Characterizing key attributes of COVID-19 transmission dynamics in China's original outbreak: Model-based estimations
<p dir="ltr">A novel coronavirus strain, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), emerged in China. This study aims to characterize key attributes of SARS-CoV-2 epidemiology as the infection emerged in China. An age-stratified mathematical model was constructed t...
محفوظ في:
| المؤلف الرئيسي: | |
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| مؤلفون آخرون: | , , , , , |
| منشور في: |
2020
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| الموضوعات: | |
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إضافة وسم
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| _version_ | 1864513555100860416 |
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| author | Houssein H. Ayoub (9262512) |
| author2 | Hiam Chemaitelly (439114) Ghina R. Mumtaz (9262518) Shaheen Seedat (9262515) Susanne F. Awad (11607966) Monia Makhoul (9262521) Laith J. Abu-Raddad (9262524) |
| author2_role | author author author author author author |
| author_facet | Houssein H. Ayoub (9262512) Hiam Chemaitelly (439114) Ghina R. Mumtaz (9262518) Shaheen Seedat (9262515) Susanne F. Awad (11607966) Monia Makhoul (9262521) Laith J. Abu-Raddad (9262524) |
| author_role | author |
| dc.creator.none.fl_str_mv | Houssein H. Ayoub (9262512) Hiam Chemaitelly (439114) Ghina R. Mumtaz (9262518) Shaheen Seedat (9262515) Susanne F. Awad (11607966) Monia Makhoul (9262521) Laith J. Abu-Raddad (9262524) |
| dc.date.none.fl_str_mv | 2020-11-18T00:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1016/j.gloepi.2020.100042 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Characterizing_key_attributes_of_COVID-19_transmission_dynamics_in_China_s_original_outbreak_Model-based_estimations/24210735 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Biomedical and clinical sciences Cardiovascular medicine and haematology Health sciences Epidemiology Public health SARS-CoV-2 COVID-19 Coronavirus Epidemiology China Mathematical model |
| dc.title.none.fl_str_mv | Characterizing key attributes of COVID-19 transmission dynamics in China's original outbreak: Model-based estimations |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">A novel coronavirus strain, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), emerged in China. This study aims to characterize key attributes of SARS-CoV-2 epidemiology as the infection emerged in China. An age-stratified mathematical model was constructed to describe transmission dynamics and estimate age-specific differences in biological susceptibility to infection, age-assortativeness in transmission mixing, and transition in rate of infectious contacts (and reproduction number <i>R</i><sub>0</sub>) following introduction of mass interventions. The model estimated the infectious contact rate in early epidemic at 0.59 contacts/day (95% uncertainty interval-UI = 0.48–0.71). Relative to those 60–69 years, susceptibility was 0.06 in those ≤19 years, 0.34 in 20–29 years, 0.57 in 30–39 years, 0.69 in 40–49 years, 0.79 in 50–59 years, 0.94 in 70–79 years, and 0.88 in ≥80 years. Assortativeness in transmission mixing by age was limited at 0.004 (95% UI = 0.002–0.008). <i>R</i><sub>0</sub> rapidly declined from 2.1 (95% UI = 1.8–2.4) to 0.06 (95% UI = 0.05–0.07) following interventions' onset. Age appears to be a principal factor in explaining the transmission patterns in China. The biological susceptibility to infection seems limited among children but high among those >50 years. There was no evidence for differential contact mixing by age.</p><h2>Other Information</h2><p dir="ltr">Published in: Global Epidemiology<br>License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1016/j.gloepi.2020.100042" target="_blank">https://dx.doi.org/10.1016/j.gloepi.2020.100042</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_433351e6d7cf79545835155568a8c509 |
| identifier_str_mv | 10.1016/j.gloepi.2020.100042 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/24210735 |
| publishDate | 2020 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Characterizing key attributes of COVID-19 transmission dynamics in China's original outbreak: Model-based estimationsHoussein H. Ayoub (9262512)Hiam Chemaitelly (439114)Ghina R. Mumtaz (9262518)Shaheen Seedat (9262515)Susanne F. Awad (11607966)Monia Makhoul (9262521)Laith J. Abu-Raddad (9262524)Biomedical and clinical sciencesCardiovascular medicine and haematologyHealth sciencesEpidemiologyPublic healthSARS-CoV-2COVID-19CoronavirusEpidemiologyChinaMathematical model<p dir="ltr">A novel coronavirus strain, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), emerged in China. This study aims to characterize key attributes of SARS-CoV-2 epidemiology as the infection emerged in China. An age-stratified mathematical model was constructed to describe transmission dynamics and estimate age-specific differences in biological susceptibility to infection, age-assortativeness in transmission mixing, and transition in rate of infectious contacts (and reproduction number <i>R</i><sub>0</sub>) following introduction of mass interventions. The model estimated the infectious contact rate in early epidemic at 0.59 contacts/day (95% uncertainty interval-UI = 0.48–0.71). Relative to those 60–69 years, susceptibility was 0.06 in those ≤19 years, 0.34 in 20–29 years, 0.57 in 30–39 years, 0.69 in 40–49 years, 0.79 in 50–59 years, 0.94 in 70–79 years, and 0.88 in ≥80 years. Assortativeness in transmission mixing by age was limited at 0.004 (95% UI = 0.002–0.008). <i>R</i><sub>0</sub> rapidly declined from 2.1 (95% UI = 1.8–2.4) to 0.06 (95% UI = 0.05–0.07) following interventions' onset. Age appears to be a principal factor in explaining the transmission patterns in China. The biological susceptibility to infection seems limited among children but high among those >50 years. There was no evidence for differential contact mixing by age.</p><h2>Other Information</h2><p dir="ltr">Published in: Global Epidemiology<br>License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1016/j.gloepi.2020.100042" target="_blank">https://dx.doi.org/10.1016/j.gloepi.2020.100042</a></p>2020-11-18T00:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1016/j.gloepi.2020.100042https://figshare.com/articles/journal_contribution/Characterizing_key_attributes_of_COVID-19_transmission_dynamics_in_China_s_original_outbreak_Model-based_estimations/24210735CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/242107352020-11-18T00:00:00Z |
| spellingShingle | Characterizing key attributes of COVID-19 transmission dynamics in China's original outbreak: Model-based estimations Houssein H. Ayoub (9262512) Biomedical and clinical sciences Cardiovascular medicine and haematology Health sciences Epidemiology Public health SARS-CoV-2 COVID-19 Coronavirus Epidemiology China Mathematical model |
| status_str | publishedVersion |
| title | Characterizing key attributes of COVID-19 transmission dynamics in China's original outbreak: Model-based estimations |
| title_full | Characterizing key attributes of COVID-19 transmission dynamics in China's original outbreak: Model-based estimations |
| title_fullStr | Characterizing key attributes of COVID-19 transmission dynamics in China's original outbreak: Model-based estimations |
| title_full_unstemmed | Characterizing key attributes of COVID-19 transmission dynamics in China's original outbreak: Model-based estimations |
| title_short | Characterizing key attributes of COVID-19 transmission dynamics in China's original outbreak: Model-based estimations |
| title_sort | Characterizing key attributes of COVID-19 transmission dynamics in China's original outbreak: Model-based estimations |
| topic | Biomedical and clinical sciences Cardiovascular medicine and haematology Health sciences Epidemiology Public health SARS-CoV-2 COVID-19 Coronavirus Epidemiology China Mathematical model |