Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/96534
DC Field | Value | Language |
---|---|---|
dc.contributor | Department of Applied Mathematics | - |
dc.creator | Fei, Y | en_US |
dc.creator | Xu, H | en_US |
dc.creator | Zhang, X | en_US |
dc.creator | Musa, SS | en_US |
dc.creator | Zhao, S | en_US |
dc.creator | He, D | en_US |
dc.date.accessioned | 2022-12-07T02:55:19Z | - |
dc.date.available | 2022-12-07T02:55:19Z | - |
dc.identifier.issn | 2468-0427 | en_US |
dc.identifier.uri | http://hdl.handle.net/10397/96534 | - |
dc.language.iso | en | en_US |
dc.publisher | Elsevier | en_US |
dc.rights | © 2022 The Authors. Publishing services by Elsevier B.V. on behalf of KeAi Communications Co. Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). | en_US |
dc.rights | The following publication Fei, Y., Xu, H., Zhang, X., Musa, S. S., Zhao, S., & He, D. (2022). Seroprevalence and infection attack rate of COVID-19 in Indian cities. Infectious Disease Modelling, 7(2), 25-32 is available at https://doi.org/10.1016/j.idm.2022.03.001. | en_US |
dc.subject | Attack rate | en_US |
dc.subject | COVID-19 | en_US |
dc.subject | Mathematical modelling | en_US |
dc.subject | Pandemic | en_US |
dc.subject | Seroprevalence | en_US |
dc.title | Seroprevalence and infection attack rate of COVID-19 in Indian cities | en_US |
dc.type | Journal/Magazine Article | en_US |
dc.identifier.spage | 25 | en_US |
dc.identifier.epage | 32 | en_US |
dc.identifier.volume | 7 | en_US |
dc.identifier.issue | 2 | en_US |
dc.identifier.doi | 10.1016/j.idm.2022.03.001 | en_US |
dcterms.abstract | Objectives: Serological surveys were used to infer the infection attack rate in different populations. The sensitivity of the testing assay, Abbott, drops fast over time since infection which makes the serological data difficult to interpret. In this work, we aim to solve this issue. | - |
dcterms.abstract | Methods: We collect longitudinal serological data of Abbott to construct a sensitive decay function. We use the reported COVID-19 deaths to infer the infections, and use the decay function to simulate the seroprevalence and match to the reported seroprevalence in 12 Indian cities. | - |
dcterms.abstract | Results: Our model simulated seroprevalence matchs the reported seroprevalence in most of the 12 Indian cities. We obtain reasonable infection attack rate and infection fatality rate for most of the 12 Indian cities. | - |
dcterms.abstract | Conclusions: Using both reported COVID-19 deaths data and serological survey data, we infer the infection attack rate and infection fatality rate with increased confidence. | - |
dcterms.accessRights | open access | en_US |
dcterms.bibliographicCitation | Infectious disease modelling, June 2022, v. 7, no. 2, p. 25-32 | en_US |
dcterms.isPartOf | Infectious disease modelling | en_US |
dcterms.issued | 2022-06 | - |
dc.identifier.scopus | 2-s2.0-85126562850 | - |
dc.description.validate | 202212 bckw | - |
dc.description.oa | Version of Record | en_US |
dc.identifier.FolderNumber | OA_Scopus/WOS | - |
dc.description.pubStatus | Published | en_US |
Appears in Collections: | Journal/Magazine Article |
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1-s2.0-S2468042722000094-main.pdf | 477.31 kB | Adobe PDF | View/Open |
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