ArticlePloS one2022
Prediction of dengue incidents using hospitalized patients, metrological and socio-economic data in Bangladesh: A machine learning approach.
Article in PloS one, 2022. 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.
- Beyond the Mosquito: Social Determinants of Health and Global Strategies for Aedes-Borne Disease Control.Tropical medicine & international health : TM & IH · 2026Pooled 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
- A systematic review of dengue outbreak prediction models: Current scenario and future directions.PLoS neglected tropical diseases · 2023Pooled it
- Advances in mosquito-borne disease surveillance using machine learning.New microbes and new infections · 2026Article
- Bridging ensemble model and public health practice: an approach for refining understanding of seasonal dengue transmission patterns in Bangladesh.BMC infectious diseases · 2026Article
- Modelling climatic and temporal dynamics of dengue transmission in Bangladesh using deep learning models.PLOS global public health · 2026Article
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- Article
- Commentary on "Explainable artificial intelligence for predicting dengue outbreaks in Bangladesh using eco-climatic triggers".Global epidemiology · 2025Article
- Understanding the social determinants of Aedes-borne diseases in Iran: A qualitative exploration of challenges and policy solutions.PLoS neglected tropical diseases · 2025Article
- Factors associated with infection and hospitalization of Zika cases during the 2016-2018 outbreak in Mexico.Scientific reports · 2025Article
- Dengue fever in Bangladesh: rising trends, contributing factors, and public health implications.Tropical diseases, travel medicine and vaccines · 2025Review
- Multivariate forecasting of dengue infection in Bangladesh: evaluating the influence of data downscaling on machine learning predictive accuracy.BMC infectious diseases · 2025Article
- Dengue Early Warning System and Outbreak Prediction Tool in Bangladesh Using Interpretable Tree-Based Machine Learning Model.Health science reports · 2025Article
- Early Dengue Prediction in Bangladesh: A Comparative Study With Feature Analysis, Explainable Artificial Intelligence, and Model Optimization.Journal of tropical medicine · 2025Article
- Forecasting dengue in Bangladesh using meteorological variables with a novel feature selection approach.Scientific reports · 2024Article
- Implications of Big Data Analytics, AI, Machine Learning, and Deep Learning in the Health Care System of Bangladesh: Scoping Review.Journal of medical Internet research · 2024Article
- Bayesian Analysis Used to Identify Clinical and Laboratory Variables Capable of Predicting Progression to Severe Dengue among Infected Pediatric Patients.Children (Basel, Switzerland) · 2023Article
- A short communication of 2022 dengue outbreak in Bangladesh: a continuous public health threat.Annals of medicine and surgery (2012) · 2023Article
- Relationship between Urban Environmental Components and Dengue Prevalence in Dhaka City-An Approach of Spatial Analysis of Satellite Remote Sensing, Hydro-Climatic, and Census Dengue Data.International journal of environmental research and public health · 2023Article
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Authors and funding
7 authors.
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Abstract
Dengue fever is a severe disease spread by Aedes mosquito-borne dengue viruses (DENVs) in tropical areas such as Bangladesh. Since its breakout in the 1960s, dengue fever has been endemic in Bangladesh, with the highest concentration of infections in the capital, Dhaka. This study aims to develop a machine learning model that can use relevant information about the factors that cause Dengue outbreaks within a geographic region. To predict dengue cases in 11 different districts of Bangladesh, we created a DengueBD dataset and employed two machine learning algorithms, Multiple Linear Regression (MLR) and Support Vector Regression (SVR). This research also explores the correlation among environmental factors like temperature, rainfall, and humidity with the rise and decline trend of Dengue cases in different cities of Bangladesh. The entire dataset was divided into an 80:20 ratio, with 80 percent used for training and 20% used for testing. The research findings imply that, for both the MLR with 67% accuracy along with Mean Absolute Error (MAE) of 4.57 and SVR models with 75% accuracy along with Mean Absolute Error (MAE) of 4.95, the number of dengue cases reduces throughout the winter season in the country and increases mainly during the rainy season in the next ten months, from August 2021 to May 2022. Importantly, Dhaka, Bangladesh's capital, will see the maximum number of dengue patients during this period. Overall, the results of this data-driven analysis show that machine learning algorithms have enormous potential for predicting dengue epidemics.
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