Epicasting: An Ensemble Wavelet Neural Network for forecasting epidemics
Infectious diseases remain among the top contributors to human illness and death worldwide, among which many diseases produce epidemic waves of infection. The lack of specific drugs and ready-to-use vaccines to prevent most of these epidemics worsens the situation. These force public health official...
محفوظ في:
| المؤلف الرئيسي: | |
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| مؤلفون آخرون: | , , |
| منشور في: |
2023
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| الموضوعات: | |
| الوصول للمادة أونلاين: | https://depot.sorbonne.ae/handle/20.500.12458/1410 |
| الوسوم: |
إضافة وسم
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| _version_ | 1857415063929880576 |
|---|---|
| author | Panja, Madhurima |
| author2 | Chakraborty, Tanujit Kumar, Uttam Liu, Nan |
| author2_role | author author author |
| author_facet | Panja, Madhurima Chakraborty, Tanujit Kumar, Uttam Liu, Nan |
| author_role | author |
| dc.creator.none.fl_str_mv | Panja, Madhurima Chakraborty, Tanujit Kumar, Uttam Liu, Nan |
| dc.date.none.fl_str_mv | 2023-06-05T05:15:51Z 2023-06-05T05:15:51Z 2023 |
| dc.identifier.none.fl_str_mv | 0893-6080 https://depot.sorbonne.ae/handle/20.500.12458/1410 10.1016/j.neunet.2023.05.049 |
| dc.language.none.fl_str_mv | en |
| dc.relation.none.fl_str_mv | Neural Networks |
| dc.subject.none.fl_str_mv | Wavelet methods MODWT Epidemiology Neural networks Time series forecasting |
| dc.title.none.fl_str_mv | Epicasting: An Ensemble Wavelet Neural Network for forecasting epidemics |
| dc.type.none.fl_str_mv | Controlled Vocabulary for Resource Type Genres::text::periodical::journal::contribution to journal::journal article |
| description | Infectious diseases remain among the top contributors to human illness and death worldwide, among which many diseases produce epidemic waves of infection. The lack of specific drugs and ready-to-use vaccines to prevent most of these epidemics worsens the situation. These force public health officials and policymakers to rely on early warning systems generated by accurate and reliable epidemic forecasters. Accurate forecasts of epidemics can assist stakeholders in tailoring countermeasures, such as vaccination campaigns, staff scheduling, and resource allocation, to the situation at hand, which could translate to reductions in the impact of a disease. Unfortunately, most of these past epidemics exhibit nonlinear and non-stationary characteristics due to their spreading fluctuations based on seasonal-dependent variability and the nature of these epidemics. We analyze various epidemic time series datasets using a maximal overlap discrete wavelet transform (MODWT) based autoregressive neural network and call it Ensemble Wavelet Neural Network (EWNet) model. MODWT techniques effectively characterize non-stationary behavior and seasonal dependencies in the epidemic time series and improve the nonlinear forecasting scheme of the autoregressive neural network in the proposed ensemble wavelet network framework. From a nonlinear time series viewpoint, we explore the asymptotic stationarity of the proposed EWNet model to show the asymptotic behavior of the associated Markov Chain. We also theoretically investigate the effect of learning stability and the choice of hidden neurons in the proposal. From a practical perspective, we compare our proposed EWNet framework with twenty-two statistical, machine learning, and deep learning models for fifteen real-world epidemic datasets with three test horizons using four key performance indicators. Experimental results show that the proposed EWNet is highly competitive compared to the state-of-the-art epidemic forecasting methods. |
| id | sorbonner_ac69f424accc94f3848130cea05f41e1 |
| identifier_str_mv | 0893-6080 10.1016/j.neunet.2023.05.049 |
| language_invalid_str_mv | en |
| network_acronym_str | sorbonner |
| network_name_str | Sorbonne University Abu Dhabi repository |
| oai_identifier_str | oai:depot.sorbonne.ae:20.500.12458/1410 |
| publishDate | 2023 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| spelling | Epicasting: An Ensemble Wavelet Neural Network for forecasting epidemicsPanja, MadhurimaChakraborty, TanujitKumar, UttamLiu, NanWavelet methodsMODWTEpidemiologyNeural networksTime series forecastingInfectious diseases remain among the top contributors to human illness and death worldwide, among which many diseases produce epidemic waves of infection. The lack of specific drugs and ready-to-use vaccines to prevent most of these epidemics worsens the situation. These force public health officials and policymakers to rely on early warning systems generated by accurate and reliable epidemic forecasters. Accurate forecasts of epidemics can assist stakeholders in tailoring countermeasures, such as vaccination campaigns, staff scheduling, and resource allocation, to the situation at hand, which could translate to reductions in the impact of a disease. Unfortunately, most of these past epidemics exhibit nonlinear and non-stationary characteristics due to their spreading fluctuations based on seasonal-dependent variability and the nature of these epidemics. We analyze various epidemic time series datasets using a maximal overlap discrete wavelet transform (MODWT) based autoregressive neural network and call it Ensemble Wavelet Neural Network (EWNet) model. MODWT techniques effectively characterize non-stationary behavior and seasonal dependencies in the epidemic time series and improve the nonlinear forecasting scheme of the autoregressive neural network in the proposed ensemble wavelet network framework. From a nonlinear time series viewpoint, we explore the asymptotic stationarity of the proposed EWNet model to show the asymptotic behavior of the associated Markov Chain. We also theoretically investigate the effect of learning stability and the choice of hidden neurons in the proposal. From a practical perspective, we compare our proposed EWNet framework with twenty-two statistical, machine learning, and deep learning models for fifteen real-world epidemic datasets with three test horizons using four key performance indicators. Experimental results show that the proposed EWNet is highly competitive compared to the state-of-the-art epidemic forecasting methods.2023-06-05T05:15:51Z2023-06-05T05:15:51Z2023Controlled Vocabulary for Resource Type Genres::text::periodical::journal::contribution to journal::journal article0893-6080https://depot.sorbonne.ae/handle/20.500.12458/141010.1016/j.neunet.2023.05.049enNeural Networksoai:depot.sorbonne.ae:20.500.12458/14102023-06-14T09:37:14Z |
| spellingShingle | Epicasting: An Ensemble Wavelet Neural Network for forecasting epidemics Panja, Madhurima Wavelet methods MODWT Epidemiology Neural networks Time series forecasting |
| title | Epicasting: An Ensemble Wavelet Neural Network for forecasting epidemics |
| title_full | Epicasting: An Ensemble Wavelet Neural Network for forecasting epidemics |
| title_fullStr | Epicasting: An Ensemble Wavelet Neural Network for forecasting epidemics |
| title_full_unstemmed | Epicasting: An Ensemble Wavelet Neural Network for forecasting epidemics |
| title_short | Epicasting: An Ensemble Wavelet Neural Network for forecasting epidemics |
| title_sort | Epicasting: An Ensemble Wavelet Neural Network for forecasting epidemics |
| topic | Wavelet methods MODWT Epidemiology Neural networks Time series forecasting |
| url | https://depot.sorbonne.ae/handle/20.500.12458/1410 |