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- Publisher Website: 10.1038/s41467-021-21776-2
- Scopus: eid_2-s2.0-85102535980
- PMID: 33686075
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Article: Real-time tracking and prediction of COVID-19 infection using digital proxies of population mobility and mixing
Title | Real-time tracking and prediction of COVID-19 infection using digital proxies of population mobility and mixing |
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Authors | |
Issue Date | 2021 |
Publisher | Nature Research: Fully open access journals. The Journal's web site is located at http://www.nature.com/ncomms/index.html |
Citation | Nature Communications, 2021, v. 12 n. 1, p. article no. 1501 How to Cite? |
Abstract | Digital proxies of human mobility and physical mixing have been used to monitor viral transmissibility and effectiveness of social distancing interventions in the ongoing COVID-19 pandemic. We develop a new framework that parameterizes disease transmission models with age-specific digital mobility data. By fitting the model to case data in Hong Kong, we are able to accurately track the local effective reproduction number of COVID-19 in near real time (i.e., no longer constrained by the delay of around 9 days between infection and reporting of cases) which is essential for quick assessment of the effectiveness of interventions on reducing transmissibility. Our findings show that accurate nowcast and forecast of COVID-19 epidemics can be obtained by integrating valid digital proxies of physical mixing into conventional epidemic models. |
Persistent Identifier | http://hdl.handle.net/10722/297700 |
ISSN | 2023 Impact Factor: 14.7 2023 SCImago Journal Rankings: 4.887 |
PubMed Central ID | |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Leung, K | - |
dc.contributor.author | Wu, JT | - |
dc.contributor.author | Leung, GM | - |
dc.date.accessioned | 2021-03-23T04:20:26Z | - |
dc.date.available | 2021-03-23T04:20:26Z | - |
dc.date.issued | 2021 | - |
dc.identifier.citation | Nature Communications, 2021, v. 12 n. 1, p. article no. 1501 | - |
dc.identifier.issn | 2041-1723 | - |
dc.identifier.uri | http://hdl.handle.net/10722/297700 | - |
dc.description.abstract | Digital proxies of human mobility and physical mixing have been used to monitor viral transmissibility and effectiveness of social distancing interventions in the ongoing COVID-19 pandemic. We develop a new framework that parameterizes disease transmission models with age-specific digital mobility data. By fitting the model to case data in Hong Kong, we are able to accurately track the local effective reproduction number of COVID-19 in near real time (i.e., no longer constrained by the delay of around 9 days between infection and reporting of cases) which is essential for quick assessment of the effectiveness of interventions on reducing transmissibility. Our findings show that accurate nowcast and forecast of COVID-19 epidemics can be obtained by integrating valid digital proxies of physical mixing into conventional epidemic models. | - |
dc.language | eng | - |
dc.publisher | Nature Research: Fully open access journals. The Journal's web site is located at http://www.nature.com/ncomms/index.html | - |
dc.relation.ispartof | Nature Communications | - |
dc.rights | Nature Communications. Copyright © Nature Research: Fully open access journals. | - |
dc.rights | This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License. | - |
dc.title | Real-time tracking and prediction of COVID-19 infection using digital proxies of population mobility and mixing | - |
dc.type | Article | - |
dc.identifier.email | Leung, K: ksmleung@hku.hk | - |
dc.identifier.email | Wu, JT: joewu@hku.hk | - |
dc.identifier.email | Leung, GM: gmleung@hku.hk | - |
dc.identifier.authority | Leung, K=rp02563 | - |
dc.identifier.authority | Wu, JT=rp00517 | - |
dc.identifier.authority | Leung, GM=rp00460 | - |
dc.description.nature | published_or_final_version | - |
dc.identifier.doi | 10.1038/s41467-021-21776-2 | - |
dc.identifier.pmid | 33686075 | - |
dc.identifier.pmcid | PMC7940469 | - |
dc.identifier.scopus | eid_2-s2.0-85102535980 | - |
dc.identifier.hkuros | 321799 | - |
dc.identifier.volume | 12 | - |
dc.identifier.issue | 1 | - |
dc.identifier.spage | article no. 1501 | - |
dc.identifier.epage | article no. 1501 | - |
dc.identifier.isi | WOS:000627442200005 | - |
dc.publisher.place | United Kingdom | - |