Epidemic mitigation via awareness propagation in communication networks: the role of time scales
The participation of individuals in multi-layer networks allows for feedback between network layers, opening new possibilities to mitigate epidemic spreading. For instance, the spread of a biological disease such as Ebola in a physical contact network may trigger the propagation of the information r...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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IOP Publishing
2017-01-01
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| Series: | New Journal of Physics |
| Subjects: | |
| Online Access: | https://doi.org/10.1088/1367-2630/aa79b7 |
| _version_ | 1827873358808612864 |
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| author | Huijuan Wang Chuyi Chen Bo Qu Daqing Li Shlomo Havlin |
| author_facet | Huijuan Wang Chuyi Chen Bo Qu Daqing Li Shlomo Havlin |
| author_sort | Huijuan Wang |
| collection | DOAJ |
| description | The participation of individuals in multi-layer networks allows for feedback between network layers, opening new possibilities to mitigate epidemic spreading. For instance, the spread of a biological disease such as Ebola in a physical contact network may trigger the propagation of the information related to this disease in a communication network, e.g. an online social network. The information propagated in the communication network may increase the awareness of some individuals, resulting in them avoiding contact with their infected neighbors in the physical contact network, which might protect the population from the infection. In this work, we aim to understand how the time scale γ of the information propagation (speed that information is spread and forgotten) in the communication network relative to that of the epidemic spread (speed that an epidemic is spread and cured) in the physical contact network influences such mitigation using awareness information. We begin by proposing a model of the interaction between information propagation and epidemic spread, taking into account the relative time scale γ . We analytically derive the average fraction of infected nodes in the meta-stable state for this model (i) by developing an individual-based mean-field approximation (IBMFA) method and (ii) by extending the microscopic Markov chain approach (MMCA). We show that when the time scale γ of the information spread relative to the epidemic spread is large, our IBMFA approximation is better compared to MMCA near the epidemic threshold, whereas MMCA performs better when the prevalence of the epidemic is high. Furthermore, we find that an optimal mitigation exists that leads to a minimal fraction of infected nodes. The optimal mitigation is achieved at a non-trivial relative time scale γ , which depends on the rate at which an infected individual becomes aware. Contrary to our intuition, information spread too fast in the communication network could reduce the mitigation effect. Finally, our finding has been validated in the real-world two-layer network obtained from the location-based social network Brightkite. |
| first_indexed | 2024-03-12T16:35:53Z |
| format | Article |
| id | doaj.art-cce68e3b18f44ba1b6eef39a28ca0d49 |
| institution | Directory Open Access Journal |
| issn | 1367-2630 |
| language | English |
| last_indexed | 2024-03-12T16:35:53Z |
| publishDate | 2017-01-01 |
| publisher | IOP Publishing |
| record_format | Article |
| series | New Journal of Physics |
| spelling | doaj.art-cce68e3b18f44ba1b6eef39a28ca0d492023-08-08T14:55:07ZengIOP PublishingNew Journal of Physics1367-26302017-01-0119707303910.1088/1367-2630/aa79b7Epidemic mitigation via awareness propagation in communication networks: the role of time scalesHuijuan Wang0https://orcid.org/0000-0003-2684-4407Chuyi Chen1Bo Qu2Daqing Li3Shlomo Havlin4Faculty of Electrical Engineering, Mathematics, and Computer Science Delft University of Technology , Delft, The NetherlandsFaculty of Electrical Engineering, Mathematics, and Computer Science Delft University of Technology , Delft, The NetherlandsFaculty of Electrical Engineering, Mathematics, and Computer Science Delft University of Technology , Delft, The NetherlandsSchool of Reliability and Systems Engineering, Beihang University , Beijing 100191, People's Republic of China; Science and Technology on Reliability and Environmental Engineering Laboratory , Beijing 100191, People's Republic of ChinaDepartment of Physics, Bar-Ilan University , Ramat Gan 5290002, IsraelThe participation of individuals in multi-layer networks allows for feedback between network layers, opening new possibilities to mitigate epidemic spreading. For instance, the spread of a biological disease such as Ebola in a physical contact network may trigger the propagation of the information related to this disease in a communication network, e.g. an online social network. The information propagated in the communication network may increase the awareness of some individuals, resulting in them avoiding contact with their infected neighbors in the physical contact network, which might protect the population from the infection. In this work, we aim to understand how the time scale γ of the information propagation (speed that information is spread and forgotten) in the communication network relative to that of the epidemic spread (speed that an epidemic is spread and cured) in the physical contact network influences such mitigation using awareness information. We begin by proposing a model of the interaction between information propagation and epidemic spread, taking into account the relative time scale γ . We analytically derive the average fraction of infected nodes in the meta-stable state for this model (i) by developing an individual-based mean-field approximation (IBMFA) method and (ii) by extending the microscopic Markov chain approach (MMCA). We show that when the time scale γ of the information spread relative to the epidemic spread is large, our IBMFA approximation is better compared to MMCA near the epidemic threshold, whereas MMCA performs better when the prevalence of the epidemic is high. Furthermore, we find that an optimal mitigation exists that leads to a minimal fraction of infected nodes. The optimal mitigation is achieved at a non-trivial relative time scale γ , which depends on the rate at which an infected individual becomes aware. Contrary to our intuition, information spread too fast in the communication network could reduce the mitigation effect. Finally, our finding has been validated in the real-world two-layer network obtained from the location-based social network Brightkite.https://doi.org/10.1088/1367-2630/aa79b7epidemic spreadinginteracting processestime scalemulti-layer networksepidemic mitigation |
| spellingShingle | Huijuan Wang Chuyi Chen Bo Qu Daqing Li Shlomo Havlin Epidemic mitigation via awareness propagation in communication networks: the role of time scales New Journal of Physics epidemic spreading interacting processes time scale multi-layer networks epidemic mitigation |
| title | Epidemic mitigation via awareness propagation in communication networks: the role of time scales |
| title_full | Epidemic mitigation via awareness propagation in communication networks: the role of time scales |
| title_fullStr | Epidemic mitigation via awareness propagation in communication networks: the role of time scales |
| title_full_unstemmed | Epidemic mitigation via awareness propagation in communication networks: the role of time scales |
| title_short | Epidemic mitigation via awareness propagation in communication networks: the role of time scales |
| title_sort | epidemic mitigation via awareness propagation in communication networks the role of time scales |
| topic | epidemic spreading interacting processes time scale multi-layer networks epidemic mitigation |
| url | https://doi.org/10.1088/1367-2630/aa79b7 |
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