Optimizing COVID-19 surveillance using historical electronic health records of influenza infection

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dc.contributorAalto-yliopistofi
dc.contributorAalto Universityen
dc.contributor.authorDu, Zhanweien_US
dc.contributor.authorBai, Yuanen_US
dc.contributor.authorWang, Linen_US
dc.contributor.authorHerrera-Diestra, Jose L.en_US
dc.contributor.authorYuan, Zhiluen_US
dc.contributor.authorGuo, Renzhongen_US
dc.contributor.authorCowling, Benjamin J.en_US
dc.contributor.authorMeyers, Lauren A.en_US
dc.contributor.authorHolme, Petteren_US
dc.contributor.departmentDepartment of Computer Scienceen
dc.contributor.groupauthorProfessorship Holme Petteren
dc.contributor.groupauthorComputer Science Professorsen
dc.contributor.groupauthorComputer Science - Complex Systems (Cxsys)en
dc.contributor.organizationUniversity of Texas at Austinen_US
dc.contributor.organizationUniversity of Hong Kongen_US
dc.contributor.organizationUniversity of Cambridgeen_US
dc.contributor.organizationShenzhen Universityen_US
dc.date.accessioned2022-08-17T09:39:21Z
dc.date.available2022-08-17T09:39:21Z
dc.date.issued2022-05en_US
dc.description.abstractTargeting surveillance resources toward individuals at high risk of early infection can accelerate the detection of emerging outbreaks. However, it is unclear which individuals are at high risk without detailed data on interpersonal and physical contacts. We propose a data-driven COVID-19 surveillance strategy using Electronic Health Record (EHR) data that identifies the most vulnerable individuals who acquired the earliest infections during historical influenza seasons. Our simulations for all three networks demonstrate that the EHR-based strategy performs as well as the most-connected strategy. Compared to the random acquaintance surveillance, our EHR-based strategy detects the early warning signal and peak timing much earlier. On average, the EHR-based strategy has 9.8 days of early warning and 13.5 days of peak timings, respectively, before the whole population. For the urban network, the expected values of our method are better than the random acquaintance strategy (24% for early warning and 14% in-advance for peak time). For a scale-free network, the average performance of the EHR-based method is 75% of the early warning and 109% in-advance when compared with the random acquaintance strategy. If the contact structure is persistent enough, it will be reflected by their history of infection. Our proposed approach suggests that seasonal influenza infection records could be used to monitor new outbreaks of emerging epidemics, including COVID-19. This is a method that exploits the effect of contact structure without considering it explicitly.en
dc.description.versionPeer revieweden
dc.format.extent8
dc.format.mimetypeapplication/pdfen_US
dc.identifier.citationDu, Z, Bai, Y, Wang, L, Herrera-Diestra, J L, Yuan, Z, Guo, R, Cowling, B J, Meyers, L A & Holme, P 2022, ' Optimizing COVID-19 surveillance using historical electronic health records of influenza infection ', PNAS Nexus, vol. 1, no. 2 . https://doi.org/10.1093/pnasnexus/pgac038en
dc.identifier.doi10.1093/pnasnexus/pgac038en_US
dc.identifier.issn2752-6542
dc.identifier.otherPURE UUID: f86af393-7db0-4c62-9150-2558d407f624en_US
dc.identifier.otherPURE ITEMURL: https://research.aalto.fi/en/publications/f86af393-7db0-4c62-9150-2558d407f624en_US
dc.identifier.otherPURE FILEURL: https://research.aalto.fi/files/86772441/SCI_Du_etal_PNAS_Nexus_2022.pdfen_US
dc.identifier.urihttps://aaltodoc.aalto.fi/handle/123456789/116108
dc.identifier.urnURN:NBN:fi:aalto-202208174925
dc.language.isoenen
dc.publisherOxford University Press
dc.relation.ispartofseriesPNAS Nexusen
dc.relation.ispartofseriesVolume 1, issue 2en
dc.rightsopenAccessen
dc.titleOptimizing COVID-19 surveillance using historical electronic health records of influenza infectionen
dc.typeA1 Alkuperäisartikkeli tieteellisessä aikakauslehdessäfi
dc.type.versionpublishedVersion
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