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Comparative analysis of the effectiveness of three immunization strategies in controlling disease outbreaks in realistic social networks.

Xu Z, Zu Z, Zheng T, Zhang W, Xu Q, Liu J - PLoS ONE (2014)

Bottom Line: Present stochastic strategies are mainly evaluated based on classical network models, such as scale-free networks and small-world networks, and thus are insufficient.The results show all the strategies have decreased the coverage of the epidemics compared to baseline scenario (no control measures).These results could have important significance for epidemic control research and practice.

View Article: PubMed Central - PubMed

Affiliation: Center for Biosecurity Strategy Management, Beijing Institute of Biotechnology, Beijing, P. R. China.

ABSTRACT
The high incidence of emerging infectious diseases has highlighted the importance of effective immunization strategies, especially the stochastic algorithms based on local available network information. Present stochastic strategies are mainly evaluated based on classical network models, such as scale-free networks and small-world networks, and thus are insufficient. Three frequently referred stochastic immunization strategies-acquaintance immunization, community-bridge immunization, and ring vaccination-were analyzed in this work. The optimal immunization ratios for acquaintance immunization and community-bridge immunization strategies were investigated, and the effectiveness of these three strategies in controlling the spreading of epidemics were analyzed based on realistic social contact networks. The results show all the strategies have decreased the coverage of the epidemics compared to baseline scenario (no control measures). However the effectiveness of acquaintance immunization and community-bridge immunization are very limited, with acquaintance immunization slightly outperforming community-bridge immunization. Ring vaccination significantly outperforms acquaintance immunization and community-bridge immunization, and the sensitivity analysis shows it could be applied to controlling the epidemics with a wide infectivity spectrum. The effectiveness of several classical stochastic immunization strategies was evaluated based on realistic contact networks for the first time in this study. These results could have important significance for epidemic control research and practice.

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Optimal immunization ratio., . (a)  varies with f for AI, the optimal immunization ratio . (b)  varies with f for CBI, the optimal immunization ratio . (c) A possible local network structure of contact network.
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pone-0095911-g003: Optimal immunization ratio., . (a) varies with f for AI, the optimal immunization ratio . (b) varies with f for CBI, the optimal immunization ratio . (c) A possible local network structure of contact network.

Mentions: The binary search method was adopted to explore the optimal immunization ratio in interval [0, 1]. The search process ends when search step . One notable thing is that the immunization ratio f for AI could be any value between 0 and 1: f = 0 indicates no interventions and f = 1 implies that all the individuals are immunized after the detection of the epidemics. However, for CBI, the immunized individuals are those targeted as community-bridge nodes by community-bridge find algorithm, which might be finite in the network. Take the sub network in Figure 3c as an example, according to the community-bridge find algorithm, node 1 and 5 are community-bridge nodes, i.e., potential immunization targets, while node 2, 3 and 4 are impossible to be targeted as immunization nodes. Therefore for any specific social contact network, there will be a ceiling immunization ratio for CBI. implies all the community-bridge nodes will be immunized. The realistic immunization ratio will always be less than .


Comparative analysis of the effectiveness of three immunization strategies in controlling disease outbreaks in realistic social networks.

Xu Z, Zu Z, Zheng T, Zhang W, Xu Q, Liu J - PLoS ONE (2014)

Optimal immunization ratio., . (a)  varies with f for AI, the optimal immunization ratio . (b)  varies with f for CBI, the optimal immunization ratio . (c) A possible local network structure of contact network.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4008523&req=5

pone-0095911-g003: Optimal immunization ratio., . (a) varies with f for AI, the optimal immunization ratio . (b) varies with f for CBI, the optimal immunization ratio . (c) A possible local network structure of contact network.
Mentions: The binary search method was adopted to explore the optimal immunization ratio in interval [0, 1]. The search process ends when search step . One notable thing is that the immunization ratio f for AI could be any value between 0 and 1: f = 0 indicates no interventions and f = 1 implies that all the individuals are immunized after the detection of the epidemics. However, for CBI, the immunized individuals are those targeted as community-bridge nodes by community-bridge find algorithm, which might be finite in the network. Take the sub network in Figure 3c as an example, according to the community-bridge find algorithm, node 1 and 5 are community-bridge nodes, i.e., potential immunization targets, while node 2, 3 and 4 are impossible to be targeted as immunization nodes. Therefore for any specific social contact network, there will be a ceiling immunization ratio for CBI. implies all the community-bridge nodes will be immunized. The realistic immunization ratio will always be less than .

Bottom Line: Present stochastic strategies are mainly evaluated based on classical network models, such as scale-free networks and small-world networks, and thus are insufficient.The results show all the strategies have decreased the coverage of the epidemics compared to baseline scenario (no control measures).These results could have important significance for epidemic control research and practice.

View Article: PubMed Central - PubMed

Affiliation: Center for Biosecurity Strategy Management, Beijing Institute of Biotechnology, Beijing, P. R. China.

ABSTRACT
The high incidence of emerging infectious diseases has highlighted the importance of effective immunization strategies, especially the stochastic algorithms based on local available network information. Present stochastic strategies are mainly evaluated based on classical network models, such as scale-free networks and small-world networks, and thus are insufficient. Three frequently referred stochastic immunization strategies-acquaintance immunization, community-bridge immunization, and ring vaccination-were analyzed in this work. The optimal immunization ratios for acquaintance immunization and community-bridge immunization strategies were investigated, and the effectiveness of these three strategies in controlling the spreading of epidemics were analyzed based on realistic social contact networks. The results show all the strategies have decreased the coverage of the epidemics compared to baseline scenario (no control measures). However the effectiveness of acquaintance immunization and community-bridge immunization are very limited, with acquaintance immunization slightly outperforming community-bridge immunization. Ring vaccination significantly outperforms acquaintance immunization and community-bridge immunization, and the sensitivity analysis shows it could be applied to controlling the epidemics with a wide infectivity spectrum. The effectiveness of several classical stochastic immunization strategies was evaluated based on realistic contact networks for the first time in this study. These results could have important significance for epidemic control research and practice.

Show MeSH
Related in: MedlinePlus