Evidence map›Paper›PMID 39837878›Full record

ArticleScientific reports2025

Dengue dynamics, predictions, and future increase under changing monsoon climate in India.

Yacob Sophia, Mathew Koll Roxy, Raghu Murtugudde, Anand Karipot, Amir Sapkota, Panini Dasgupta, Kalpana Baliwant, Sujata Saunik, Abhiyant Tiwari, Rajib Chattopadhyay and 1 more

Abstract read
In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.

0numbers the graph read from it
0cells of the map it votes in
17citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

17 citing papers in PubMed.

  1. Article
  2. Climate change and antimicrobial resistance.Nature reviews. Microbiology · 2026
    Review
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  15. Observational
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Yacob SophiaCentre for Climate Change Research, Indian Institute of Tropical Meteorology, Ministry of Earth Sciences, Pune, 411008, India.
Mathew Koll RoxyCentre for Climate Change Research, Indian Institute of Tropical Meteorology, Ministry of Earth Sciences, Pune, 411008, India. roxy@tropmet.res.in.ORCID 0000-0001-9311-5162
Raghu MurtuguddeEarth System Science Interdisciplinary Center (ESSIC)/DOAS, University of Maryland, College Park, MD, USA.
Anand KaripotDepartment of Atmospheric and Space Sciences, Savitribai Phule Pune University, Pune, India.
Amir SapkotaDepartment of Epidemiology and Biostatistics, School of Public Health, University of Maryland, College Park, MD, USA.
Panini DasguptaFuture Innovation Institute, Seoul National University, Siheung, Republic of Korea.
Kalpana BaliwantHealth Department, Pune Municipal Corporation, Pune, India.
Sujata SaunikHarvard TH Chan School of Public Health, Boston, MA, USA.
Abhiyant TiwariNatural Resources Defense Council, New Delhi, India.
Rajib ChattopadhyayCentre for Climate Change Research, Indian Institute of Tropical Meteorology, Ministry of Earth Sciences, Pune, 411008, India.
Revati K PhalkeyHeidelberg Institute of Global Health, University of Heidelberg, Heidelberg, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The global burden of dengue disease is escalating under the influence of climate change, with India contributing a third of the total. The non-linearity and regional heterogeneity inherent in the climate-dengue relationship and the lack of consistent data makes it difficult to make useful predictions for effective disease prevention. The current study investigates these non-linear climate-dengue links in Pune, a dengue hotspot region in India with a monsoonal climate and presents a model framework for predicting both the near-term and future dengue mortalities. Dengue mortality and meteorological conditions over a twelve-year period (2004-2015) are analyzed using statistical tools and machine learning methods. Our findings point to a significant influence of temperature, rainfall, and relative humidity on dengue mortality in Pune, at a time-lag of 2-5 months, providing sufficient lead time for an early warning targeted at curbing dengue outbreaks. We find that moderate rains spread over the summer monsoon season lead to an increase in dengue mortality, whereas heavy rains reduce it through the flushing effect, indicating the links between dengue and monsoon intraseasonal variability. Additionally, warm temperatures above 27°C and humidity levels between 60% and 78% elevate the risk of dengue. Based on these weather-dengue associations, we developed a machine-learning model utilizing the random forest regression algorithm. The dengue model yields a skillful forecast, achieving a statistically significant correlation coefficient of r = 0.77 and a relatively low Normalized Root Mean Squared Error score of 0.52 between actual and predicted dengue mortalities, at a lead time of two months. The model finds that the relative contributions of temperature, rainfall, and relative humidity to dengue mortality in Pune are 41%, 39%, and 20%, respectively. We use the dengue model in conjunction with the climate change simulations from the Coupled Model Intercomparison Project phase 6 for the future dengue mortality projections under a global warming scenario. In a changing climate, dengue-related mortality in Pune is projected to rise by 13% in the near future (2021-2040), 23-40% in the mid-century (2041-2060), and 30-112% in the late century (2081-2100) under low-to-high emission pathways in response to the associated increase in temperature and changes in monsoon rainfall patterns.

Indexed as

Climate ChangeDengueForecastingHumansHumidityIndiaMachine LearningRainSeasonsTemperatureWeather

Identifiers

PMID39837878
PMCPMC11750985

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.