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Epidemic management and control through risk-dependent individual contact interventions
Testing, contact tracing, and isolation (TTI) is an epidemic management and control approach that is difficult to implement at scale because it relies on manual tracing of contacts. Exposure notification apps have been developed to digitally scale up TTI by harnessing contact data obtained from mobi...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9223336/ https://www.ncbi.nlm.nih.gov/pubmed/35737648 http://dx.doi.org/10.1371/journal.pcbi.1010171 |
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author | Schneider, Tapio Dunbar, Oliver R. A. Wu, Jinlong Böttcher, Lucas Burov, Dmitry Garbuno-Inigo, Alfredo Wagner, Gregory L. Pei, Sen Daraio, Chiara Ferrari, Raffaele Shaman, Jeffrey |
author_facet | Schneider, Tapio Dunbar, Oliver R. A. Wu, Jinlong Böttcher, Lucas Burov, Dmitry Garbuno-Inigo, Alfredo Wagner, Gregory L. Pei, Sen Daraio, Chiara Ferrari, Raffaele Shaman, Jeffrey |
author_sort | Schneider, Tapio |
collection | PubMed |
description | Testing, contact tracing, and isolation (TTI) is an epidemic management and control approach that is difficult to implement at scale because it relies on manual tracing of contacts. Exposure notification apps have been developed to digitally scale up TTI by harnessing contact data obtained from mobile devices; however, exposure notification apps provide users only with limited binary information when they have been directly exposed to a known infection source. Here we demonstrate a scalable improvement to TTI and exposure notification apps that uses data assimilation (DA) on a contact network. Network DA exploits diverse sources of health data together with the proximity data from mobile devices that exposure notification apps rely upon. It provides users with continuously assessed individual risks of exposure and infection, which can form the basis for targeting individual contact interventions. Simulations of the early COVID-19 epidemic in New York City are used to establish proof-of-concept. In the simulations, network DA identifies up to a factor 2 more infections than contact tracing when both harness the same contact data and diagnostic test data. This remains true even when only a relatively small fraction of the population uses network DA. When a sufficiently large fraction of the population (≳ 75%) uses network DA and complies with individual contact interventions, targeting contact interventions with network DA reduces deaths by up to a factor 4 relative to TTI. Network DA can be implemented by expanding the computational backend of existing exposure notification apps, thus greatly enhancing their capabilities. Implemented at scale, it has the potential to precisely and effectively control future epidemics while minimizing economic disruption. |
format | Online Article Text |
id | pubmed-9223336 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-92233362022-06-24 Epidemic management and control through risk-dependent individual contact interventions Schneider, Tapio Dunbar, Oliver R. A. Wu, Jinlong Böttcher, Lucas Burov, Dmitry Garbuno-Inigo, Alfredo Wagner, Gregory L. Pei, Sen Daraio, Chiara Ferrari, Raffaele Shaman, Jeffrey PLoS Comput Biol Research Article Testing, contact tracing, and isolation (TTI) is an epidemic management and control approach that is difficult to implement at scale because it relies on manual tracing of contacts. Exposure notification apps have been developed to digitally scale up TTI by harnessing contact data obtained from mobile devices; however, exposure notification apps provide users only with limited binary information when they have been directly exposed to a known infection source. Here we demonstrate a scalable improvement to TTI and exposure notification apps that uses data assimilation (DA) on a contact network. Network DA exploits diverse sources of health data together with the proximity data from mobile devices that exposure notification apps rely upon. It provides users with continuously assessed individual risks of exposure and infection, which can form the basis for targeting individual contact interventions. Simulations of the early COVID-19 epidemic in New York City are used to establish proof-of-concept. In the simulations, network DA identifies up to a factor 2 more infections than contact tracing when both harness the same contact data and diagnostic test data. This remains true even when only a relatively small fraction of the population uses network DA. When a sufficiently large fraction of the population (≳ 75%) uses network DA and complies with individual contact interventions, targeting contact interventions with network DA reduces deaths by up to a factor 4 relative to TTI. Network DA can be implemented by expanding the computational backend of existing exposure notification apps, thus greatly enhancing their capabilities. Implemented at scale, it has the potential to precisely and effectively control future epidemics while minimizing economic disruption. Public Library of Science 2022-06-23 /pmc/articles/PMC9223336/ /pubmed/35737648 http://dx.doi.org/10.1371/journal.pcbi.1010171 Text en © 2022 Schneider et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Schneider, Tapio Dunbar, Oliver R. A. Wu, Jinlong Böttcher, Lucas Burov, Dmitry Garbuno-Inigo, Alfredo Wagner, Gregory L. Pei, Sen Daraio, Chiara Ferrari, Raffaele Shaman, Jeffrey Epidemic management and control through risk-dependent individual contact interventions |
title | Epidemic management and control through risk-dependent individual contact interventions |
title_full | Epidemic management and control through risk-dependent individual contact interventions |
title_fullStr | Epidemic management and control through risk-dependent individual contact interventions |
title_full_unstemmed | Epidemic management and control through risk-dependent individual contact interventions |
title_short | Epidemic management and control through risk-dependent individual contact interventions |
title_sort | epidemic management and control through risk-dependent individual contact interventions |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9223336/ https://www.ncbi.nlm.nih.gov/pubmed/35737648 http://dx.doi.org/10.1371/journal.pcbi.1010171 |
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