI data were processed to assess their correlation with dengue incidence. K-Means clustering was applied to classify districts into epidemiological risk zones, identifying dengue hotspots, while hierarchical clustering was used to analyze seasonal outbreak patterns. The findings revealed significant correlations between dengue incidence and climate factors, with moderate positive associations observed between precipitation and dengue cases, highlighting the role of monsoon-driven vector proliferation. Results indicated that high-risk districts experienced persistent outbreaks, emphasizing the need for targeted vector control strategies. The use of Google Colab enabled efficient data processing and visualization, leveraging cloud-based computational tools for advanced analytics. This study underscores the importance of integrating climatic parameters into dengue surveillance and predictive modelling to enhance early warning systems. The insights gained can inform public health policies, facilitating proactive interventions and resource allocation to mitigate dengue transmission in Manipur. Dengue Epidemiology Geospatial Analysis Climate Trends Dengue Clusters Manipur Time-Series Analysis Environmental Factors Temperature and Precipitation Vector-Borne Diseases Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 1 Introduction Dengue fever, a mosquito-borne viral infection, has emerged as a significant public health challenge in many tropical and subtropical regions, including India. The disease, primarily transmitted by Aedes aegypti and Aedes albopictus mosquitoes, has exhibited increasing incidence due to rapid urbanization, climate change, and inadequate vector control measures (Bhatt et al. 2013 ). Manipur, a northeastern state of India, has experienced a rise in dengue cases over the years, necessitating a comprehensive understanding of its epidemiological patterns and associated environmental factors. Climate variables such as temperature, precipitation, and vegetation cover significantly influence the breeding and survival of Aedes mosquitoes, thereby impacting dengue transmission dynamics (Morin, Comrie, and Ernst 2013). Studies have shown that rising temperatures and altered precipitation patterns create optimal conditions for mosquito proliferation, extending their seasonal activity and geographical distribution (Liu-Helmersson et al. 2014). Similarly, increased vegetation density, as represented by the Normalized Difference Vegetation Index (NDVI), has been linked to mosquito habitat suitability and disease outbreaks (Eisen and Lozano-Fuentes 2009). Analysing the interplay between these climatic factors and dengue incidence is crucial for developing predictive models and effective intervention strategies. Recent advancements in geospatial and machine learning techniques have enabled a more refined approach to studying disease transmission patterns. The integration of geospatial data with epidemiological records facilitates the identification of high-risk areas, allowing for targeted public health interventions(Kraemer et al. 2015). Clustering methods such as K-Means and hierarchical clustering provide insights into spatial and temporal trends of dengue outbreaks, aiding in the formulation of early warning systems and vector control measures (Gubler 2012 ). Previous studies have successfully employed machine learning techniques to classify dengue risk zones based on climatic and epidemiological parameters, highlighting the potential of data-driven decision-making in disease management (Cristobal et al. 2025 ). This study aims to analyse the spatiotemporal trends of dengue outbreaks in Manipur by integrating dengue case data with climate variables using geospatial analytics and machine learning techniques. Specifically, the research focuses on (i) validating the Manipur administrative boundary shapefile for accurate geospatial analysis, (ii) extracting and processing climate data from Google Earth Engine (GEE), (iii) identifying dengue hotspots using K-Means clustering, and (iv) analysing temporal outbreak trends through hierarchical clustering. By leveraging cloud-based computational tools such as Google Colab, this study ensures efficient data processing, visualization, and model implementation (Google Colaboratory). The findings will contribute to the enhancement of dengue surveillance systems, facilitate targeted vector control efforts, and support policymakers in designing evidence-based public health strategies for dengue mitigation in Manipur. 2. Methodology 2.1 Data Collection and Preprocessing The dataset used in this study comprises dengue case reports from various districts of Manipur, obtained from the National Center for Vector Borne Diseases Control, Manipur. It includes monthly total dengue cases recorded from 2018 to 2024, along with serological test results (IgM Mac ELISA and NS1 Antigen ELISA positivity rates). The dataset was processed using Python in Google Colab (McKinney 2010 ). To integrate climate data, geospatial processing and validation of administrative boundaries were performed before data extraction. 2.2 Validation of the Manipur shapefile using Folium in Google Colab The Manipur administrative boundary shapefile was validated using the Folium library in Google Colab. Google Earth Engine (GEE) was authenticated and initialized, and the shapefile was retrieved and converted into GeoJSON format for visualization (Gorelick et al. 2017 ). Using Folium, the correctness of the boundaries was verified before extracting climate variables relevant to dengue outbreaks. Temperature (°C) data was derived from MODIS Land Surface Temperature (MOD11A2), precipitation (mm) from CHIRPS, and NDVI from the MODIS Vegetation Index dataset (MOD13Q1) (Cheshire 2015 ). These climate variables were spatially aggregated over district boundaries using zonal statistics to align with the dengue case dataset. 2.3 Data Normalization and Standardization To ensure temporal consistency, the "Months" column was mapped to numerical values and combined with the "Year" column to create a "Time" variable in "YYYY-MM" format. Missing values were handled using descriptive statistics, and numerical data types were standardized where necessary. Outliers were managed using the interquartile range (IQR) method and Winsorization. Climate variables (Temperature, Precipitation, and NDVI) were standardized using StandardScaler from scikit-learn. Time-series trends were visualized using seaborn's line plots, and a correlation heatmap was constructed to assess relationships between climate variables and dengue cases. 2.4 Predictive Modelling and Statistical Analysis A machine learning-based Linear Regression model was developed to predict dengue cases using climate variables. The dataset was split into training (80%) and testing (20%) sets using train_test_split from scikit-learn. The model was evaluated using: Mean Squared Error (MSE): Measures average squared differences between actual and predicted values. R-squared (R²) Score: Assesses model explanatory power. To evaluate the impact of climate variables on dengue incidence, an Analysis of Covariance (ANCOVA) model was applied using the Ordinary Least Squares (OLS) method from statsmodels: Total cases \(\:={\beta\:}_{^\circ\:\:}+\:{\beta\:}_{1}\:\left(Temperature\right)+\:\:{\beta\:}_{2}\:\left(Precipitation\right)+\:\:{\beta\:}_{3}\left(NDVI\right)+\:\epsilon\:\) An ANOVA table was generated to determine statistical significance. 2.5 Hotspot Analysis of Dengue Cases Using K-Means Clustering Dengue case data was aggregated at the district level, and z-score normalization was applied using StandardScaler (Pedregosa et al. 2011 ). K-Means clustering classified districts into three epidemiological clusters: low-risk, medium-risk, and high-risk. The optimal number of clusters (k = 3) was determined based on epidemiological insights. The results were visualized with a district-wise bar chart, aiding in the identification of dengue hotspots for targeted public health interventions (Jain 2010 )(Anselin 1995 ). 2.6 Hierarchical Clustering for Temporal Analysis To explore temporal trends, hierarchical clustering grouped districts based on monthly case trends. Ward’s linkage method was used to minimize intra-cluster variance, and a dendrogram was generated to visualize relationships among districts with similar seasonal outbreak patterns (Ward Jr 1963 )(Kaufman and Rousseeuw 1990 ). 2.6 Computational Environment and Tools All analyses were conducted in Google Colab using Python 3.13. Data processing was performed with Pandas and NumPy, geospatial handling with GeoPandas and Folium, and visualization with Matplotlib and Seaborn. Climate data extraction utilized Google Earth Engine (ee), and statistical modelling was conducted with SciPy and Statsmodels. Machine learning implementations leveraged Scikit-learn. This structured framework integrates inferential statistics and predictive modelling to assess the influence of climate variability on dengue outbreaks. 3 Results 3.1 Validation of the Manipur shapefile The overlay analysis of the Manipur GeoJSON shapefile on the base map confirmed the spatial accuracy of district boundaries within the defined extent of Manipur before extracting climate data, as illustrated in Fig. 1. 3.2 Acquisition of climate trends across Manipur from 2018 to 2024 The MODIS LST dataset showed an average annual temperature variation between 18°C and 30°C, with peaks in April to June and a decline during December to February Fig. 2 . The CHIRPS dataset indicated a monsoon-driven trend, with the highest rainfall recorded between June and September and minimal precipitation from November to February as shown in Fig. 3. The MODIS NDVI dataset exhibited seasonal fluctuations, with higher vegetation greenness during the post-monsoon season (September to November) and lower values during dry months (January to March) as shown in Fig. 4. 3.2 Correlation analysis between dengue cases and climate data The correlation matrix highlights the interdependencies among dengue cases, serological test results (IgM Mac ELISA and NS1 Antigen ELISA), and climate variables (temperature, precipitation, and NDVI) as shown in Fig. 5 . From an epidemiological perspective, a strong positive correlation (0.81) was observed between IgM Mac ELISA Positive cases and Total Cases, confirming IgM-based detection as a reliable marker for dengue surveillance. Similarly, NS1 Antigen ELISA Positive cases exhibited a high correlation (0.82) with Total Cases, reinforcing its diagnostic significance. Additionally, a moderate correlation (0.56) between Time and NS1 Antigen ELISA Positive cases suggests seasonal trends in antigen detection. Climatic factors also demonstrated significant correlations. Temperature (°C) and Precipitation (mm) showed a moderate positive correlation (0.63), reflecting seasonal patterns where higher temperatures are often accompanied by increased rainfall. NDVI, an indicator of vegetation health, negatively correlated with temperature (-0.47) and precipitation (-0.53), suggesting that extreme weather conditions, such as monsoonal flooding, may reduce vegetation greenness. Regarding disease-climate interactions, Total Cases exhibited a weak positive correlation with precipitation (0.22) and a negligible negative correlation with temperature (-0.06). This indicates that while rainfall may contribute to vector breeding, temperature alone does not strongly influence dengue case numbers. Interestingly, NDVI showed a mild positive correlation (0.38) with Total Cases, suggesting that vegetation dynamics may influence vector habitat suitability. Overall, this correlation analysis underscores the complex interactions between climate variability and dengue transmission. These findings emphasize the importance of incorporating climatic parameters into vector-borne disease surveillance and predictive modelling for more effective public health interventions. 3.3 Analysis of Covariance (ANCOVA) To assess the impact of climate variables on dengue incidence, an Analysis of Covariance (ANCOVA) was performed, considering Temperature (°C), Precipitation (mm), and NDVI as independent variables and Total Cases as the dependent variable. The ANCOVA model revealed significant effects of Precipitation (F = 12.87, p = 0.0006) and NDVI (F = 34.79, p < 0.0001) on dengue cases as shown in Table 1 . However, Temperature (F = 0.36, p = 0.551) did not show a statistically significant association. The residual mean square error (MSE) was 549.30, indicating variability in dengue cases not explained by the predictors. These results suggest that Precipitation and NDVI play crucial roles in influencing dengue transmission, possibly by affecting mosquito breeding conditions and vegetation density, respectively. The lack of significance in Temperature might indicate that other environmental or socio-epidemiological factors moderate its effect. Table 1 ANCOVA results. Predictor df Sum Sq Mean Sq F-value p-value Temperature (°C) 1 197.23 197.23 0.359 0.551 Precipitation (mm) 1 7070.03 7070.03 12.87 0.0006 NDVI 1 19112.74 19112.74 34.79 < 0.0001 Residual 68 37352.20 549.30 3.4 Clustering analysis of dengue cases across districts in Manipur The K-Means clustering analysis of dengue cases across districts in Manipur reveals significant spatial variability in disease burden. Imphal West exhibits the highest number of total dengue cases, forming a distinct high-risk cluster (red), followed by Imphal East (green), which also shows a considerable case load. The remaining districts (blue) report relatively low case counts, indicating a lower dengue burden. This clustering pattern suggests that urbanized regions, particularly Imphal West and Imphal East, serve as major hotspots for dengue transmission, potentially due to higher population density and favourable vector breeding conditions. 3.5 Regression analysis of environmental factors influencing dengue cases The regression analysis illustrates the relationship between dengue cases and three environmental factors: temperature, precipitation, and NDVI. The first plot indicates a weak negative correlation between temperature and dengue cases, suggesting that higher temperatures may not necessarily lead to increased transmission. In contrast, precipitation exhibits a slight positive correlation with dengue cases, implying that increased rainfall may create favourable breeding conditions for vector proliferation. The strongest positive correlation is observed between NDVI (vegetation index) and dengue cases, indicating that higher vegetation density may be associated with an increased risk of dengue transmission, possibly due to the availability of breeding habitats for mosquitoes. 3.6 Hierarchical Clustering for Temporal Analysis The hierarchical clustering analysis grouped districts based on similar monthly case patterns, highlighting three major outbreak trends. Early-onset outbreaks (May–July) were observed in districts such as Thoubal and Kakching, where early monsoon rainfall contributed to vector proliferation. Peak-season outbreaks (August–October) were observed in high-burden districts, including Imphal West and Churachandpur, where peak transmission occurred during the late monsoon and early post-monsoon periods. Late-season outbreaks (November–December) were recorded in a few districts, such as Bishnupur and Senapati, which exhibited prolonged transmission beyond the usual peak period. 3.7 Time-series analysis of dengue cases and temperature trends The time-series analysis of dengue cases and temperature trends from 2019 to 2025 shows a seasonal pattern in dengue outbreaks, with distinct peaks occurring annually as shown in Fig. 9 . These peaks indicate a cyclical trend in dengue cases, likely influenced by seasonal factors. The temperature trend remains relatively stable with slight fluctuations, suggesting that while temperature may have some influence, other environmental or socio-ecological factors contribute significantly to dengue outbreaks. The periodic surges in dengue cases highlight the need for targeted preventive measures during high-risk seasons. 4 Discussion The analysis of dengue case trends in Manipur reveals significant spatial and temporal variations, influenced by climatic factors and urbanization patterns. The dendrogram analysis indicates that Imphal West exhibits the highest cluster distance, signifying a markedly higher dengue burden compared to other districts. This aligns with findings that urbanization facilitates the proliferation of Aedes mosquitoes , thereby increasing dengue transmission(Kolimenakis et al. 2021 ). Climatic variables play a crucial role in dengue dynamics. The MODIS LST dataset indicates annual temperature variations between 18°C and 30°C, with peaks from April to June and declines from December to February. The CHIRPS dataset shows monsoon-driven rainfall trends, with maximum precipitation between June and September. The MODIS NDVI dataset reflects higher vegetation greenness during the post-monsoon season (September to November) and lower values during the dry months (January to March). These climatic patterns are consistent with the seasonal nature of dengue transmission, where increased rainfall and temperature create favourable breeding conditions for Aedes mosquitoes (Xu, Xu, and Wang 2024). The correlation analysis underscores the complex interplay between climatic factors and dengue incidence. A moderate positive correlation (0.63) between temperature and precipitation suggests that higher temperatures are often accompanied by increased rainfall, both of which can enhance mosquito breeding habitats. The negative correlations of NDVI with temperature (-0.47) and precipitation (-0.53) may indicate that extreme weather conditions, such as heavy rainfall, can reduce vegetation greenness due to flooding or other environmental changes. The weak positive correlation between total dengue cases and precipitation (0.22) implies that while rainfall contributes to vector breeding, other factors also play significant roles in transmission dynamics. The mild positive correlation between NDVI and total cases (0.38) suggests that vegetation density may influence mosquito habitat suitability. These findings are consistent with studies highlighting the multifaceted impact of climatic variables on dengue transmission (Kirk et al. 2024 ). The temporal analysis reveals distinct outbreak patterns: early-onset outbreaks (May–July) in districts like Thoubal and Kakching, peak-season outbreaks (August–October) in high-burden districts such as Imphal West and Churachandpur, and late-season outbreaks (November–December) in districts like Bishnupur and Senapati. These patterns correspond with the climatic conditions observed, where early monsoon rainfall and subsequent environmental changes create conducive environments for mosquito proliferation. This temporal distribution emphasizes the need for targeted vector control measures aligned with seasonal climatic variations (Aayushi 2025 ). The regression analysis further illustrates the relationship between environmental factors and dengue cases. The weak negative correlation between temperature and dengue cases suggests that higher temperatures alone may not significantly influence transmission. In contrast, the slight positive correlation with precipitation indicates that increased rainfall may enhance breeding sites for mosquitoes. The moderate positive correlation with NDVI implies that areas with higher vegetation density could support more mosquito habitats, potentially increasing transmission risk. These insights align with research indicating that both climatic and environmental factors contribute to the spatial and temporal distribution of dengue (Aayushi 2025 ). 5 Conclusion The interplay between climatic factors, urbanization, and dengue transmission in Manipur underscores the importance of integrated surveillance systems that incorporate environmental data. Understanding these dynamics is crucial for developing effective public health strategies to mitigate dengue outbreaks. Future research should focus on longitudinal studies to assess the impact of climate change on dengue epidemiology and the effectiveness of targeted interventions in high-risk areas. Declarations Acknowledgements The first author expresses gratitude to Dr. S. Priyokumar, State Programme Officer at the National Center for Vector Borne Diseases Control, for his valuable insights and for providing the dengue disease data in Manipur. Author Contribution RML: Conceptualization, Methodology, Data Curation, Formal Analysis and Writing – Original draft. SID: Analysis and Writing – Original draft. Funding Not applicable. Data availability All the Google Colab codes and the datasets involved in this study are made available on GitHub: https://github.com/romenmeitei/Dengue-cases-in-Manipur.git. Conflict of Interest No potential conflict of interest was reported by the authors. Ethics approval and consent to participate Not applicable Consent for publication Not applicable References Aayushi, Sharma. 2025. “Rising Heat, Falling Rain: How Climate Change Is Turning Dengue into a Year-Round Threat in India.” Retrieved (https://climatefactchecks.org/rising-heat-falling-rain-how-climate-change-is-turning-dengue-into-a-year-round-threat-in-india/?utm_source=chatgpt.com). Anselin, Luc. 1995. “Local Indicators of Spatial Association—LISA.” Geographical Analysis 27(2):93–115. Bhatt, Samir, Peter W. Gething, Oliver J. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6274739","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":435391782,"identity":"7108310d-761f-44c8-a424-1e691180a350","order_by":0,"name":"Romen Meitei Lourembam","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Romen","middleName":"Meitei","lastName":"Lourembam","suffix":""},{"id":435391783,"identity":"9cc20f53-7359-4bdc-8340-7d3ebe7a0f30","order_by":1,"name":"Indira Devi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2ElEQVRIiWNgGAWjYBACNgYGgwMMDBIJDAzJQJpBQoYULWkJIC08xFhkACKAynPADMJa+KSbNx6uqLHIk2/P+fzqRo0FDwP74aMb8DpM5ljBwTPHJIoNzrzdZp1zDOgwnrS0G3i1SOQYHGxgk0jcIJG7zTiHDahFgseMCC3/JBLnz8h5Zpzzj1gtjW0SiQ03cpgf57YRpSWt4GBjH8gvz8yYc/skeNgI+UV+RvLmjw3f6oAhlvz4c863Ojl+9sPH8GpBtRFMEqscBJg/kKJ6FIyCUTAKRg4AAK1OR/KJkva8AAAAAElFTkSuQmCC","orcid":"","institution":"IBSD: Institute of Bioresources and Sustainable Development","correspondingAuthor":true,"prefix":"","firstName":"Indira","middleName":"","lastName":"Devi","suffix":""}],"badges":[],"createdAt":"2025-03-21 06:44:29","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6274739/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6274739/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":81013423,"identity":"4837f119-ae2a-466c-92c5-9c34ad8b5721","added_by":"auto","created_at":"2025-04-21 08:35:45","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":144708,"visible":true,"origin":"","legend":"\u003cp\u003eOverlay of the GeoJSON shapefile associated with the boundaries of Manipur validated using the Folium library in Google Colab.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-6274739/v1/45056e58763c503f8a20271d.png"},{"id":81012186,"identity":"a07e7b2c-dc8c-42f8-9f7e-fddd4877ace4","added_by":"auto","created_at":"2025-04-21 08:27:45","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":261445,"visible":true,"origin":"","legend":"\u003cp\u003eMonthly Temperature (°C) Trend from 2019 to 2024, illustrating seasonal fluctuations with peak temperatures observed during summer months and a decline in winter months.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-6274739/v1/093b236ff5916cf3dab8ec58.png"},{"id":81012182,"identity":"d2e21085-61b1-4dd1-93ae-8717d7a1b4b5","added_by":"auto","created_at":"2025-04-21 08:27:45","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":267787,"visible":true,"origin":"","legend":"\u003cp\u003eMonthly Precipitation (mm) Trend from 2019 to 2024, showing seasonal variations with peak rainfall occurring during the monsoon months and lower precipitation during the dry season.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-6274739/v1/917562f7989f58a8fdb21724.png"},{"id":81012179,"identity":"13ccbd89-4f34-4110-8e22-4ea615497375","added_by":"auto","created_at":"2025-04-21 08:27:45","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":254843,"visible":true,"origin":"","legend":"\u003cp\u003eMonthly NDVI Trend from 2019 to 2024, illustrating seasonal fluctuations in vegetation greenness, with peaks corresponding to post-monsoon recovery and declines likely influenced by climatic variations such as rainfall and temperature extremes.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-6274739/v1/1b14f1f77535e2aa9232d3a4.png"},{"id":81012187,"identity":"0d8479fb-d94d-4365-beac-839fbdc7813f","added_by":"auto","created_at":"2025-04-21 08:27:45","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":187208,"visible":true,"origin":"","legend":"\u003cp\u003eHeatmap showing the correlation matrix between dengue cases and climate variables (Temperature, Precipitation, and NDVI). Strong correlations are indicated by colour intensity, highlighting potential associations between climate factors and disease incidence. Precipitation (p = 0.0006) and NDVI (p \u0026lt; 0.0001) has shown statistically significant effects, while Temperature does not significantly impact dengue cases.\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-6274739/v1/4235af4cbac7aa1d557121ad.png"},{"id":81013428,"identity":"0a91d330-6038-4678-a8f8-a4abde9ec301","added_by":"auto","created_at":"2025-04-21 08:35:46","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":297678,"visible":true,"origin":"","legend":"\u003cp\u003eDengue Case Clusters in Different Districts of Manipur (K-Means Clustering). The bar chart categorizes districts based on total dengue cases, with higher case clusters highlighted in red (Imphal West) and green (Imphal East). Other districts, shown in blue, report comparatively lower-case counts. This visualization helps identify dengue hotspots and potential areas requiring targeted interventions.\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-6274739/v1/32fb9c360346fd36f05091c7.png"},{"id":81013427,"identity":"800a2c47-22ac-4f47-8b0f-a3574fa6240e","added_by":"auto","created_at":"2025-04-21 08:35:46","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":113676,"visible":true,"origin":"","legend":"\u003cp\u003eA) Temperature vs Dengue Cases – A weak negative correlation suggests that higher temperatures may not have a significant impact on dengue case numbers. B) Precipitation vs Dengue Cases – A slight positive correlation indicates that increased rainfall may contribute to a rise in dengue cases. C) NDVI vs Dengue Cases – A moderate positive correlation suggests that vegetation density (NDVI) may influence the distribution of dengue cases.\u003c/p\u003e","description":"","filename":"floatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-6274739/v1/344cc3d4c51c9bc990b61d4b.png"},{"id":81013431,"identity":"ed9bb4af-d5f2-4255-8a89-b78301ad64c2","added_by":"auto","created_at":"2025-04-21 08:35:46","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":120128,"visible":true,"origin":"","legend":"\u003cp\u003eThe dendrogram illustrates the clustering of districts in Manipur based on dengue case trends. Imphal West exhibits the highest cluster distance, indicating a significantly different case burden compared to other districts. The clustering structure highlights regional similarities in dengue case distribution over time.\u003c/p\u003e","description":"","filename":"floatimage9.png","url":"https://assets-eu.researchsquare.com/files/rs-6274739/v1/7d7bb191ab3792e1e836d4e8.png"},{"id":81015551,"identity":"c03f1058-9b7d-48c7-a358-a3a0461dc2e7","added_by":"auto","created_at":"2025-04-21 08:51:46","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":344854,"visible":true,"origin":"","legend":"\u003cp\u003eTime-Series Analysis of Dengue Cases and Temperature Trends (2019-2025). ANCOVA results indicate that temperature does not have a statistically significant impact on dengue cases (R² = 0.448).\u003c/p\u003e","description":"","filename":"floatimage10.png","url":"https://assets-eu.researchsquare.com/files/rs-6274739/v1/f92f11f05e4bc5c682691524.png"},{"id":83417092,"identity":"96392d2e-b493-44fa-b61f-53e25e495944","added_by":"auto","created_at":"2025-05-25 21:25:14","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2790069,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6274739/v1/2986ebfc-04b6-413c-b61e-2b01f859af18.pdf"}],"financialInterests":"","formattedTitle":"Geospatial Insights into Dengue: Climate Trends and Epidemic Clusters in Manipur","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eDengue fever, a mosquito-borne viral infection, has emerged as a significant public health challenge in many tropical and subtropical regions, including India. The disease, primarily transmitted by \u003cem\u003eAedes aegypti\u003c/em\u003e and \u003cem\u003eAedes albopictus\u003c/em\u003e mosquitoes, has exhibited increasing incidence due to rapid urbanization, climate change, and inadequate vector control measures (Bhatt et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Manipur, a northeastern state of India, has experienced a rise in dengue cases over the years, necessitating a comprehensive understanding of its epidemiological patterns and associated environmental factors.\u003c/p\u003e \u003cp\u003eClimate variables such as temperature, precipitation, and vegetation cover significantly influence the breeding and survival of \u003cem\u003eAedes\u003c/em\u003e mosquitoes, thereby impacting dengue transmission dynamics (Morin, Comrie, and Ernst 2013). Studies have shown that rising temperatures and altered precipitation patterns create optimal conditions for mosquito proliferation, extending their seasonal activity and geographical distribution (Liu-Helmersson et al. 2014). Similarly, increased vegetation density, as represented by the Normalized Difference Vegetation Index (NDVI), has been linked to mosquito habitat suitability and disease outbreaks (Eisen and Lozano-Fuentes 2009). Analysing the interplay between these climatic factors and dengue incidence is crucial for developing predictive models and effective intervention strategies.\u003c/p\u003e \u003cp\u003eRecent advancements in geospatial and machine learning techniques have enabled a more refined approach to studying disease transmission patterns. The integration of geospatial data with epidemiological records facilitates the identification of high-risk areas, allowing for targeted public health interventions(Kraemer et al. 2015). Clustering methods such as K-Means and hierarchical clustering provide insights into spatial and temporal trends of dengue outbreaks, aiding in the formulation of early warning systems and vector control measures (Gubler \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Previous studies have successfully employed machine learning techniques to classify dengue risk zones based on climatic and epidemiological parameters, highlighting the potential of data-driven decision-making in disease management (Cristobal et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThis study aims to analyse the spatiotemporal trends of dengue outbreaks in Manipur by integrating dengue case data with climate variables using geospatial analytics and machine learning techniques. Specifically, the research focuses on (i) validating the Manipur administrative boundary shapefile for accurate geospatial analysis, (ii) extracting and processing climate data from Google Earth Engine (GEE), (iii) identifying dengue hotspots using K-Means clustering, and (iv) analysing temporal outbreak trends through hierarchical clustering. By leveraging cloud-based computational tools such as Google Colab, this study ensures efficient data processing, visualization, and model implementation (Google Colaboratory). The findings will contribute to the enhancement of dengue surveillance systems, facilitate targeted vector control efforts, and support policymakers in designing evidence-based public health strategies for dengue mitigation in Manipur.\u003c/p\u003e"},{"header":"2. Methodology","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Data Collection and Preprocessing\u003c/h2\u003e \u003cp\u003eThe dataset used in this study comprises dengue case reports from various districts of Manipur, obtained from the National Center for Vector Borne Diseases Control, Manipur. It includes monthly total dengue cases recorded from 2018 to 2024, along with serological test results (IgM Mac ELISA and NS1 Antigen ELISA positivity rates). The dataset was processed using Python in Google Colab (McKinney \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). To integrate climate data, geospatial processing and validation of administrative boundaries were performed before data extraction.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Validation of the Manipur shapefile using Folium in Google Colab\u003c/h2\u003e \u003cp\u003eThe Manipur administrative boundary shapefile was validated using the Folium library in Google Colab. Google Earth Engine (GEE) was authenticated and initialized, and the shapefile was retrieved and converted into GeoJSON format for visualization (Gorelick et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Using Folium, the correctness of the boundaries was verified before extracting climate variables relevant to dengue outbreaks. Temperature (\u0026deg;C) data was derived from MODIS Land Surface Temperature (MOD11A2), precipitation (mm) from CHIRPS, and NDVI from the MODIS Vegetation Index dataset (MOD13Q1) (Cheshire \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). These climate variables were spatially aggregated over district boundaries using zonal statistics to align with the dengue case dataset.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Data Normalization and Standardization\u003c/h2\u003e \u003cp\u003eTo ensure temporal consistency, the \"Months\" column was mapped to numerical values and combined with the \"Year\" column to create a \"Time\" variable in \"YYYY-MM\" format. Missing values were handled using descriptive statistics, and numerical data types were standardized where necessary. Outliers were managed using the interquartile range (IQR) method and Winsorization. Climate variables (Temperature, Precipitation, and NDVI) were standardized using StandardScaler from scikit-learn. Time-series trends were visualized using seaborn's line plots, and a correlation heatmap was constructed to assess relationships between climate variables and dengue cases.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Predictive Modelling and Statistical Analysis\u003c/h2\u003e \u003cp\u003eA machine learning-based Linear Regression model was developed to predict dengue cases using climate variables. The dataset was split into training (80%) and testing (20%) sets using train_test_split from scikit-learn. The model was evaluated using:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eMean Squared Error (MSE): Measures average squared differences between actual and predicted values.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eR-squared (R\u0026sup2;) Score: Assesses model explanatory power.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eTo evaluate the impact of climate variables on dengue incidence, an Analysis of Covariance (ANCOVA) model was applied using the Ordinary Least Squares (OLS) method from statsmodels: Total cases \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:={\\beta\\:}_{^\\circ\\:\\:}+\\:{\\beta\\:}_{1}\\:\\left(Temperature\\right)+\\:\\:{\\beta\\:}_{2}\\:\\left(Precipitation\\right)+\\:\\:{\\beta\\:}_{3}\\left(NDVI\\right)+\\:\\epsilon\\:\\)\u003c/span\u003e\u003c/span\u003e An ANOVA table was generated to determine statistical significance.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Hotspot Analysis of Dengue Cases Using K-Means Clustering\u003c/h2\u003e \u003cp\u003eDengue case data was aggregated at the district level, and z-score normalization was applied using StandardScaler (Pedregosa et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). K-Means clustering classified districts into three epidemiological clusters: low-risk, medium-risk, and high-risk. The optimal number of clusters (k\u0026thinsp;=\u0026thinsp;3) was determined based on epidemiological insights. The results were visualized with a district-wise bar chart, aiding in the identification of dengue hotspots for targeted public health interventions (Jain \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2010\u003c/span\u003e)(Anselin \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e1995\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Hierarchical Clustering for Temporal Analysis\u003c/h2\u003e \u003cp\u003eTo explore temporal trends, hierarchical clustering grouped districts based on monthly case trends. Ward\u0026rsquo;s linkage method was used to minimize intra-cluster variance, and a dendrogram was generated to visualize relationships among districts with similar seasonal outbreak patterns (Ward Jr \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e1963\u003c/span\u003e)(Kaufman and Rousseeuw \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e1990\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Computational Environment and Tools\u003c/h2\u003e \u003cp\u003eAll analyses were conducted in Google Colab using Python 3.13. Data processing was performed with Pandas and NumPy, geospatial handling with GeoPandas and Folium, and visualization with Matplotlib and Seaborn. Climate data extraction utilized Google Earth Engine (ee), and statistical modelling was conducted with SciPy and Statsmodels. Machine learning implementations leveraged Scikit-learn. This structured framework integrates inferential statistics and predictive modelling to assess the influence of climate variability on dengue outbreaks.\u003c/p\u003e \u003c/div\u003e"},{"header":"3 Results","content":"\u003cdiv id=\"Sec11\"\u003e\n \u003ch2\u003e3.1 Validation of the Manipur shapefile\u003c/h2\u003e\n \u003cp\u003eThe overlay analysis of the Manipur GeoJSON shapefile on the base map confirmed the spatial accuracy of district boundaries within the defined extent of Manipur before extracting climate data, as illustrated in Fig. 1.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\"\u003e\n \u003ch2\u003e3.2 Acquisition of climate trends across Manipur from 2018 to 2024\u003c/h2\u003e\n \u003cp\u003eThe MODIS LST dataset showed an average annual temperature variation between 18\u0026deg;C and 30\u0026deg;C, with peaks in April to June and a decline during December to February Fig. \u003cspan\u003e2\u003c/span\u003e.\u003c/p\u003e\n \u003cp\u003eThe CHIRPS dataset indicated a monsoon-driven trend, with the highest rainfall recorded between June and September and minimal precipitation from November to February as shown in Fig. 3.\u003c/p\u003e\n \u003cp\u003eThe MODIS NDVI dataset exhibited seasonal fluctuations, with higher vegetation greenness during the post-monsoon season (September to November) and lower values during dry months (January to March) as shown in Fig. 4.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\"\u003e\n \u003ch2\u003e3.2 Correlation analysis between dengue cases and climate data\u003c/h2\u003e\n \u003cp\u003eThe correlation matrix highlights the interdependencies among dengue cases, serological test results (IgM Mac ELISA and NS1 Antigen ELISA), and climate variables (temperature, precipitation, and NDVI) as shown in Fig. \u003cspan\u003e5\u003c/span\u003e. From an epidemiological perspective, a strong positive correlation (0.81) was observed between IgM Mac ELISA Positive cases and Total Cases, confirming IgM-based detection as a reliable marker for dengue surveillance. Similarly, NS1 Antigen ELISA Positive cases exhibited a high correlation (0.82) with Total Cases, reinforcing its diagnostic significance. Additionally, a moderate correlation (0.56) between Time and NS1 Antigen ELISA Positive cases suggests seasonal trends in antigen detection. Climatic factors also demonstrated significant correlations. Temperature (\u0026deg;C) and Precipitation (mm) showed a moderate positive correlation (0.63), reflecting seasonal patterns where higher temperatures are often accompanied by increased rainfall. NDVI, an indicator of vegetation health, negatively correlated with temperature (-0.47) and precipitation (-0.53), suggesting that extreme weather conditions, such as monsoonal flooding, may reduce vegetation greenness. Regarding disease-climate interactions, Total Cases exhibited a weak positive correlation with precipitation (0.22) and a negligible negative correlation with temperature (-0.06). This indicates that while rainfall may contribute to vector breeding, temperature alone does not strongly influence dengue case numbers. Interestingly, NDVI showed a mild positive correlation (0.38) with Total Cases, suggesting that vegetation dynamics may influence vector habitat suitability. Overall, this correlation analysis underscores the complex interactions between climate variability and dengue transmission. These findings emphasize the importance of incorporating climatic parameters into vector-borne disease surveillance and predictive modelling for more effective public health interventions.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\"\u003e\n \u003ch2\u003e3.3 Analysis of Covariance (ANCOVA)\u003c/h2\u003e\n \u003cp\u003eTo assess the impact of climate variables on dengue incidence, an Analysis of Covariance (ANCOVA) was performed, considering Temperature (\u0026deg;C), Precipitation (mm), and NDVI as independent variables and Total Cases as the dependent variable. The ANCOVA model revealed significant effects of Precipitation (F\u0026thinsp;=\u0026thinsp;12.87, p\u0026thinsp;=\u0026thinsp;0.0006) and NDVI (F\u0026thinsp;=\u0026thinsp;34.79, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) on dengue cases as shown in Table \u003cspan\u003e1\u003c/span\u003e. However, Temperature (F\u0026thinsp;=\u0026thinsp;0.36, p\u0026thinsp;=\u0026thinsp;0.551) did not show a statistically significant association. The residual mean square error (MSE) was 549.30, indicating variability in dengue cases not explained by the predictors. These results suggest that Precipitation and NDVI play crucial roles in influencing dengue transmission, possibly by affecting mosquito breeding conditions and vegetation density, respectively. The lack of significance in Temperature might indicate that other environmental or socio-epidemiological factors moderate its effect.\u003c/p\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 1\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eANCOVA results.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"6\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePredictor\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003edf\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSum Sq\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMean Sq\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eF-value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTemperature (\u0026deg;C)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e197.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e197.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.359\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.551\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePrecipitation (mm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7070.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7070.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0006\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNDVI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e19112.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e19112.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e34.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eResidual\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e37352.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e549.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\"\u003e\n \u003ch2\u003e3.4 Clustering analysis of dengue cases across districts in Manipur\u003c/h2\u003e\n \u003cp\u003eThe K-Means clustering analysis of dengue cases across districts in Manipur reveals significant spatial variability in disease burden. Imphal West exhibits the highest number of total dengue cases, forming a distinct high-risk cluster (red), followed by Imphal East (green), which also shows a considerable case load. The remaining districts (blue) report relatively low case counts, indicating a lower dengue burden. This clustering pattern suggests that urbanized regions, particularly Imphal West and Imphal East, serve as major hotspots for dengue transmission, potentially due to higher population density and favourable vector breeding conditions.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec16\"\u003e\n \u003ch2\u003e3.5 Regression analysis of environmental factors influencing dengue cases\u003c/h2\u003e\n \u003cp\u003eThe regression analysis illustrates the relationship between dengue cases and three environmental factors: temperature, precipitation, and NDVI. The first plot indicates a weak negative correlation between temperature and dengue cases, suggesting that higher temperatures may not necessarily lead to increased transmission. In contrast, precipitation exhibits a slight positive correlation with dengue cases, implying that increased rainfall may create favourable breeding conditions for vector proliferation. The strongest positive correlation is observed between NDVI (vegetation index) and dengue cases, indicating that higher vegetation density may be associated with an increased risk of dengue transmission, possibly due to the availability of breeding habitats for mosquitoes.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec17\"\u003e\n \u003ch2\u003e3.6 Hierarchical Clustering for Temporal Analysis\u003c/h2\u003e\n \u003cp\u003eThe hierarchical clustering analysis grouped districts based on similar monthly case patterns, highlighting three major outbreak trends. Early-onset outbreaks (May\u0026ndash;July) were observed in districts such as Thoubal and Kakching, where early monsoon rainfall contributed to vector proliferation. Peak-season outbreaks (August\u0026ndash;October) were observed in high-burden districts, including Imphal West and Churachandpur, where peak transmission occurred during the late monsoon and early post-monsoon periods. Late-season outbreaks (November\u0026ndash;December) were recorded in a few districts, such as Bishnupur and Senapati, which exhibited prolonged transmission beyond the usual peak period.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec18\"\u003e\n \u003ch2\u003e3.7 Time-series analysis of dengue cases and temperature trends\u003c/h2\u003e\n \u003cp\u003eThe time-series analysis of dengue cases and temperature trends from 2019 to 2025 shows a seasonal pattern in dengue outbreaks, with distinct peaks occurring annually as shown in Fig. \u003cspan\u003e9\u003c/span\u003e. These peaks indicate a cyclical trend in dengue cases, likely influenced by seasonal factors. The temperature trend remains relatively stable with slight fluctuations, suggesting that while temperature may have some influence, other environmental or socio-ecological factors contribute significantly to dengue outbreaks. The periodic surges in dengue cases highlight the need for targeted preventive measures during high-risk seasons.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"4 Discussion","content":"\u003cp\u003eThe analysis of dengue case trends in Manipur reveals significant spatial and temporal variations, influenced by climatic factors and urbanization patterns. The dendrogram analysis indicates that Imphal West exhibits the highest cluster distance, signifying a markedly higher dengue burden compared to other districts. This aligns with findings that urbanization facilitates the proliferation of \u003cem\u003eAedes mosquitoes\u003c/em\u003e, thereby increasing dengue transmission(Kolimenakis et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eClimatic variables play a crucial role in dengue dynamics. The MODIS LST dataset indicates annual temperature variations between 18\u0026deg;C and 30\u0026deg;C, with peaks from April to June and declines from December to February. The CHIRPS dataset shows monsoon-driven rainfall trends, with maximum precipitation between June and September. The MODIS NDVI dataset reflects higher vegetation greenness during the post-monsoon season (September to November) and lower values during the dry months (January to March). These climatic patterns are consistent with the seasonal nature of dengue transmission, where increased rainfall and temperature create favourable breeding conditions for Aedes mosquitoes (Xu, Xu, and Wang 2024).\u003c/p\u003e \u003cp\u003eThe correlation analysis underscores the complex interplay between climatic factors and dengue incidence. A moderate positive correlation (0.63) between temperature and precipitation suggests that higher temperatures are often accompanied by increased rainfall, both of which can enhance mosquito breeding habitats. The negative correlations of NDVI with temperature (-0.47) and precipitation (-0.53) may indicate that extreme weather conditions, such as heavy rainfall, can reduce vegetation greenness due to flooding or other environmental changes. The weak positive correlation between total dengue cases and precipitation (0.22) implies that while rainfall contributes to vector breeding, other factors also play significant roles in transmission dynamics. The mild positive correlation between NDVI and total cases (0.38) suggests that vegetation density may influence mosquito habitat suitability. These findings are consistent with studies highlighting the multifaceted impact of climatic variables on dengue transmission (Kirk et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe temporal analysis reveals distinct outbreak patterns: early-onset outbreaks (May\u0026ndash;July) in districts like Thoubal and Kakching, peak-season outbreaks (August\u0026ndash;October) in high-burden districts such as Imphal West and Churachandpur, and late-season outbreaks (November\u0026ndash;December) in districts like Bishnupur and Senapati. These patterns correspond with the climatic conditions observed, where early monsoon rainfall and subsequent environmental changes create conducive environments for mosquito proliferation. This temporal distribution emphasizes the need for targeted vector control measures aligned with seasonal climatic variations (Aayushi \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe regression analysis further illustrates the relationship between environmental factors and dengue cases. The weak negative correlation between temperature and dengue cases suggests that higher temperatures alone may not significantly influence transmission. In contrast, the slight positive correlation with precipitation indicates that increased rainfall may enhance breeding sites for mosquitoes. The moderate positive correlation with NDVI implies that areas with higher vegetation density could support more mosquito habitats, potentially increasing transmission risk. These insights align with research indicating that both climatic and environmental factors contribute to the spatial and temporal distribution of dengue (Aayushi \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e"},{"header":"5 Conclusion","content":"\u003cp\u003eThe interplay between climatic factors, urbanization, and dengue transmission in Manipur underscores the importance of integrated surveillance systems that incorporate environmental data. Understanding these dynamics is crucial for developing effective public health strategies to mitigate dengue outbreaks. Future research should focus on longitudinal studies to assess the impact of climate change on dengue epidemiology and the effectiveness of targeted interventions in high-risk areas.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe first author expresses gratitude to Dr. S. Priyokumar, State Programme Officer at the National Center for Vector Borne Diseases Control, for his valuable insights and for providing the dengue disease data in Manipur.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contribution\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRML:\u0026nbsp;Conceptualization, Methodology, Data Curation, Formal Analysis and Writing \u0026ndash; Original draft. SID:\u0026nbsp;Analysis and Writing \u0026ndash; Original draft.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll the Google Colab codes and the datasets involved in this study are made available on GitHub: https://github.com/romenmeitei/Dengue-cases-in-Manipur.git.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo potential conflict of interest was reported by the authors.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAayushi, Sharma. 2025. \u0026ldquo;Rising Heat, Falling Rain: How Climate Change Is Turning Dengue into a Year-Round Threat in India.\u0026rdquo; Retrieved (https://climatefactchecks.org/rising-heat-falling-rain-how-climate-change-is-turning-dengue-into-a-year-round-threat-in-india/?utm_source=chatgpt.com).\u003c/li\u003e\n\u003cli\u003eAnselin, Luc. 1995. \u0026ldquo;Local Indicators of Spatial Association\u0026mdash;LISA.\u0026rdquo; \u003cem\u003eGeographical Analysis\u003c/em\u003e 27(2):93\u0026ndash;115.\u003c/li\u003e\n\u003cli\u003eBhatt, Samir, Peter W. 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Childs, Mallory Harris, Lisa Couper, T. Jonathan Davies, Coreen Forbes, Alyssa-Lois Gehman, Maya L. Groner, Christopher Harley, Kevin D. Lafferty, Van Savage, Eloise Skinner, Mary O\u0026rsquo;Connor, and Erin A. Mordecai. 2024. \u0026ldquo;Temperature Impacts on Dengue Incidence Are Nonlinear and Mediated by Climatic and Socioeconomic Factors: A Meta-Analysis.\u0026rdquo; \u003cem\u003ePLOS Climate\u003c/em\u003e 3(3):e0000152.\u003c/li\u003e\n\u003cli\u003eKolimenakis, Antonios, Sabine Heinz, Michael Lowery Wilson, Volker Winkler, Laith Yakob, Antonios Michaelakis, Dimitrios Papachristos, Clive Richardson, and Olaf Horstick. 2021. \u0026ldquo;The Role of Urbanisation in the Spread of Aedes Mosquitoes and the Diseases They Transmit\u0026mdash;A Systematic Review.\u0026rdquo; \u003cem\u003ePLOS Neglected Tropical Diseases\u003c/em\u003e 15(9):e0009631.\u003c/li\u003e\n\u003cli\u003eKraemer, Moritz U. G., Marianne E. Sinka, Kirsten A. Duda, Adrian Q. N. Mylne, Freya M. 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Comrie, and Kacey Ernst. 2013. \u0026ldquo;Climate and Dengue Transmission: Evidence and Implications.\u0026rdquo; \u003cem\u003eEnvironmental Health Perspectives\u003c/em\u003e 121(11\u0026ndash;12):1264\u0026ndash;72. doi: 10.1289/ehp.1306556.\u003c/li\u003e\n\u003cli\u003ePedregosa, Fabian, Ga\u0026euml;l Varoquaux, Alexandre Gramfort, Vincent Michel, Bertrand Thirion, Olivier Grisel, Mathieu Blondel, Peter Prettenhofer, Ron Weiss, and Vincent Dubourg. 2011. \u0026ldquo;Scikit-Learn: Machine Learning in Python.\u0026rdquo; \u003cem\u003eThe Journal of Machine Learning Research\u003c/em\u003e 12:2825\u0026ndash;30.\u003c/li\u003e\n\u003cli\u003eWard Jr, Joe H. 1963. \u0026ldquo;Hierarchical Grouping to Optimize an Objective Function.\u0026rdquo; \u003cem\u003eJournal of the American Statistical Association\u003c/em\u003e 58(301):236\u0026ndash;44.\u003c/li\u003e\n\u003cli\u003eXu, Chengdong, Jingyi Xu, and Li Wang. 2024. \u0026ldquo;Long-Term Effects of Climate Factors on Dengue Fever over a 40-Year Period.\u0026rdquo; \u003cem\u003eBMC Public Health\u003c/em\u003e 24(1):1451. doi: 10.1186/s12889-024-18869-\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Dengue Epidemiology, Geospatial Analysis, Climate Trends, Dengue Clusters, Manipur, Time-Series Analysis, Environmental Factors, Temperature and Precipitation, Vector-Borne Diseases","lastPublishedDoi":"10.21203/rs.3.rs-6274739/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6274739/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eDengue fever, a vector-borne disease transmitted by \u003cem\u003eAedes\u003c/em\u003e mosquitoes, has become a growing public health concern in India, particularly in Manipur. The interplay between climate variables\u0026mdash;temperature, precipitation, and vegetation cover\u0026mdash;significantly influences dengue transmission dynamics. This study employs geospatial analytics and machine learning techniques to analyse the spatiotemporal trends of dengue outbreaks in Manipur from 2018 to 2024. Dengue case data, obtained from the National Center for Vector Borne Diseases Control, were integrated with climate variables extracted from Google Earth Engine (GEE).\u003c/p\u003e \u003cp\u003eThe administrative boundary of Manipur was validated using Folium, ensuring accurate geospatial analysis. Temperature, precipitation, and NDVI data were processed to assess their correlation with dengue incidence. K-Means clustering was applied to classify districts into epidemiological risk zones, identifying dengue hotspots, while hierarchical clustering was used to analyze seasonal outbreak patterns. The findings revealed significant correlations between dengue incidence and climate factors, with moderate positive associations observed between precipitation and dengue cases, highlighting the role of monsoon-driven vector proliferation.\u003c/p\u003e \u003cp\u003eResults indicated that high-risk districts experienced persistent outbreaks, emphasizing the need for targeted vector control strategies. The use of Google Colab enabled efficient data processing and visualization, leveraging cloud-based computational tools for advanced analytics. This study underscores the importance of integrating climatic parameters into dengue surveillance and predictive modelling to enhance early warning systems. The insights gained can inform public health policies, facilitating proactive interventions and resource allocation to mitigate dengue transmission in Manipur.\u003c/p\u003e","manuscriptTitle":"Geospatial Insights into Dengue: Climate Trends and Epidemic Clusters in Manipur","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-21 08:27:41","doi":"10.21203/rs.3.rs-6274739/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"73db5557-d3c7-4476-853b-43fb74ab8fb2","owner":[],"postedDate":"April 21st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-05-25T21:17:06+00:00","versionOfRecord":[],"versionCreatedAt":"2025-04-21 08:27:41","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6274739","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6274739","identity":"rs-6274739","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}