Computing R0 of dynamic models by a definition-based method
Objectives: Computing the basic reproduction number (R0) in deterministic dynamical models is a hot topic and is frequently demanded by researchers in public health. The next-generation methods (NGM) are widely used for such computation, however, the results of NGM are usually not to be the true R0...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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KeAi Communications Co., Ltd.
2022-06-01
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| Series: | Infectious Disease Modelling |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2468042722000288 |
| _version_ | 1797203548080963584 |
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| author | Xiaohao Guo Yichao Guo Zeyu Zhao Shiting Yang Yanhua Su Benhua Zhao Tianmu Chen |
| author_facet | Xiaohao Guo Yichao Guo Zeyu Zhao Shiting Yang Yanhua Su Benhua Zhao Tianmu Chen |
| author_sort | Xiaohao Guo |
| collection | DOAJ |
| description | Objectives: Computing the basic reproduction number (R0) in deterministic dynamical models is a hot topic and is frequently demanded by researchers in public health. The next-generation methods (NGM) are widely used for such computation, however, the results of NGM are usually not to be the true R0 but only a threshold quantity with little interpretation. In this paper, a definition-based method (DBM) is proposed to solve such a problem. Methods: Start with the definition of R0, consider different states that one infected individual may develop into, and take expectations. A comparison with NGM has proceeded. Numerical verification is performed using parameters fitted by data of COVID-19 in Hunan Province. Results: DBM and NGM give identical expressions for single-host models with single-group and interactive Rij of single-host models with multi-groups, while difference arises for models partitioned into subgroups. Numerical verification showed the consistencies and differences between DBM and NGM, which supports the conclusion that R0 derived by DBM with true epidemiological interpretations are better. Conclusions: DBM is more suitable for single-host models, especially for models partitioned into subgroups. However, for multi-host dynamic models where the true R0 is failed to define, we may turn to the NGM for the threshold R0. |
| first_indexed | 2024-04-24T08:21:05Z |
| format | Article |
| id | doaj.art-148a34e0c01746d6884931435b589bea |
| institution | Directory Open Access Journal |
| issn | 2468-0427 |
| language | English |
| last_indexed | 2024-04-24T08:21:05Z |
| publishDate | 2022-06-01 |
| publisher | KeAi Communications Co., Ltd. |
| record_format | Article |
| series | Infectious Disease Modelling |
| spelling | doaj.art-148a34e0c01746d6884931435b589bea2024-04-17T01:01:19ZengKeAi Communications Co., Ltd.Infectious Disease Modelling2468-04272022-06-0172196210Computing R0 of dynamic models by a definition-based methodXiaohao Guo0Yichao Guo1Zeyu Zhao2Shiting Yang3Yanhua Su4Benhua Zhao5Tianmu Chen6State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen City, 361102, Fujian Province, People's Republic of ChinaState Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen City, 361102, Fujian Province, People's Republic of ChinaState Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen City, 361102, Fujian Province, People's Republic of China; Université de Montpellier, CIRAD, Intertryp, IES, Université de Montpellier-CNRS, Montpellier, FranceState Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen City, 361102, Fujian Province, People's Republic of ChinaState Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen City, 361102, Fujian Province, People's Republic of ChinaState Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen City, 361102, Fujian Province, People's Republic of ChinaState Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen City, 361102, Fujian Province, People's Republic of China; Corresponding author. State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, 4221-117, South Xiang'an Road, Xiang'an District, Xiamen City, Fujian Province, People's Republic of China.Objectives: Computing the basic reproduction number (R0) in deterministic dynamical models is a hot topic and is frequently demanded by researchers in public health. The next-generation methods (NGM) are widely used for such computation, however, the results of NGM are usually not to be the true R0 but only a threshold quantity with little interpretation. In this paper, a definition-based method (DBM) is proposed to solve such a problem. Methods: Start with the definition of R0, consider different states that one infected individual may develop into, and take expectations. A comparison with NGM has proceeded. Numerical verification is performed using parameters fitted by data of COVID-19 in Hunan Province. Results: DBM and NGM give identical expressions for single-host models with single-group and interactive Rij of single-host models with multi-groups, while difference arises for models partitioned into subgroups. Numerical verification showed the consistencies and differences between DBM and NGM, which supports the conclusion that R0 derived by DBM with true epidemiological interpretations are better. Conclusions: DBM is more suitable for single-host models, especially for models partitioned into subgroups. However, for multi-host dynamic models where the true R0 is failed to define, we may turn to the NGM for the threshold R0.http://www.sciencedirect.com/science/article/pii/S2468042722000288Definition-based methodDynamics modelBasic reproduction numberNext-generation method |
| spellingShingle | Xiaohao Guo Yichao Guo Zeyu Zhao Shiting Yang Yanhua Su Benhua Zhao Tianmu Chen Computing R0 of dynamic models by a definition-based method Infectious Disease Modelling Definition-based method Dynamics model Basic reproduction number Next-generation method |
| title | Computing R0 of dynamic models by a definition-based method |
| title_full | Computing R0 of dynamic models by a definition-based method |
| title_fullStr | Computing R0 of dynamic models by a definition-based method |
| title_full_unstemmed | Computing R0 of dynamic models by a definition-based method |
| title_short | Computing R0 of dynamic models by a definition-based method |
| title_sort | computing r0 of dynamic models by a definition based method |
| topic | Definition-based method Dynamics model Basic reproduction number Next-generation method |
| url | http://www.sciencedirect.com/science/article/pii/S2468042722000288 |
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