Sensitivity and identifiability analysis of COVID-19 pandemic models
The paper presents the results of sensitivity-based identif iability analysis of the COVID-19 pandemic spread models in the Novosibirsk region using the systems of differential equations and mass balance law. The algorithm is built on the sensitivity matrix analysis using the methods of differential...
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| Format: | Article |
| Language: | English |
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Siberian Branch of the Russian Academy of Sciences, Federal Research Center Institute of Cytology and Genetics, The Vavilov Society of Geneticists and Breeders
2021-03-01
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| Series: | Вавиловский журнал генетики и селекции |
| Subjects: | |
| Online Access: | https://vavilov.elpub.ru/jour/article/view/2919 |
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| author | O. I. Krivorotko S. I. Kabanikhin M. I. Sosnovskaya D. V. Andornaya |
| author_facet | O. I. Krivorotko S. I. Kabanikhin M. I. Sosnovskaya D. V. Andornaya |
| author_sort | O. I. Krivorotko |
| collection | DOAJ |
| description | The paper presents the results of sensitivity-based identif iability analysis of the COVID-19 pandemic spread models in the Novosibirsk region using the systems of differential equations and mass balance law. The algorithm is built on the sensitivity matrix analysis using the methods of differential and linear algebra. It allows one to determine the parameters that are the least and most sensitive to data changes to build a regularization for solving an identif ication problem of the most accurate pandemic spread scenarios in the region. The performed analysis has demonstrated that the virus contagiousness is identif iable from the number of daily conf irmed, critical and recovery cases. On the other hand, the predicted proportion of the admitted patients who require a ventilator and the mortality rate are determined much less consistently. It has been shown that building a more realistic forecast requires adding additional information about the process such as the number of daily hospital admissions. In our study, the problems of parameter identif ication using additional information about the number of daily conf irmed, critical and mortality cases in the region were reduced to minimizing the corresponding misf it functions. The minimization problem was solved through the differential evolution method that is widely applied for stochastic global optimization. It has been demonstrated that a more general COVID-19 spread compartmental model consisting of seven ordinary differential equations describes the main trend of the spread and is sensitive to the peaks of conf irmed cases but does not qualitatively describe small statistical datasets such as the number of daily critical cases or mortality that can lead to errors in forecasting. A more detailed agent-oriented model has been able to capture statistical data with additional noise to build scenarios of COVID-19 spread in the region. |
| first_indexed | 2024-03-07T16:05:22Z |
| format | Article |
| id | doaj.art-b2c12d6bf1644e63941135ac3f4005c8 |
| institution | Directory Open Access Journal |
| issn | 2500-3259 |
| language | English |
| last_indexed | 2024-04-24T11:07:40Z |
| publishDate | 2021-03-01 |
| publisher | Siberian Branch of the Russian Academy of Sciences, Federal Research Center Institute of Cytology and Genetics, The Vavilov Society of Geneticists and Breeders |
| record_format | Article |
| series | Вавиловский журнал генетики и селекции |
| spelling | doaj.art-b2c12d6bf1644e63941135ac3f4005c82024-04-11T15:31:03ZengSiberian Branch of the Russian Academy of Sciences, Federal Research Center Institute of Cytology and Genetics, The Vavilov Society of Geneticists and BreedersВавиловский журнал генетики и селекции2500-32592021-03-01251829110.18699/VJ21.0101135Sensitivity and identifiability analysis of COVID-19 pandemic modelsO. I. Krivorotko0S. I. Kabanikhin1M. I. Sosnovskaya2D. V. Andornaya3Institute of Computational Mathematics and Mathematical Geophysics of Siberian Branch of the Russian Academy of Sciences; Novosibirsk State UniversityInstitute of Computational Mathematics and Mathematical Geophysics of Siberian Branch of the Russian Academy of Sciences; Novosibirsk State UniversityNovosibirsk State UniversityNovosibirsk State UniversityThe paper presents the results of sensitivity-based identif iability analysis of the COVID-19 pandemic spread models in the Novosibirsk region using the systems of differential equations and mass balance law. The algorithm is built on the sensitivity matrix analysis using the methods of differential and linear algebra. It allows one to determine the parameters that are the least and most sensitive to data changes to build a regularization for solving an identif ication problem of the most accurate pandemic spread scenarios in the region. The performed analysis has demonstrated that the virus contagiousness is identif iable from the number of daily conf irmed, critical and recovery cases. On the other hand, the predicted proportion of the admitted patients who require a ventilator and the mortality rate are determined much less consistently. It has been shown that building a more realistic forecast requires adding additional information about the process such as the number of daily hospital admissions. In our study, the problems of parameter identif ication using additional information about the number of daily conf irmed, critical and mortality cases in the region were reduced to minimizing the corresponding misf it functions. The minimization problem was solved through the differential evolution method that is widely applied for stochastic global optimization. It has been demonstrated that a more general COVID-19 spread compartmental model consisting of seven ordinary differential equations describes the main trend of the spread and is sensitive to the peaks of conf irmed cases but does not qualitatively describe small statistical datasets such as the number of daily critical cases or mortality that can lead to errors in forecasting. A more detailed agent-oriented model has been able to capture statistical data with additional noise to build scenarios of COVID-19 spread in the region.https://vavilov.elpub.ru/jour/article/view/2919parameter sensitivityidentif iabilityordinary differential equationsinverse problemsepidemiologycovid-19forecastingnovosibirsk region |
| spellingShingle | O. I. Krivorotko S. I. Kabanikhin M. I. Sosnovskaya D. V. Andornaya Sensitivity and identifiability analysis of COVID-19 pandemic models Вавиловский журнал генетики и селекции parameter sensitivity identif iability ordinary differential equations inverse problems epidemiology covid-19 forecasting novosibirsk region |
| title | Sensitivity and identifiability analysis of COVID-19 pandemic models |
| title_full | Sensitivity and identifiability analysis of COVID-19 pandemic models |
| title_fullStr | Sensitivity and identifiability analysis of COVID-19 pandemic models |
| title_full_unstemmed | Sensitivity and identifiability analysis of COVID-19 pandemic models |
| title_short | Sensitivity and identifiability analysis of COVID-19 pandemic models |
| title_sort | sensitivity and identifiability analysis of covid 19 pandemic models |
| topic | parameter sensitivity identif iability ordinary differential equations inverse problems epidemiology covid-19 forecasting novosibirsk region |
| url | https://vavilov.elpub.ru/jour/article/view/2919 |
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