Beyond COVID-19 pandemic: Topology-aware optimization of vaccination strategy for minimizing virus spreading
The mitigation of an infectious disease spreading has recently gained considerable attention from the research community. It may be obtained by adopting sanitary measurements (e.g., vaccination, wearing masks), social rules (e.g., social distancing), together with an extensive vaccination campaign....
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| Format: | Article |
| Language: | English |
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Elsevier
2022-01-01
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| Series: | Computational and Structural Biotechnology Journal |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2001037022001969 |
| _version_ | 1828087977860923392 |
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| author | Francesco Petrizzelli Pietro Hiram Guzzi Tommaso Mazza |
| author_facet | Francesco Petrizzelli Pietro Hiram Guzzi Tommaso Mazza |
| author_sort | Francesco Petrizzelli |
| collection | DOAJ |
| description | The mitigation of an infectious disease spreading has recently gained considerable attention from the research community. It may be obtained by adopting sanitary measurements (e.g., vaccination, wearing masks), social rules (e.g., social distancing), together with an extensive vaccination campaign. Vaccination is currently the primary way for mitigating the Coronavirus Disease (COVID-19) outbreak without severe lockdown. Its effectiveness also depends on the number and timeliness of administrations and thus demands strict prioritization criteria. Almost all countries have prioritized similar classes of exposed workers: healthcare professionals and the elderly, obtaining to maximize the survival of patients and years of life saved. Nevertheless, the virus is currently spreading at high rates, and any prioritization criterion so far adopted did not account for the structural organization of the contact networks.We reckon that a network where nodes are people while the edges represent their social contacts may efficiently model the virus’s spreading. It is known that tailored interventions (e.g., vaccination) on central nodes may efficiently stop the propagation, thereby eliminating the “bridge edges.” We then introduce such a model and consider both synthetic and real datasets. We present the benefits of a topology-aware versus an age-based vaccination strategy to mitigate the spreading of the virus. The code is available athttps://github.com/mazzalab/playgrounds. |
| first_indexed | 2024-04-11T05:19:38Z |
| format | Article |
| id | doaj.art-88039d74d5ef4d759bff9e4b18eb7eba |
| institution | Directory Open Access Journal |
| issn | 2001-0370 |
| language | English |
| last_indexed | 2024-04-11T05:19:38Z |
| publishDate | 2022-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Computational and Structural Biotechnology Journal |
| spelling | doaj.art-88039d74d5ef4d759bff9e4b18eb7eba2022-12-24T04:52:39ZengElsevierComputational and Structural Biotechnology Journal2001-03702022-01-012026642671Beyond COVID-19 pandemic: Topology-aware optimization of vaccination strategy for minimizing virus spreadingFrancesco Petrizzelli0Pietro Hiram Guzzi1Tommaso Mazza2Laboratory of Bioinformatics, Fondazione IRCCS Casa Sollievo della Sofferenza, Viale Capuccini, 71013 S. Giovanni Rotondo, Fg, ItalyDepartment of Surgical and Medical Sciences, University of Catanzaro, Catanzaro, Campus S Venuta, 88100, Italy; Corresponding authors.Laboratory of Bioinformatics, Fondazione IRCCS Casa Sollievo della Sofferenza, Viale Capuccini, 71013 S. Giovanni Rotondo, Fg, Italy; Corresponding authors.The mitigation of an infectious disease spreading has recently gained considerable attention from the research community. It may be obtained by adopting sanitary measurements (e.g., vaccination, wearing masks), social rules (e.g., social distancing), together with an extensive vaccination campaign. Vaccination is currently the primary way for mitigating the Coronavirus Disease (COVID-19) outbreak without severe lockdown. Its effectiveness also depends on the number and timeliness of administrations and thus demands strict prioritization criteria. Almost all countries have prioritized similar classes of exposed workers: healthcare professionals and the elderly, obtaining to maximize the survival of patients and years of life saved. Nevertheless, the virus is currently spreading at high rates, and any prioritization criterion so far adopted did not account for the structural organization of the contact networks.We reckon that a network where nodes are people while the edges represent their social contacts may efficiently model the virus’s spreading. It is known that tailored interventions (e.g., vaccination) on central nodes may efficiently stop the propagation, thereby eliminating the “bridge edges.” We then introduce such a model and consider both synthetic and real datasets. We present the benefits of a topology-aware versus an age-based vaccination strategy to mitigate the spreading of the virus. The code is available athttps://github.com/mazzalab/playgrounds.http://www.sciencedirect.com/science/article/pii/S2001037022001969SimulationsNetwork sciencesDisease containment |
| spellingShingle | Francesco Petrizzelli Pietro Hiram Guzzi Tommaso Mazza Beyond COVID-19 pandemic: Topology-aware optimization of vaccination strategy for minimizing virus spreading Computational and Structural Biotechnology Journal Simulations Network sciences Disease containment |
| title | Beyond COVID-19 pandemic: Topology-aware optimization of vaccination strategy for minimizing virus spreading |
| title_full | Beyond COVID-19 pandemic: Topology-aware optimization of vaccination strategy for minimizing virus spreading |
| title_fullStr | Beyond COVID-19 pandemic: Topology-aware optimization of vaccination strategy for minimizing virus spreading |
| title_full_unstemmed | Beyond COVID-19 pandemic: Topology-aware optimization of vaccination strategy for minimizing virus spreading |
| title_short | Beyond COVID-19 pandemic: Topology-aware optimization of vaccination strategy for minimizing virus spreading |
| title_sort | beyond covid 19 pandemic topology aware optimization of vaccination strategy for minimizing virus spreading |
| topic | Simulations Network sciences Disease containment |
| url | http://www.sciencedirect.com/science/article/pii/S2001037022001969 |
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