Prediction of dengue incidents using hospitalized patients, metrological and socio-economic data in Bangladesh: A machine learning approach

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...

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Bibliographic Details
Published in:PloS one 2022-07, Vol.17 (7), p.e0270933
Main Authors: Dey, Samrat Kumar, Rahman, Md. Mahbubur, Howlader, Arpita, Siddiqi, Umme Raihan, Uddin, Khandaker Mohammad Mohi, Borhan, Rownak, Rahman, Elias Ur
Format: Article
Language:English
Subjects:
Algorithms
Analysis
Biology and life sciences
Climate change
Computer and Information Sciences
Data mining
Datasets
Dengue
Dengue fever
Dengue viruses
Diagnosis
Disease spread
Disease transmission
Distribution
Earth Sciences
Environmental factors
Epidemics
Evaluation
Exocrine glands
Fever
Health services
Humidity
Learning algorithms
Machine learning
Medical records
Medicine and Health Sciences
Mosquitoes
Patients
People and Places
Physical Sciences
Rain
Rainfall
Rainy season
Regression analysis
Research and Analysis Methods
Social Sciences
Support vector machines
Tropical diseases
Vector-borne diseases
Viral diseases
Viruses
Wind
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Summary: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.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0270933