COVID-19: Metaheuristic Optimization-Based Forecast Method on Time-Dependent Bootstrapped Data
A compounded method—exploiting the searching capabilities of an operation research algorithm and the power of bootstrap techniques—is presented. The resulting algorithm has been successfully tested to predict the turning point reached by the epidemic curve followed by the COVID-19 virus in Italy. Fu...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2021-01-01
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| Series: | Journal of Probability and Statistics |
| Online Access: | http://dx.doi.org/10.1155/2021/1235973 |
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| _version_ | 1832546997878390784 |
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| author | Livio Fenga Carlo Del Castello |
| author_facet | Livio Fenga Carlo Del Castello |
| author_sort | Livio Fenga |
| collection | DOAJ |
| description | A compounded method—exploiting the searching capabilities of an operation research algorithm and the power of bootstrap techniques—is presented. The resulting algorithm has been successfully tested to predict the turning point reached by the epidemic curve followed by the COVID-19 virus in Italy. Future lines of research, which include the generalization of the method to a broad set of distribution, will be finally given. |
| format | Article |
| id | doaj-art-2c23f9fab2fc4a7cae7bdcb3b16270c4 |
| institution | Kabale University |
| issn | 1687-952X 1687-9538 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Probability and Statistics |
| spelling | doaj-art-2c23f9fab2fc4a7cae7bdcb3b16270c42025-02-03T06:46:16ZengWileyJournal of Probability and Statistics1687-952X1687-95382021-01-01202110.1155/2021/12359731235973COVID-19: Metaheuristic Optimization-Based Forecast Method on Time-Dependent Bootstrapped DataLivio Fenga0Carlo Del Castello1Italian National Institute of Statistics ISTAT, Rome 00184, ItalyKantar Group Ltd., Bologna, ItalyA compounded method—exploiting the searching capabilities of an operation research algorithm and the power of bootstrap techniques—is presented. The resulting algorithm has been successfully tested to predict the turning point reached by the epidemic curve followed by the COVID-19 virus in Italy. Future lines of research, which include the generalization of the method to a broad set of distribution, will be finally given.http://dx.doi.org/10.1155/2021/1235973 |
| spellingShingle | Livio Fenga Carlo Del Castello COVID-19: Metaheuristic Optimization-Based Forecast Method on Time-Dependent Bootstrapped Data Journal of Probability and Statistics |
| title | COVID-19: Metaheuristic Optimization-Based Forecast Method on Time-Dependent Bootstrapped Data |
| title_full | COVID-19: Metaheuristic Optimization-Based Forecast Method on Time-Dependent Bootstrapped Data |
| title_fullStr | COVID-19: Metaheuristic Optimization-Based Forecast Method on Time-Dependent Bootstrapped Data |
| title_full_unstemmed | COVID-19: Metaheuristic Optimization-Based Forecast Method on Time-Dependent Bootstrapped Data |
| title_short | COVID-19: Metaheuristic Optimization-Based Forecast Method on Time-Dependent Bootstrapped Data |
| title_sort | covid 19 metaheuristic optimization based forecast method on time dependent bootstrapped data |
| url | http://dx.doi.org/10.1155/2021/1235973 |
| work_keys_str_mv | AT liviofenga covid19metaheuristicoptimizationbasedforecastmethodontimedependentbootstrappeddata AT carlodelcastello covid19metaheuristicoptimizationbasedforecastmethodontimedependentbootstrappeddata |