Supplementary Information

The online version contains supplementary material available at 10.1038/s41598-025-85437-w.

Subject terms: Epidemiology, Climate-change impacts, Machine learning, Climate sciences

Climatic factors affecting dengue mortality in Pune

By incorporating insights from the previous studies and establishing defined thresholds based on statistical analysis, we identified favorable windows of climatic conditions for increased dengue mortalities over Pune and devised a novel dengue metric for a preliminary statistical analysis. The novelty of our metric is that instead of looking into the direct relationship between climate and dengue, the number of days or weeks with favorable climatic conditions are considered here. Also, unlike existing studies examining the rainfall-dengue association, this metric employs three rainfall dependent variables—wet weeks, flush counts, and monsoon intraseasonal oscillations—to understand how rainfall patterns impact dengue mortality in Pune. The various climate-based predictors included in the metric are described in detail in the following sections (sections “Temperature” to “Humidity”).

Dengue early warning system for Pune

Machine learning methods are found to be superior to traditional statistical methods in understanding and predicting the complex interactions between multiple variables⁴⁷. To identify potential climatic variables that influence dengue incidence in Pune and to understand their cumulative effect, we used the machine learning technique of Random Forest Regression (RFR) on 12 years of observed dengue mortality and meteorological data (2004–2015) at weekly timescales. Random Forest is an ensemble learning method that combines the predictions of multiple decision trees to create a more robust and accurate model⁴⁸. A combination of individual models like this can help to reduce overfitting and improve generalization.

Despite the availability of many advanced machine learning algorithms, RFR was selected for this study due to its favorable prediction performance in prior research^49–51 and its relative ease of comprehension, making it suitable for researchers new to machine learning. RFR is highly flexible and can handle high-dimensional data with non-linear effects and numerous covariate interactions. Its built-in mechanism to rank significant predictors also provides valuable insights into the model predictions⁵⁰. Predictor inputs used in the RFR model (dengue model from here) are weekly means of temperature, cumulative rainfall, relative humidity, and weekly count of active and break days. These weekly predictors were chosen from a larger group of variables based on statistical analyses and the improvement in model skill (Fig. 1, steps 2 and 3). The other derivative variables from the dengue metric (section “Climatic factors affecting dengue mortality in Pune”) were not used as additional individual predictors in the dengue model, as their inclusion did not enhance model skill. This is because RFR is a decision tree-based algorithm, capable of capturing the non-linear associations between predictors and the target⁵². It can identify the optimal climatic conditions for increased dengue risk using basic climate variables such as mean temperature, cumulative rainfall, and mean relative humidity, without the need to explicitly include derived variables like the count of days with optimal temperature, wet weeks, or flush counts. Although the dengue metric variables were not directly used in the model, they aid in interpreting the predictions and projections generated by the dengue model.

It is critical to incorporate the lag between favorable weather conditions and dengue mortality into the dengue model, as it accounts for multiple processes occurring over different time spans, from mosquito breeding to human host getting infected with the dengue virus. The mosquito’s lifecycle from an egg to an adult, takes about 8–10 days, while the extrinsic incubation period of the dengue virus inside an infected mosquito takes 5–33 days¹². After infecting a human through a mosquito bite, the virus has an intrinsic incubation period of about 2–15 days in the human body⁵³. A person typically develops viremia, a condition characterized by a high level of the dengue virus in the blood, four days after being bitten by an infectedAe. aegypti mosquito. Viremia can last up to 12 days but usually lasts around five days⁵⁴. Studies report that the possible lag between hospital admission (after the onset of symptoms) and dengue mortality ranges from 3–15 days^55,56. Altogether, from the availability of aquatic habitats for mosquito breeding to a dengue mortality, it takes approximately 1–3 months (8–73 days). In addition to this, the dengue vectors capable of transmitting dengue virus can survive for about 30 days²¹.

Similar to the methodology followed in the prior studies on dengue modeling using climatic variables, input variables are provided to the dengue model at the lags where variable-dengue correlations are strongest^57,58. Using cross-correlation analysis, our study finds that dengue mortality over Pune (2004–2015) shows maximum correlation with mean temperature (r = 0.38,p < 0.05) and cumulative rainfall (r = 0.20,p < 0.05) at lags of 20 and 11 weeks, respectively (Fig. 2d,e). Relative humidity shows a statistically significant correlation with dengue mortality at a lag of six weeks (r = 0.21,p < 0.05), with a slightly stronger correlation at lag zero (r = 0.22,p < 0.05) (Fig. 2f). Though relative humidity at lag zero had a slightly stronger correlation with dengue mortality, relative humidity at a lag of six weeks was chosen for the dengue modeling due to its potential for longer-lead predictions. The dengue model achieved highest prediction skill by using temperature twenty weeks prior, rainfall eleven weeks prior, and relative humidity six weeks prior for predicting dengue mortality of the current week.

It is important to note that these linear correlation values are part of the preliminary statistical analysis aimed at identifying the lag in climate-dengue association (Fig. 2d–f) and do not represent the predictive power of each variable in forecasting dengue mortality. Given the non-linear and complex nature of climate-dengue interactions, we further developed a machine learning-based dengue model using the random forest regression algorithm to capture the intricate relationships between climatic variables and dengue.

We used the meteorological and dengue mortality data from 2005–2014 for training and the years 2004 and 2015 for testing the dengue model. At the end of the optimization of model parameters usingRandomizedSearchCV andK-fold cross-validation (Fig. 1, steps 6 and 7), the model yields a good prediction skill with a statistically significant correlation coefficient ofr = 0.77 (p < 0.05) and a low Normalized Root Mean Squared Error (NRMSE) score of 0.52 between the actual and predicted dengue mortalities during the test period, indicating that the model can effectively predict dengue mortality in Pune (Fig. 4a).

To understand the role of different weather variables in predicting Pune’s dengue mortality, we utilized the variable importance feature of Random Forest algorithms. Variable importance refers to the degree of association between a given predictor variable and the model predictions. From the observed climate and dengue mortality data, the RFR model finds that mean temperature and relative humidity has a variable importance of 41% and 20%, respectively, in predicting dengue mortality over Pune (Fig. 4b). Rainfall and its variability together account for 39% of variable importance, with cumulative rainfall contributing 29% and monsoon intraseasonal variability (active-break days) contributing 10%. The variable importance is directly linked to the model skill, indicating that 41% of the model’s prediction skill is driven by mean temperature and 29% by cumulative rainfall.

Dengue future projections for Pune

Monsoon conditions, particularly rainfall patterns, are projected to change in the coming years and decades over India⁵⁹. This may have significant implications for Pune since the dengue mortality in the region exhibits a strong dependence on the rainfall pattern. The dengue model developed based on the observed climate-dengue associations in Pune is used in conjunction with climate change projections from the Coupled Model Intercomparison Project phase 6 (CMIP6) models for depicting future projections of dengue mortality in the district (Fig. 5a). Dengue mortality in Pune is projected under three distinct Shared Socioeconomic Pathways (SSPs), representing different levels of socioeconomic development and greenhouse gas emissions. SSP1, SSP2 and SSP5 correspond to low, intermediate, and high emissions, respectively. Eight CMIP6 models that best represent the interannual variability in the intraseasonal oscillations of the Indian summer monsoon and spatial distribution of the mean precipitation over India are selected for assessing the future projections of climate and dengue in Pune (Supplementary TableS1). All climatic predictors of the dengue in Pune were computed from the CMIP6 model outputs and provided as inputs into the dengue model at optimal lags to obtain the dengue future projections for the region.

According to the future projections obtained from the dengue model, dengue mortalities over Pune are projected to rise in the future under all emission pathways (SSPs) (Fig. 5a,b). In the near future (2020–40), regardless of the emission pathways, a 12–13% increase in dengue mortalities is expected relative to the reference period (1995–2014). Whereas in the mid-century (2040–60), dengue mortality over Pune is projected to increase by 25% under SSP2 and 40% under SSP5 relative to the reference period (1995–2014) (Fig. 5b). This increase is amplified in the late century (2081–2100), with projections of up to a 112% increase in dengue mortality by 2100 under SSP5. In the late century, the percentage increase in dengue mortality under SSP1 and SSP2 is 30% and 58%, respectively (Fig. 5b). The first virologically proven dengue epidemic in India occurred in Calcutta and the Eastern Coast of India in 1963–1964^60,61, and hence the future projections are considered from 1950 onwards.

In Fig. 5b, the changes in average global surface temperature compared to the pre-industrial era (1850–1900) are indicated in red font within the bars corresponding to SSP1, SSP2, and SSP5 pathways for the near future, mid-century, and late century time periods. If future emissions can be controlled to keep the global average surface temperature changes below 1.5 °C, the anticipated rise in dengue mortalities relative to the reference period will be below 13%. However, if emissions continue to increase and temperatures exceed the 1.5 °C threshold, the corresponding increase in dengue mortality ranges from 23–40% (Fig. 5b). Alternatively, when the global surface temperature crosses the 2 °C threshold, dengue mortalities are projected to increase by 40–112% relative to the reference period.

Climatic changes leading to the projected increase in dengue in Pune

To understand the climatic changes leading to the projected increase in dengue mortalities over Pune (Fig. 5a,b), we analyzed the future projections of environmental predictors of dengue in Pune. The future projections from the selected CMIP6 models show that all environmental predictors of dengue are expected to increase in the future (relative to historical levels), with greenhouse gas emissions and socio-economic pathways having a particularly pronounced effect on future projections towards the late century (2081–2100) (Supplementary Fig.S3). In the near-term, the meteorological variables show little change relative to their respective reference period (1995–2014) values. However, by the mid-century, the mean temperature is likely to increase by 1–1.5 °C, cumulative rainfall by 23–27 cm, relative humidity by 0.4–1%, and the number of active-break days by 1–3 days from their corresponding reference period values (Supplementary TableS2). In the far-future, selected CMIP6 models indicate an increase in mean temperature, cumulative rainfall, mean relative humidity, count of active and break days in the range of 1.2–3.5 °C, 28–65 cm, 0.2–1.6%, and 2–5 days, respectively, under the three emission pathways considered in this study. The lower limit in the ranges corresponds to the low emission scenario (SSP1), while the maximum changes are projected under the higher emission scenario (SSP5).

The future trends (2015–2100) in the climatic predictors of dengue mortality in Pune is shown in Table2. Both temperature and rainfall demonstrate a statistically significant increasing trend across all emission pathways. The count of active-break days shows a statistically significant increasing trend only under SSP2 and SSP5. Additionally, relative humidity exhibits an increasing trend only in the high-emission scenario, SSP5.

Even though all meteorological predictors of dengue mortality in Pune are projected to increase in the future, their collective impact on dengue mortality cannot be determined through linear analysis alone. The dengue model was used to gain insights into the combined effect of projected changes in dengue predictors on dengue mortality in Pune. To isolate the impact of these trends in the predictor variables on dengue mortality projections, we performed experiments detrending the climatic predictors in the dengue model. In these experiments, each predictor variable was detrended individually while keeping the other predictors unchanged. The results of these experiments are presented in Supplementary Figs.S4 andS5. According to these experiments, the trend in the mean temperature has a significant influence, contributing to a 12–22% increase in dengue mortality across low-to-high emission scenarios (Supplementary Fig.S5). In contrast, the trend in rainfall offsets dengue mortality by 2–4% (Supplementary Fig.S5). The percentage changes are calculated for the detrending experiments relative to the original dengue mortality projections. The trends in other variables have little effect on the dengue mortality in Pune.

Our analysis of observed meteorological variables and reported dengue mortalities from 2004 to 2015 in Pune (presented in section “Climatic factors affecting dengue mortality in Pune”) reveals a complex relationship between rainfall patterns and dengue mortality. Specifically, we found that moderate rains spread in time lead to increased dengue mortality over Pune, whereas heavy rains reduce it through the flushing effect (Table1). To further investigate if the dengue model is picking up this relationship while giving future projections, we performed an experiment in which we modified the rainfall pattern in the selected CMIP6 model simulations by eliminating all extreme rainfall events and testing the impact on dengue mortality. Here we considered extreme rainfall as those with values greater than the 90th percentile of the reference period rainfall time series (1995 and 2014). Our results show that the removal of extreme rainfall events lead to an increase in dengue mortality by about 3–4% (refer to Supplementary Figs.S4b, d, f, andS5). Thus, future extreme rainfall events can be expected to offset the increase in dengue by 3–4%. This result is consistent with our findings from observational data, which suggest that extreme rainfall tends to reduce dengue transmission, while moderate rainfall is likely to increase dengue incidence.

In summary, our study reveals that the projected increase in dengue mortality over Pune is primarily driven by the increase in temperature and changes in rainfall patterns. These results underscore the importance of a comprehensive and multivariate approach to assessing the risks associated with dengue transmission. Furthermore, it emphasizes the importance of reducing greenhouse gas emissions to mitigate the harmful impact of climate change on public health.

Observed climate and health data

The current study uses dengue mortality data for Pune from 2001–2015, obtained from the health department of Pune Municipal Corporation (PMC). The dataset pertains to urban areas with 80% of the data reported from Pune city (Fig. 1, step 1). For the principal analyses in this study, we omitted the initial years (2001–2003) due to sparse data. To assess whether the dengue mortality over a region can represent its dengue incidences, we compared dengue cases and mortality from the National Center for Vector Borne Diseases Control (NCVBDC) data³. Our analysis reveals a statistically significant strong correlation (r = 0.72,p < 0.05) between dengue cases and mortality, demonstrating that dengue mortality can be used as a proxy for dengue morbidity (Supplementary Fig.S6).

To study the climate-dengue associations in Pune, the current study uses high-resolution daily gridded rainfall (0.25° × 0.25° grid)⁷⁶ and temperature (1° × 1° grid)⁷⁷ data from the India Meteorological Department (IMD), both validated using IMD station data at Pune. Relative humidity data, covering the same period as the health data (2004–2015), were obtained from the IMD’s Pune station (Fig. 1, step 2).

Climate projections from CMIP6

CMIP6 offers the latest climate projections from multiple modeling centers worldwide under different socioeconomic and emission scenarios. The current study uses historical (1850–2015) and future (2015–2100) simulations from the CMIP6 models for the dengue future projection analysis. Future dengue projections under three distinct Shared Socioeconomic Pathways (SSPs), representing different levels of socioeconomic development and greenhouse gas emissions are investigated. The three pathways are: a world of sustainability-focused growth and equality where the radiative forcing is limited to 2.6 W m⁻² by the end of the twenty-first century (SSP1-2.6, low emission pathway); a “middle of the road” world where trends broadly follow their historical patterns and the radiative forcing is limited to 4.5 W m⁻² (SSP2-4.5, intermediate emission pathway); and the high road—a world of rapid and unconstrained fossil fuel-driven growth in economic output and energy use where the radiative forcing is high at 8.5 W m⁻² (SSP5-8.5, high emission pathway)⁷⁸. Fifteen CMIP6 models with all three predictor variables—temperature, rainfall, and relative humidity—available under SSP1, SSP2, and SSP5 are evaluated. Instead of a direct ensemble mean of the 15 models, models with a better skill in representing the Indian summer monsoon rainfall alone are selected for future projection analysis.

Climate based dengue model

To explore the non-linear and cumulative effects of selected climatic predictors on dengue mortality, we developed a machine learning model employing the Random Forest Regression (RFR) algorithm (Fig. 1, steps 4–12). The resulting dengue model, incorporating the lag in climate-dengue association, can function as a dengue early warning system for Pune with a two-month lead time (Fig. 1, steps 11 and 12). RFR is a machine learning ensemble method that combines several separately trained models to create a strong learner that can be used for classification and regression⁴⁷. In this study, we implemented RFR using the Python packagescikit-learn⁷⁹.

The development of a dengue early warning model for Pune using the machine learning algorithm Random Forest Regression (RFR) involves many steps, such as the selection of best predictor variables, test-train split, optimization of model hyperparameters, and K-fold cross-validation. Various potential dengue predictors, such as rainfall, minimum temperature, maximum temperature, diurnal temperature range, relative humidity, specific humidity, vapour pressure, and wind speed were collected and analyzed to determine the climatic predictors of dengue mortality in Pune, and those contributed to the model performance alone were retained for predicting dengue mortality (Fig. 1, steps 2 and 3). Also, when variables were highly correlated (multicollinearity), only one was retained, as adding more did not enhance model skill. The Pearson correlation and cross-correlation analyses were performed to aid in the selection of the best climatic predictors of dengue and to identify the lag in climate-dengue associations in Pune (Fig. 1, step 3). The final dengue model is obtained after the following procedures:

Dengue early warning system and future projections for Pune

The dengue model developed based on observed climate-dengue associations in Pune can serve as an early warning system with real-time inputs (Fig. 1, steps 11 and 12) and can also be used to prepare future dengue projections using climate change projections from CMIP6 models (Fig. 1, steps 13 and 14). To obtain dengue mortality predictions for Pune, selected climatic predictors at their respective optimal lags from real-time observations are used as inputs. For future dengue projections, future climate simulations (2015–2100) from selected CMIP6 models are provided as inputs to the dengue model. Incorporating the lag in the climate-dengue association enables dengue mortality predictions two months ahead, providing sufficient lead time for an early warning forecast targeted at curbing dengue outbreaks.

We assessed the fidelity of CMIP6 models in representing the rainfall climatology and its intraseasonal oscillations by comparing model historical rainfall data (1980–2014) over the Indian region (70° E–100° E, 10° N–30° N) with the IMD gridded rainfall data (1980–2014) using the following two criteria. The first criteria checks if the June to September mean rainfall of the model shows a significant pattern correlation with that of the observations and thus selects models with a better spatial representation of the mean Indian summer monsoon rainfall. The second criteria use the standard deviation of summer monsoon rainfall data filtered for 30–90 days to evaluate how well the model represents the year-to-year changes in the monsoon intraseasonal oscillations. The pattern correlation coefficient between the standard deviations of the 30–90 days filtered rainfall from the observations and model simulations is calculated for the Indian region, and models with a statistically significant correlation alone are considered for further analysis (Supplementary Fig.S2).

Eight models that satisfy the above two criteria are selected for further analysis (Supplementary Table 1). Since we are using the ensemble mean of the model projections and most of the models have sufficiently high resolutions (100 km), no additional downscaling is done. The selected model outputs are interpolated into a common spatial grid (1° × 1°) by bilinear interpolation. Bias correction using the quantile mapping method is applied to the selected CMIP6 models⁸². Quantile mapping is a nonparametric method that matches the full distribution variable values between the model and observations by adjusting the mean and variance of a model simulation to agree with the statistical properties of the observations⁸³. After bias correction, a one-degree box around Pune is considered for analyzing the future projections.

We conducted several model experiments to understand the impacts of future climate change on dengue mortality in Pune. Climate predictors from the selected CMIP6 models were detrended individually while keeping other predictors unchanged, and the corresponding impact on future dengue projections was studied. In addition, we modified the rainfall patterns in the CMIP6 simulations by removing all extreme rainfall events to evaluate their relative impact on dengue mortality.

Competing interests

The authors declare no competing interests.

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Supplementary Materials

Data Availability Statement

The dengue mortality data for Pune were obtained from the health department of Pune Municipal Corporation (PMC) under a data agreement and can be accessed via request submitted to the PMC. Researchers can refer to the corresponding author for more information on accessing the data. Observed meteorological data is available in the public domain from the India Meteorological Department, and the CMIP6 model outputs are available through the CMIP6 data archive developed and operated by the Earth System Grid Federation (https://esgf-node.llnl.gov/search/cmip6/). The code sources are available from the corresponding author upon request.