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Analysis of the COVID-19 pandemic using a compartmental model with time-varying parameters fitted by a genetic algorithm
Analyzing the COVID-19 pandemic is a critical factor in developing effective policies to deal with similar challenges in the future. However, many parameters (e.g., the actual number of infected people, the effectiveness of vaccination) are still subject to considerable debate because they are unobs...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier Ltd.
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10072952/ https://www.ncbi.nlm.nih.gov/pubmed/37033691 http://dx.doi.org/10.1016/j.eswa.2023.120034 |
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author | Zelenkov, Yuri Reshettsov, Ivan |
author_facet | Zelenkov, Yuri Reshettsov, Ivan |
author_sort | Zelenkov, Yuri |
collection | PubMed |
description | Analyzing the COVID-19 pandemic is a critical factor in developing effective policies to deal with similar challenges in the future. However, many parameters (e.g., the actual number of infected people, the effectiveness of vaccination) are still subject to considerable debate because they are unobservable. To model a pandemic and estimate unobserved parameters, researchers use compartmental models. Most often, in such models, the transition rates are considered as constants, which allows simulating only one epidemiological wave. However, multiple waves have been reported for COVID-19 caused by different strains of the virus. This paper presents an approach based on the reconstruction of real distributions of transition rates using genetic algorithms, which makes it possible to create a model that describes several pandemic peaks. The model is fitted on registered COVID-19 cases in four countries with different pandemic control strategies (Germany, Sweden, UK, and US). Mean absolute percentage error (MAPE) was chosen as the objective function, the MAPE values of 2.168%, 2.096%, 1.208% and 1.703% were achieved for the listed countries, respectively. Simulation results are consistent with the empirical statistics of medical studies, which confirms the quality of the model. In addition to observables such as registered infected, the output of the model contains variables that cannot be measured directly. Among them are the proportion of the population protected by vaccines, the size of the exposed compartment, and the number of unregistered cases of COVID-19. According to the results, at the peak of the pandemic, between 14% (Sweden) and 25% (the UK) of the population were infected. At the same time, the number of unregistered cases exceeds the number of registered cases by 17 and 3.4 times, respectively. The average duration of the vaccine induced immune period is shorter than claimed by vaccine manufacturers, and the effectiveness of vaccination has declined sharply since the appearance of the Delta and Omicron strains. However, on average, vaccination reduces the risk of infection by about 65–70%. |
format | Online Article Text |
id | pubmed-10072952 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Elsevier Ltd. |
record_format | MEDLINE/PubMed |
spelling | pubmed-100729522023-04-05 Analysis of the COVID-19 pandemic using a compartmental model with time-varying parameters fitted by a genetic algorithm Zelenkov, Yuri Reshettsov, Ivan Expert Syst Appl Article Analyzing the COVID-19 pandemic is a critical factor in developing effective policies to deal with similar challenges in the future. However, many parameters (e.g., the actual number of infected people, the effectiveness of vaccination) are still subject to considerable debate because they are unobservable. To model a pandemic and estimate unobserved parameters, researchers use compartmental models. Most often, in such models, the transition rates are considered as constants, which allows simulating only one epidemiological wave. However, multiple waves have been reported for COVID-19 caused by different strains of the virus. This paper presents an approach based on the reconstruction of real distributions of transition rates using genetic algorithms, which makes it possible to create a model that describes several pandemic peaks. The model is fitted on registered COVID-19 cases in four countries with different pandemic control strategies (Germany, Sweden, UK, and US). Mean absolute percentage error (MAPE) was chosen as the objective function, the MAPE values of 2.168%, 2.096%, 1.208% and 1.703% were achieved for the listed countries, respectively. Simulation results are consistent with the empirical statistics of medical studies, which confirms the quality of the model. In addition to observables such as registered infected, the output of the model contains variables that cannot be measured directly. Among them are the proportion of the population protected by vaccines, the size of the exposed compartment, and the number of unregistered cases of COVID-19. According to the results, at the peak of the pandemic, between 14% (Sweden) and 25% (the UK) of the population were infected. At the same time, the number of unregistered cases exceeds the number of registered cases by 17 and 3.4 times, respectively. The average duration of the vaccine induced immune period is shorter than claimed by vaccine manufacturers, and the effectiveness of vaccination has declined sharply since the appearance of the Delta and Omicron strains. However, on average, vaccination reduces the risk of infection by about 65–70%. Elsevier Ltd. 2023-08-15 2023-04-05 /pmc/articles/PMC10072952/ /pubmed/37033691 http://dx.doi.org/10.1016/j.eswa.2023.120034 Text en © 2023 Elsevier Ltd. All rights reserved. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. |
spellingShingle | Article Zelenkov, Yuri Reshettsov, Ivan Analysis of the COVID-19 pandemic using a compartmental model with time-varying parameters fitted by a genetic algorithm |
title | Analysis of the COVID-19 pandemic using a compartmental model with time-varying parameters fitted by a genetic algorithm |
title_full | Analysis of the COVID-19 pandemic using a compartmental model with time-varying parameters fitted by a genetic algorithm |
title_fullStr | Analysis of the COVID-19 pandemic using a compartmental model with time-varying parameters fitted by a genetic algorithm |
title_full_unstemmed | Analysis of the COVID-19 pandemic using a compartmental model with time-varying parameters fitted by a genetic algorithm |
title_short | Analysis of the COVID-19 pandemic using a compartmental model with time-varying parameters fitted by a genetic algorithm |
title_sort | analysis of the covid-19 pandemic using a compartmental model with time-varying parameters fitted by a genetic algorithm |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10072952/ https://www.ncbi.nlm.nih.gov/pubmed/37033691 http://dx.doi.org/10.1016/j.eswa.2023.120034 |
work_keys_str_mv | AT zelenkovyuri analysisofthecovid19pandemicusingacompartmentalmodelwithtimevaryingparametersfittedbyageneticalgorithm AT reshettsovivan analysisofthecovid19pandemicusingacompartmentalmodelwithtimevaryingparametersfittedbyageneticalgorithm |