Voluntary Vaccination through Perceiving Epidemic Severity in Social Networks
The severity of an epidemic has a significant impact on individual vaccinating decisions under voluntary vaccination. During the epidemic of a vaccine-preventable disease, individuals in a social network can perceive the infection risks based on global information announced by public health authorit...
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| Format: | Article |
| Language: | English |
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Wiley
2019-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2019/3901218 |
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| _version_ | 1850157406491770880 |
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| author | Benyun Shi Guangliang Liu Hongjun Qiu Yu-Wang Chen Shaoliang Peng |
| author_facet | Benyun Shi Guangliang Liu Hongjun Qiu Yu-Wang Chen Shaoliang Peng |
| author_sort | Benyun Shi |
| collection | DOAJ |
| description | The severity of an epidemic has a significant impact on individual vaccinating decisions under voluntary vaccination. During the epidemic of a vaccine-preventable disease, individuals in a social network can perceive the infection risks based on global information announced by public health authorities, or local information obtained from their social neighbors. After that, they can rationally decide whether or not to take the vaccine through weighing the relative cost of vaccination and infection (i.e., relative vaccine cost). In this case, both social network structure and individuals’ risk perception strategies will affect the final vaccine coverage. In this paper, we focus on the problem of how individuals’ perceptions on epidemic severity affect their vaccinating behaviors in the face of flu-like seasonal diseases in social networks, and vice versa. Specifically, we first present three types of static decision-making mechanisms, each of which simulates human vaccinating behaviors based on different local/global information. On this basis, we further present a reinforcement-learning-based mechanism, where individuals can use their historical vaccination experiences to determine what information is more suitable to estimate the severity of the epidemic. Finally, we carry out simulations on three types of social networks to investigate the effects of network structure, source of information, relative vaccine cost, and individual social connections on the final vaccine coverage and epidemic size. The results and findings can provide a new insight for designing incentive-based vaccination policies and intervention strategies for flu-like seasonal diseases. |
| format | Article |
| id | doaj-art-3964b0ce3cb14f52b1b2f06cd20a95e6 |
| institution | OA Journals |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2019-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-3964b0ce3cb14f52b1b2f06cd20a95e62025-08-20T02:24:09ZengWileyComplexity1076-27871099-05262019-01-01201910.1155/2019/39012183901218Voluntary Vaccination through Perceiving Epidemic Severity in Social NetworksBenyun Shi0Guangliang Liu1Hongjun Qiu2Yu-Wang Chen3Shaoliang Peng4School of Cyberspace, Hangzhou Dianzi University, Hangzhou 310018, ChinaSchool of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou 310018, ChinaSchool of Cyberspace, Hangzhou Dianzi University, Hangzhou 310018, ChinaDecision and Cognitive Sciences Research Centre, The University of Manchester, Manchester, M13 9SS, UKCollege of Computer Science and Electronic Engineering & National Supercomputer Centre in Changsha, Hunan University, Changsha 410082, ChinaThe severity of an epidemic has a significant impact on individual vaccinating decisions under voluntary vaccination. During the epidemic of a vaccine-preventable disease, individuals in a social network can perceive the infection risks based on global information announced by public health authorities, or local information obtained from their social neighbors. After that, they can rationally decide whether or not to take the vaccine through weighing the relative cost of vaccination and infection (i.e., relative vaccine cost). In this case, both social network structure and individuals’ risk perception strategies will affect the final vaccine coverage. In this paper, we focus on the problem of how individuals’ perceptions on epidemic severity affect their vaccinating behaviors in the face of flu-like seasonal diseases in social networks, and vice versa. Specifically, we first present three types of static decision-making mechanisms, each of which simulates human vaccinating behaviors based on different local/global information. On this basis, we further present a reinforcement-learning-based mechanism, where individuals can use their historical vaccination experiences to determine what information is more suitable to estimate the severity of the epidemic. Finally, we carry out simulations on three types of social networks to investigate the effects of network structure, source of information, relative vaccine cost, and individual social connections on the final vaccine coverage and epidemic size. The results and findings can provide a new insight for designing incentive-based vaccination policies and intervention strategies for flu-like seasonal diseases.http://dx.doi.org/10.1155/2019/3901218 |
| spellingShingle | Benyun Shi Guangliang Liu Hongjun Qiu Yu-Wang Chen Shaoliang Peng Voluntary Vaccination through Perceiving Epidemic Severity in Social Networks Complexity |
| title | Voluntary Vaccination through Perceiving Epidemic Severity in Social Networks |
| title_full | Voluntary Vaccination through Perceiving Epidemic Severity in Social Networks |
| title_fullStr | Voluntary Vaccination through Perceiving Epidemic Severity in Social Networks |
| title_full_unstemmed | Voluntary Vaccination through Perceiving Epidemic Severity in Social Networks |
| title_short | Voluntary Vaccination through Perceiving Epidemic Severity in Social Networks |
| title_sort | voluntary vaccination through perceiving epidemic severity in social networks |
| url | http://dx.doi.org/10.1155/2019/3901218 |
| work_keys_str_mv | AT benyunshi voluntaryvaccinationthroughperceivingepidemicseverityinsocialnetworks AT guangliangliu voluntaryvaccinationthroughperceivingepidemicseverityinsocialnetworks AT hongjunqiu voluntaryvaccinationthroughperceivingepidemicseverityinsocialnetworks AT yuwangchen voluntaryvaccinationthroughperceivingepidemicseverityinsocialnetworks AT shaoliangpeng voluntaryvaccinationthroughperceivingepidemicseverityinsocialnetworks |