An ensemble neural network approach to forecast Dengue outbreak based on climatic condition
Dengue fever is a virulent disease spreading over 100 tropical and subtropical countries in Africa, the Americas, and Asia. This arboviral disease affects around 400 million people globally, severely distressing the healthcare systems. The unavailability of a specific drug and ready-to-use vaccine m...
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| Other Authors: | , , , , |
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2023
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| Online Access: | https://depot.sorbonne.ae/handle/20.500.12458/1380 |
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| _version_ | 1857415062268936193 |
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| author | Panja, Madhurima |
| author2 | Chakraborty, Tanujit Nadim. Sk Shahid Ghosh, Indrajit Kumar, Uttam Liu, Nan |
| author2_role | author author author author author |
| author_facet | Panja, Madhurima Chakraborty, Tanujit Nadim. Sk Shahid Ghosh, Indrajit Kumar, Uttam Liu, Nan |
| author_role | author |
| dc.creator.none.fl_str_mv | Panja, Madhurima Chakraborty, Tanujit Nadim. Sk Shahid Ghosh, Indrajit Kumar, Uttam Liu, Nan |
| dc.date.none.fl_str_mv | 2023-01-13T04:25:25Z 2023-01-13T04:25:25Z 2023 |
| dc.format.none.fl_str_mv | application/pdf |
| dc.identifier.none.fl_str_mv | 10.1016/j.chaos.2023.113124 0960-0779 https://depot.sorbonne.ae/handle/20.500.12458/1380 10.1016/j.chaos.2023.113124 |
| dc.language.none.fl_str_mv | en |
| dc.relation.none.fl_str_mv | Chaos, Solitons & Fractals |
| dc.subject.none.fl_str_mv | Dengue Wavelet transform Forecasting MODWT Neural networks Ensemble |
| dc.title.none.fl_str_mv | An ensemble neural network approach to forecast Dengue outbreak based on climatic condition |
| dc.type.none.fl_str_mv | Controlled Vocabulary for Resource Type Genres::text::periodical::journal::contribution to journal::journal article |
| description | Dengue fever is a virulent disease spreading over 100 tropical and subtropical countries in Africa, the Americas, and Asia. This arboviral disease affects around 400 million people globally, severely distressing the healthcare systems. The unavailability of a specific drug and ready-to-use vaccine makes the situation worse. Hence, policymakers must rely on early warning systems to control intervention-related decisions. Forecasts routinely provide critical information for dangerous epidemic events. However, the available forecasting models (e.g., weather-driven mechanistic, statistical time series, and machine learning models) lack a clear understanding of different components to improve prediction accuracy and often provide unstable and unreliable forecasts. This study proposes an ensemble wavelet neural network with exogenous factor(s) (XEWNet) model that can produce reliable estimates for dengue outbreak prediction for three geographical regions, namely San Juan, Iquitos, and Ahmedabad. The proposed XEWNet model is flexible and can easily incorporate exogenous climate variable(s) confirmed by statistical causality tests in its scalable framework. The proposed model is an integrated approach that uses wavelet transformation into an ensemble neural network framework that helps in generating more reliable long-term forecasts. The proposed XEWNet allows complex non-linear relationships between the dengue incidence cases and rainfall; however, mathematically interpretable, fast in execution, and easily comprehensible. The proposal's competitiveness is measured using computational experiments based on various statistical metrics and several statistical comparison tests. In comparison with statistical, machine learning, and deep learning methods, our proposed XEWNet performs better in 75% of the cases for short-term and long-term forecasting of dengue incidence. |
| id | sorbonner_ebe1e0a697601e69e58fffef6b674f1c |
| identifier_str_mv | 10.1016/j.chaos.2023.113124 0960-0779 |
| language_invalid_str_mv | en |
| network_acronym_str | sorbonner |
| network_name_str | Sorbonne University Abu Dhabi repository |
| oai_identifier_str | oai:depot.sorbonne.ae:20.500.12458/1380 |
| publishDate | 2023 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| spelling | An ensemble neural network approach to forecast Dengue outbreak based on climatic conditionPanja, MadhurimaChakraborty, TanujitNadim. Sk ShahidGhosh, IndrajitKumar, UttamLiu, NanDengueWavelet transformForecastingMODWTNeural networksEnsembleDengue fever is a virulent disease spreading over 100 tropical and subtropical countries in Africa, the Americas, and Asia. This arboviral disease affects around 400 million people globally, severely distressing the healthcare systems. The unavailability of a specific drug and ready-to-use vaccine makes the situation worse. Hence, policymakers must rely on early warning systems to control intervention-related decisions. Forecasts routinely provide critical information for dangerous epidemic events. However, the available forecasting models (e.g., weather-driven mechanistic, statistical time series, and machine learning models) lack a clear understanding of different components to improve prediction accuracy and often provide unstable and unreliable forecasts. This study proposes an ensemble wavelet neural network with exogenous factor(s) (XEWNet) model that can produce reliable estimates for dengue outbreak prediction for three geographical regions, namely San Juan, Iquitos, and Ahmedabad. The proposed XEWNet model is flexible and can easily incorporate exogenous climate variable(s) confirmed by statistical causality tests in its scalable framework. The proposed model is an integrated approach that uses wavelet transformation into an ensemble neural network framework that helps in generating more reliable long-term forecasts. The proposed XEWNet allows complex non-linear relationships between the dengue incidence cases and rainfall; however, mathematically interpretable, fast in execution, and easily comprehensible. The proposal's competitiveness is measured using computational experiments based on various statistical metrics and several statistical comparison tests. In comparison with statistical, machine learning, and deep learning methods, our proposed XEWNet performs better in 75% of the cases for short-term and long-term forecasting of dengue incidence.2023-01-13T04:25:25Z2023-01-13T04:25:25Z2023Controlled Vocabulary for Resource Type Genres::text::periodical::journal::contribution to journal::journal articleapplication/pdf10.1016/j.chaos.2023.1131240960-0779https://depot.sorbonne.ae/handle/20.500.12458/138010.1016/j.chaos.2023.113124enChaos, Solitons & Fractalsoai:depot.sorbonne.ae:20.500.12458/13802024-03-06T09:40:12Z |
| spellingShingle | An ensemble neural network approach to forecast Dengue outbreak based on climatic condition Panja, Madhurima Dengue Wavelet transform Forecasting MODWT Neural networks Ensemble |
| title | An ensemble neural network approach to forecast Dengue outbreak based on climatic condition |
| title_full | An ensemble neural network approach to forecast Dengue outbreak based on climatic condition |
| title_fullStr | An ensemble neural network approach to forecast Dengue outbreak based on climatic condition |
| title_full_unstemmed | An ensemble neural network approach to forecast Dengue outbreak based on climatic condition |
| title_short | An ensemble neural network approach to forecast Dengue outbreak based on climatic condition |
| title_sort | An ensemble neural network approach to forecast Dengue outbreak based on climatic condition |
| topic | Dengue Wavelet transform Forecasting MODWT Neural networks Ensemble |
| url | https://depot.sorbonne.ae/handle/20.500.12458/1380 |