ArticleScientific reports2022
A retrospective study of environmental predictors of dengue in Delhi from 2015 to 2018 using the generalized linear model.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Dengue and Chikungunya Seroprevalence in an Urban Low-Income Cohort in Delhi: A Cross-Sectional and Nested Longitudinal Study.Tropical medicine and infectious disease · 2026Article
- A novel approach to quantify the optimal range and causal effect of rainfall on vector-borne diseases: a case study of dengue epidemics.Journal of the Royal Society, Interface · 2025Article
- Disentangling climate's dual role in dengue dynamics: A multiregion causal analysis study.Science advances · 2025Article
- Review
- Socioeconomic and environmental factors associated with dengue fever incidence in Guatemala: Rising temperatures increase dengue risk.PloS one · 2024Article
- A Maximum Entropy Model of the Distribution of Dengue Serotype in Mexico.Transboundary and emerging diseases · 2023Article
- The effect of temperature on dengue virus transmission byFrontiers in cellular and infection microbiology · 2023Review
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Abstract
Dengue fever is a mosquito-borne infection with a rising trend, expected to increase further with the rise in global temperature. The study aimed to use the environmental and dengue data 2015-2018 to examine the seasonal variation and establish a probabilistic model of environmental predictors of dengue using the generalized linear model (GLM). In Delhi, dengue cases started emerging in the monsoon season, peaked in the post-monsoon, and thereafter, declined in early winter. The annual trend of dengue cases declined, but the seasonal pattern remained alike (2015-18). The Spearman correlation coefficient of dengue was significantly high with the maximum and minimum temperature at 2 months lag, but it was negatively correlated with the difference of average minimum and maximum temperature at lag 1 and 2. The GLM estimated β coefficients of environmental predictors such as temperature difference, cumulative rainfall, relative humidity and maximum temperature were significant (p < 0.01) at different lag (0 to 2), and maximum temperature at lag 2 was having the highest effect (IRR 1.198). The increasing temperature of two previous months and cumulative rainfall are the best predictors of dengue incidence. The vector control should be implemented at least 2 months ahead of disease transmission (August-November).
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