<italic>ABV-CoViD</italic>: An Ensemble Forecasting Model to Predict Availability of Beds and Ventilators for COVID-19 Like Pandemics
Recently, healthcare stakeholders have orchestrated steps to strengthen and curb the COVID-19 wave. There has been a surge in vaccinations to curb the virus wave, but it is crucial to strengthen our healthcare resources to fight COVID-19 and like pandemics. Recent researchers have suggested effectiv...
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9828031/ |
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| author | Vivek Kumar Prasad Pronaya Bhattacharya Madhuri Bhavsar Ashwin Verma Sudeep Tanwar Gulshan Sharma Pitshou N. Bokoro Ravi Sharma |
| author_facet | Vivek Kumar Prasad Pronaya Bhattacharya Madhuri Bhavsar Ashwin Verma Sudeep Tanwar Gulshan Sharma Pitshou N. Bokoro Ravi Sharma |
| author_sort | Vivek Kumar Prasad |
| collection | DOAJ |
| description | Recently, healthcare stakeholders have orchestrated steps to strengthen and curb the COVID-19 wave. There has been a surge in vaccinations to curb the virus wave, but it is crucial to strengthen our healthcare resources to fight COVID-19 and like pandemics. Recent researchers have suggested effective forecasting models for COVID-19 transmission rate, spread, and the number of positive cases, but the focus on healthcare resources to meet the current spread is not discussed. Motivated from the gap, in this paper, we propose a scheme, <italic>ABV-CoViD</italic> (<bold>A</bold>vailibility of <bold>B</bold>eds and <bold>V</bold>entilators for <bold>COVID</bold>-19 patients), that forms an ensemble forecasting model to predict the availability of beds and ventilators (ABV) for the COVID-19 patients. The scheme considers a region-wise demarcation for the allotment of beds and ventilators (BV), termed resources, based on region-wise ABV and COVID-19 positive patients (inside the hospitals occupying the BV resource). We consider an integration of artificial neural network (ANN) and auto-regressive integrated neural network (ARIMA) model to address both the linear and non-linear dependencies. We also consider the effective wave spread of COVID-19 on external patients (not occupying the BV resources) through a <inline-formula> <tex-math notation="LaTeX">$\theta $ </tex-math></inline-formula>- ARNN model, which gives us long-term complex dependencies of BV resources in the future time window. We have considered the COVID-19 healthcare dataset on 3 USA regions (Illinois, Michigan, and Indiana) for testing our ensemble forecasting scheme from January 2021 to May 2022. We evaluated our scheme in terms of statistical performance metrics and validated that ensemble methods have higher accuracy. In simulation, for linear modelling, we considered the <inline-formula> <tex-math notation="LaTeX">$ARIMA(1,0,12)$ </tex-math></inline-formula> model, and <inline-formula> <tex-math notation="LaTeX">$N^{8-3-2}$ </tex-math></inline-formula> model for ANN modelling. We considered the <inline-formula> <tex-math notation="LaTeX">$\theta -ARNN$ </tex-math></inline-formula>(12,6) forecasting. On a population of 2,93,90,897, the obtained mean absolute error (MAE) on average for 3 regions is 170.5514. The average root means square error (RMSE) of <inline-formula> <tex-math notation="LaTeX">$\theta $ </tex-math></inline-formula>-ARNN is 333.18, with an accuracy of 98.876%, which shows the scheme’s efficacy in ABV measurement over conventional and manual resource allocation schemes. |
| first_indexed | 2024-04-13T21:14:31Z |
| format | Article |
| id | doaj.art-e738515abf87424e8ed6753d37a4ef0e |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-13T21:14:31Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-e738515abf87424e8ed6753d37a4ef0e2022-12-22T02:29:45ZengIEEEIEEE Access2169-35362022-01-0110741317415110.1109/ACCESS.2022.31904979828031<italic>ABV-CoViD</italic>: An Ensemble Forecasting Model to Predict Availability of Beds and Ventilators for COVID-19 Like PandemicsVivek Kumar Prasad0https://orcid.org/0000-0003-4942-8094Pronaya Bhattacharya1https://orcid.org/0000-0002-1206-2298Madhuri Bhavsar2https://orcid.org/0000-0003-3576-9947Ashwin Verma3https://orcid.org/0000-0001-8904-228XSudeep Tanwar4https://orcid.org/0000-0002-1776-4651Gulshan Sharma5Pitshou N. Bokoro6https://orcid.org/0000-0002-9178-2700Ravi Sharma7https://orcid.org/0000-0002-8584-9753Department of Computer Science and Engineering, Institute of Technology, Nirma University, Ahmedabad, Gujarat, IndiaDepartment of Computer Science and Engineering, Institute of Technology, Nirma University, Ahmedabad, Gujarat, IndiaDepartment of Computer Science and Engineering, Institute of Technology, Nirma University, Ahmedabad, Gujarat, IndiaDepartment of Computer Science and Engineering, Institute of Technology, Nirma University, Ahmedabad, Gujarat, IndiaDepartment of Computer Science and Engineering, Institute of Technology, Nirma University, Ahmedabad, Gujarat, IndiaDepartment of Electrical Engineering Technology, University of Johannesburg, Johannesburg, South AfricaDepartment of Electrical Engineering Technology, University of Johannesburg, Johannesburg, South AfricaCentre for Inter-Disciplinary Research and Innovation, University of Petroleum and Energy Studies, Dehradun, IndiaRecently, healthcare stakeholders have orchestrated steps to strengthen and curb the COVID-19 wave. There has been a surge in vaccinations to curb the virus wave, but it is crucial to strengthen our healthcare resources to fight COVID-19 and like pandemics. Recent researchers have suggested effective forecasting models for COVID-19 transmission rate, spread, and the number of positive cases, but the focus on healthcare resources to meet the current spread is not discussed. Motivated from the gap, in this paper, we propose a scheme, <italic>ABV-CoViD</italic> (<bold>A</bold>vailibility of <bold>B</bold>eds and <bold>V</bold>entilators for <bold>COVID</bold>-19 patients), that forms an ensemble forecasting model to predict the availability of beds and ventilators (ABV) for the COVID-19 patients. The scheme considers a region-wise demarcation for the allotment of beds and ventilators (BV), termed resources, based on region-wise ABV and COVID-19 positive patients (inside the hospitals occupying the BV resource). We consider an integration of artificial neural network (ANN) and auto-regressive integrated neural network (ARIMA) model to address both the linear and non-linear dependencies. We also consider the effective wave spread of COVID-19 on external patients (not occupying the BV resources) through a <inline-formula> <tex-math notation="LaTeX">$\theta $ </tex-math></inline-formula>- ARNN model, which gives us long-term complex dependencies of BV resources in the future time window. We have considered the COVID-19 healthcare dataset on 3 USA regions (Illinois, Michigan, and Indiana) for testing our ensemble forecasting scheme from January 2021 to May 2022. We evaluated our scheme in terms of statistical performance metrics and validated that ensemble methods have higher accuracy. In simulation, for linear modelling, we considered the <inline-formula> <tex-math notation="LaTeX">$ARIMA(1,0,12)$ </tex-math></inline-formula> model, and <inline-formula> <tex-math notation="LaTeX">$N^{8-3-2}$ </tex-math></inline-formula> model for ANN modelling. We considered the <inline-formula> <tex-math notation="LaTeX">$\theta -ARNN$ </tex-math></inline-formula>(12,6) forecasting. On a population of 2,93,90,897, the obtained mean absolute error (MAE) on average for 3 regions is 170.5514. The average root means square error (RMSE) of <inline-formula> <tex-math notation="LaTeX">$\theta $ </tex-math></inline-formula>-ARNN is 333.18, with an accuracy of 98.876%, which shows the scheme’s efficacy in ABV measurement over conventional and manual resource allocation schemes.https://ieeexplore.ieee.org/document/9828031/Artificial neural networksARIMACOVID-19healthcare servicesIoTprediction models |
| spellingShingle | Vivek Kumar Prasad Pronaya Bhattacharya Madhuri Bhavsar Ashwin Verma Sudeep Tanwar Gulshan Sharma Pitshou N. Bokoro Ravi Sharma <italic>ABV-CoViD</italic>: An Ensemble Forecasting Model to Predict Availability of Beds and Ventilators for COVID-19 Like Pandemics IEEE Access Artificial neural networks ARIMA COVID-19 healthcare services IoT prediction models |
| title | <italic>ABV-CoViD</italic>: An Ensemble Forecasting Model to Predict Availability of Beds and Ventilators for COVID-19 Like Pandemics |
| title_full | <italic>ABV-CoViD</italic>: An Ensemble Forecasting Model to Predict Availability of Beds and Ventilators for COVID-19 Like Pandemics |
| title_fullStr | <italic>ABV-CoViD</italic>: An Ensemble Forecasting Model to Predict Availability of Beds and Ventilators for COVID-19 Like Pandemics |
| title_full_unstemmed | <italic>ABV-CoViD</italic>: An Ensemble Forecasting Model to Predict Availability of Beds and Ventilators for COVID-19 Like Pandemics |
| title_short | <italic>ABV-CoViD</italic>: An Ensemble Forecasting Model to Predict Availability of Beds and Ventilators for COVID-19 Like Pandemics |
| title_sort | italic abv covid italic an ensemble forecasting model to predict availability of beds and ventilators for covid 19 like pandemics |
| topic | Artificial neural networks ARIMA COVID-19 healthcare services IoT prediction models |
| url | https://ieeexplore.ieee.org/document/9828031/ |
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