High-Order Mean-Field Approximations for Adaptive Susceptible-Infected-Susceptible Model in Finite-Size Networks
Exact solutions of epidemic models are critical for identifying the severity and mitigation possibility for epidemics. However, solving complex models can be difficult when interfering conditions from the real-world are incorporated into the models. In this paper, we focus on the generally unsolvabl...
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2021/6637761 |
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| author | Kai Wang Xiao Fan Liu Dongchao Guo |
| author_facet | Kai Wang Xiao Fan Liu Dongchao Guo |
| author_sort | Kai Wang |
| collection | DOAJ |
| description | Exact solutions of epidemic models are critical for identifying the severity and mitigation possibility for epidemics. However, solving complex models can be difficult when interfering conditions from the real-world are incorporated into the models. In this paper, we focus on the generally unsolvable adaptive susceptible-infected-susceptible (ASIS) epidemic model, a typical example of a class of epidemic models that characterize the complex interplays between the virus spread and network structural evolution. We propose two methods based on mean-field approximation, i.e., the first-order mean-field approximation (FOMFA) and higher-order mean-field approximation (HOMFA), to derive the exact solutions to ASIS models. Both methods demonstrate the capability of accurately approximating the metastable-state statistics of the model, such as the infection fraction and network density, with low computational cost. These methods are potentially powerful tools in understanding, mitigating, and controlling disease outbreaks and infodemics. |
| format | Article |
| id | doaj-art-9c9dc4ea2f0e4d8cb790fe1756950bf4 |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-9c9dc4ea2f0e4d8cb790fe1756950bf42025-08-20T03:38:16ZengWileyComplexity1076-27871099-05262021-01-01202110.1155/2021/66377616637761High-Order Mean-Field Approximations for Adaptive Susceptible-Infected-Susceptible Model in Finite-Size NetworksKai Wang0Xiao Fan Liu1Dongchao Guo2School of Computer Science and Technology, Harbin Institute of Technology, Weihai 264209, ChinaDepartment of Media and Communication, City University of Hong Kong, Kowloon Tong, Hong Kong SAR, ChinaSchool of Computer Science, Beijing Information Science and Technology University, Beijing 100192, ChinaExact solutions of epidemic models are critical for identifying the severity and mitigation possibility for epidemics. However, solving complex models can be difficult when interfering conditions from the real-world are incorporated into the models. In this paper, we focus on the generally unsolvable adaptive susceptible-infected-susceptible (ASIS) epidemic model, a typical example of a class of epidemic models that characterize the complex interplays between the virus spread and network structural evolution. We propose two methods based on mean-field approximation, i.e., the first-order mean-field approximation (FOMFA) and higher-order mean-field approximation (HOMFA), to derive the exact solutions to ASIS models. Both methods demonstrate the capability of accurately approximating the metastable-state statistics of the model, such as the infection fraction and network density, with low computational cost. These methods are potentially powerful tools in understanding, mitigating, and controlling disease outbreaks and infodemics.http://dx.doi.org/10.1155/2021/6637761 |
| spellingShingle | Kai Wang Xiao Fan Liu Dongchao Guo High-Order Mean-Field Approximations for Adaptive Susceptible-Infected-Susceptible Model in Finite-Size Networks Complexity |
| title | High-Order Mean-Field Approximations for Adaptive Susceptible-Infected-Susceptible Model in Finite-Size Networks |
| title_full | High-Order Mean-Field Approximations for Adaptive Susceptible-Infected-Susceptible Model in Finite-Size Networks |
| title_fullStr | High-Order Mean-Field Approximations for Adaptive Susceptible-Infected-Susceptible Model in Finite-Size Networks |
| title_full_unstemmed | High-Order Mean-Field Approximations for Adaptive Susceptible-Infected-Susceptible Model in Finite-Size Networks |
| title_short | High-Order Mean-Field Approximations for Adaptive Susceptible-Infected-Susceptible Model in Finite-Size Networks |
| title_sort | high order mean field approximations for adaptive susceptible infected susceptible model in finite size networks |
| url | http://dx.doi.org/10.1155/2021/6637761 |
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