Weather integrated multiple machine learning models for prediction of dengue prevalence in India

• Published in: International journal of biometeorology Vol. 67; no. 2; pp. 285 - 297
• Main Authors: Kakarla, Satya Ganesh, Kondeti, Phani Krishna, Vavilala, Hari Prasad, Boddeda, Gopi Sumanth Bhaskar, Mopuri, Rajasekhar, Kumaraswamy, Sriram, Kadiri, Madhusudhan Rao, Mutheneni, Srinivasa Rao
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.02.2023; Springer Nature B.V
• Subjects: Animal Physiology; Animals; Aquatic insects; Biological and Medical Physics; Biophysics; Climate change; Climate models; Dengue - epidemiology; Dengue fever; Disease Outbreaks; Disease transmission; Earth and Environmental Science; Environment; Environmental Health; Humans; Incidence; Independent variables; Long short-term memory; Machine Learning; Mathematical models; Mean temperatures; Meteorology; Modelling; Moisture effects; Mosquitoes; Original Paper; Plant Physiology; Predictions; Prevalence; Public health; Regression analysis; Relative humidity; Soil moisture; Soil temperature; Support vector machines; Urbanization; Vector-borne diseases; Viral diseases; Weather; India; Deep learning; Modelling; Dengue; Kerala; Machine learning; India
• ISSN: 0020-7128; 1432-1254
• DOI: 10.1007/s00484-022-02405-z

Dengue is a rapidly spreading viral disease transmitted to humans by Aedes mosquitoes. Due to global urbanization and climate change, the number of dengue cases are gradually increasing in recent decades. Hence, an early prediction of dengue continues to be a major concern for public health in countries with high prevalence of dengue. Creating a robust forecast model for the accurate prediction of dengue is a complex task and can be done through various data modelling approaches. In the present study, we have applied vector auto regression, generalized boosted models, support vector regression, and long short-term memory (LSTM) to predict the dengue prevalence in Kerala state of the Indian subcontinent. We consider the number of dengue cases as the target variable and weather variables viz., relative humidity, soil moisture, mean temperature, precipitation, and NINO3.4 as independent variables. Various analytical models have been applied on both datasets and predicted the dengue cases. Among all the models, the LSTM model was outperformed with superior prediction capability (RMSE: 0.345 and R 2 :0.86) than the other models. However, other models are able to capture the trend of dengue cases but failed in predicting the outbreak periods when compared to LSTM. The findings of this study will be helpful for public health agencies and policymakers to draw appropriate control measures before the onset of dengue. The proposed LSTM model for dengue prediction can be followed by other states of India as well.