Effective node vaccination and containing strategies to halt SIR epidemic spreading in real-world face-to-face contact networks
2022 RIVF International Conference on Computing and Communication Technologies, RIVF 2022
; : 106-111, 2022.
Article
in English
| Scopus | ID: covidwho-2233766
ABSTRACT
We model the COVID-19 spreading by running SIR Monte-Carlo simulations in four real face-to-face contact networks. We evaluate the effectiveness of the 'facemask use' and 'vaccination policies' to curb epidemic spreading. We model the facemask use policy by assuming a lower individual infection probability β. We found that while this strategy can delay the disease spreading, it does not significantly reduce the total number of infected individuals (TI), as 80% of the total population still is infected at the end of the epidemic. We model vaccination by setting individual's infection probability β=0, which is equivalent to remove nodes/individuals from the network. The vaccination was found to be very effective. Even with a partial vaccination of 30% of the population nodes selected considering their centrality measure ranking, such as degree, betweenness, or PageRank, it was possible to reduce the TI of 14%. Finally, yet importantly, random partial vaccination is not effective at all, meaning that most of the unvaccinated population will be infected. © 2022 IEEE.
Full text:
Available
Collection:
Databases of international organizations
Database:
Scopus
Type of study:
Experimental Studies
/
Randomized controlled trials
Topics:
Vaccines
Language:
English
Journal:
2022 RIVF International Conference on Computing and Communication Technologies, RIVF 2022
Year:
2022
Document Type:
Article
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