ArticleFrontiers in public health2021
Forecasting Dengue Hotspots Associated With Variation in Meteorological Parameters Using Regression and Time Series Models.
Article in Frontiers in public health, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 3 of them syntheses that pooled it.
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Who cites it
20 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Battling dengue in a warming world: How climate and urbanization shape transmission in low- and middle-income countries (a rapid review).PLoS neglected tropical diseases · 2026Pooled it
- Reviewing the progress of infectious disease early warning systems and planning for the future.BMC public health · 2024Pooled it
- Effects of high temperatures and heatwaves on dengue fever: a systematic review and meta-analysis.EBioMedicine · 2023Pooled it
- Machine learning reveals temperature as a key predictor of dengue risk across Thailand's provinces: A 20-year analysis.PLoS neglected tropical diseases · 2026Article
- Dengue fever prediction based on meteorological features and deep learning models.Infectious Disease Modelling · 2026Article
- Advances in mosquito-borne disease surveillance using machine learning.New microbes and new infections · 2026Article
- Probabilistic forecasting of hand, foot and mouth disease in Mainland China using Bayesian additive regression tree model.Scientific reports · 2025Article
- Comparison of ARIMA and FB-Prophet time series models for the prediction of national and regional malaria incidence in Uganda.Malaria journal · 2025Article
- Integrating meteorological data and hybrid intelligent models for dengue fever prediction.BMC public health · 2025Article
- Article
- Leveraging Climate Data for Dengue Forecasting in Ba Ria Vung Tau Province, Vietnam: An Advanced Machine Learning Approach.Tropical medicine and infectious disease · 2024Article
- Hybrid Machine Learning Approach to Zero-Inflated Data Improves Accuracy of Dengue Prediction.PLoS neglected tropical diseases · 2024Article
- Article
- Deep evolutionary fusion neural network: a new prediction standard for infectious disease incidence rates.BMC bioinformatics · 2024Article
- Article
- Weekly Forecasting of Yellow Fever Occurrence and Incidence via Eco-Meteorological Dynamics.GeoHealth · 2023Article
- Prediction of global omicron pandemic using ARIMA, MLR, and Prophet models.Scientific reports · 2022Article
- The practicality of Malaysia dengue outbreak forecasting model as an early warning system.Infectious Disease Modelling · 2022Article
- Dengue Early Warning System as Outbreak Prediction Tool: A Systematic Review.Risk management and healthcare policy · 2022Review
- Health system resilience: quantifying the dynamic impact of environmental shocks on health service utilization using an interrupted time series and time-series forecasting approach in Western Province, Zambia.The journal of climate change and healthArticle
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
For forecasting the spread of dengue, monitoring climate change and its effects specific to the disease is necessary. Dengue is one of the most rapidly spreading vector-borne infectious diseases. This paper proposes a forecasting model for predicting dengue incidences considering climatic variability across nine cities of Maharashtra state of India over 10 years. The work involves the collection of five climatic factors such as mean minimum temperature, mean maximum temperature, relative humidity, rainfall, and mean wind speed for 10 years. Monthly incidences of dengue for the same locations are also collected. Different regression models such as random forest regression, decision trees regression, support vector regress, multiple linear regression, elastic net regression, and polynomial regression are used. Time-series forecasting models such as holt's forecasting, autoregressive, Moving average, ARIMA, SARIMA, and Facebook prophet are implemented and compared to forecast the dengue outbreak accurately. The research shows that humidity and mean maximum temperature are the major climate factors and exhibit strong positive and negative correlation, respectively, with dengue incidences for all locations of Maharashtra state. Mean minimum temperature and rainfall are moderately positively correlated with dengue incidences. Mean wind speed is a less significant factor and is weakly negatively correlated with dengue incidences. Root mean square error (RMSE), mean absolute error (MAE), and R square error (
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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.