dc.contributor.author | MANZO, Gianluca | |
dc.contributor.author | VAN DE RIJT, Arnout | |
dc.date.accessioned | 2020-05-21T12:19:04Z | |
dc.date.available | 2020-05-21T12:19:04Z | |
dc.date.issued | 2020 | |
dc.identifier.uri | https://hdl.handle.net/1814/67088 | |
dc.description | Published on 18 May 2020 | en |
dc.description.abstract | Two decades ago network scientists proposed that infectious diseases may be effectively halted by targeting interventions at a minority of highly connected individuals. Can this strategy be effective in combating a disease transmitted through physical proximity such as the contemporary SARS-CoV-2? The strategy's effectiveness critically depends on high between-person variability in close-range contacts. We analyze population survey data showing that indeed the frequency of close-range contacts across individuals is characterized by a small fraction of people reporting very high frequencies of close-range contacts. We simulate a population with empirical contact distributions coming out of lockdown. Simulations show that targeting a small fraction of high-degree individuals dramatically improves containment. Our results further suggest two concrete procedures for identifying high-contact individuals: acquaintance sampling and employment-based target. | en |
dc.language.iso | en | en |
dc.publisher | Cornell University | en |
dc.relation.ispartofseries | arXiv | en |
dc.relation.ispartofseries | 2020 | en |
dc.relation.ispartofseries | [SPS] | en |
dc.relation.hasversion | https://hdl.handle.net/1814/68876 | |
dc.relation.uri | https://arxiv.org/abs/2005.08907 | en |
dc.rights | info:eu-repo/semantics/openAccess | en |
dc.subject | Covid-19 | en |
dc.subject | COVID-19 | en |
dc.subject | Coronavirus | en |
dc.title | Halting SARS-CoV-2 by targeting high-contact individuals | en |
dc.type | Other | en |