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Epidemic management and control through risk-dependent individual contact interventions

Schneider, Tapio and Dunbar, Oliver R. A. and Wu, Jinlong and Böttcher, Lucas and Burov, Dmitry and Garbuno-Iñigo, Alfredo and Wagner, Gregory L. and Pei, Sen and Daraio, Chiara and Ferrari, Raffaele and Shaman, Jeffrey (2022) Epidemic management and control through risk-dependent individual contact interventions. PLoS Computational Biology, 18 (6). Art. No. e1010171. ISSN 1553-734X. PMCID PMC9223336. doi:10.1371/journal.pcbi.1010171.

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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.

Item Type:Article
Related URLs:
URLURL TypeDescription CentralArticle Paper ItemCode
Schneider, Tapio0000-0001-5687-2287
Dunbar, Oliver R. A.0000-0001-7374-0382
Wu, Jinlong0000-0001-7438-4228
Böttcher, Lucas0000-0003-1700-1897
Burov, Dmitry0000-0002-5060-6794
Garbuno-Iñigo, Alfredo0000-0003-3279-619X
Wagner, Gregory L.0000-0001-5317-2445
Pei, Sen0000-0002-7072-2995
Daraio, Chiara0000-0001-5296-4440
Ferrari, Raffaele0000-0003-1895-4294
Shaman, Jeffrey0000-0002-7216-7809
Additional Information:© 2022 Schneider et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Received: September 25, 2021; Accepted: May 5, 2022; Published: June 23, 2022. We thank Tobias Bischoff, Mason Porter, and Andrew Stuart for helpful discussions. This research was supported by Eric and Wendy Schmidt and Schmidt Futures (T.S., O.R.A. D., J.W., D.B.); Swiss National Science Foundation (P2EZP2_191888), National Institutes of Health (R01HL146552), and Army Research Office (W911NF-18-1-0345) (L.B.); National Science Foundation (DMS-2027369), National Institute of Allergy and Infectious Diseases (R01AI163023) (S. P., J.S.), and the Morris-Singer Foundation (J.S.). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Data Availability Statement: All code written in support of this publication is publicly available at Author Contributions: Conceptualization: Tapio Schneider, Chiara Daraio. Formal analysis: Tapio Schneider, Oliver R. A. Dunbar, Jinlong Wu, Lucas Böttcher, Sen Pei, Jeffrey Shaman. Funding acquisition: Tapio Schneider, Lucas Böttcher, Jeffrey Shaman. Investigation: Oliver R. A. Dunbar, Jinlong Wu, Dmitry Burov, Alfredo Garbuno-Inigo. Methodology: Tapio Schneider, Oliver R. A. Dunbar, Jinlong Wu, Lucas Böttcher, Alfredo Garbuno-Inigo, Sen Pei, Jeffrey Shaman. Project administration: Tapio Schneider. Software: Oliver R. A. Dunbar, Jinlong Wu, Lucas Böttcher, Dmitry Burov, Gregory L. Wagner. Supervision: Tapio Schneider. Visualization: Oliver R. A. Dunbar, Jinlong Wu, Lucas Böttcher, Alfredo Garbuno-Inigo. Writing – original draft: Tapio Schneider, Oliver R. A. Dunbar, Lucas Böttcher, Jeffrey Shaman. Writing – review & editing: Tapio Schneider, Oliver R. A. Dunbar, Jinlong Wu, Lucas Böttcher, Alfredo Garbuno-Inigo, Chiara Daraio, Raffaele Ferrari, Jeffrey Shaman. Competing interests: I have read the journal’s policy and the authors of this manuscript have the following competing interests: J.S. and Columbia University disclose partial ownership of SK Analytics. J.S. discloses consulting for BNI. The California Institute of Technology has filed a patent application covering the epidemic management and control methods described in this paper. All other authors declare no competing interests.
Funding AgencyGrant Number
Schmidt Futures ProgramUNSPECIFIED
Swiss National Science Foundation (SNSF)P2EZP2_191888
Army Research Office (ARO)W911NF-18-1-0345
Andrew and Corey Morris-Singer FoundationUNSPECIFIED
Issue or Number:6
PubMed Central ID:PMC9223336
Record Number:CaltechAUTHORS:20220325-220336231
Persistent URL:
Official Citation:Schneider T, Dunbar ORA, Wu J, Böttcher L, Burov D, Garbuno-Inigo A, et al. (2022). Epidemic management and control through risk-dependent individual contact interventions. PLoS Comput Biol 18(6): e1010171.
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:114086
Deposited By: George Porter
Deposited On:25 Mar 2022 23:17
Last Modified:12 Jul 2022 21:46

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