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@article{154075,
        author = {Asha Bharambe and Dhananjay Kalbande},
        title = {Prediction of Dengue Outbreaks in Mumbai Region Based on Disease Surveillance and Meteorological Factors using Big Data Approach},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {8},
        number = {9},
        pages = {613-620},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=154075},
        abstract = {One of the most prevalent vector-borne disease in India is Dengue. This study investigated the effects of various environmental/climatic factors on dengue incidence using time series analysis and machine learning approach using the big data environment. The aim of the current study is to understand environmental impact on vector-borne disease (Dengue) in a particular area from Mumbai and use it for prediction of the disease. Disease Incidence Data for dengue was collected from 2012 to 2019 from the study hospital in Mumbai while the climatic data was obtained from the web resources. Statistical time series analysis methods and machine learning techniques are used to make predictions. Our preliminary analysis has identified that there is a correlation between Dengue incidence rate and climatic conditions. Amongst machine learning model, random forest technique gave the results with RMSE of 47.08. Amongst time series analysis techniques, SARIMA model gave significant results with RMSE as 20.79 in univariate analysis as compared to ARIMA with RMSE of 61.27. The multivariate VAR model gave results with RMSE of 27.32. In both univariate and multivariate models, we concluded that when confounding factors are incorporated into forecasting model, it significantly improves AIC value (an AIC value of 5.89 was obtained for VAR model). },
        keywords = {Big Data, Dengue, Machine Learning, Prediction, Time-Series.},
        month = {},
        }