Modeling the population-level impact of treatment on COVID-19 disease and SARS-CoV-2 transmission
<p>Different COVID-19 treatment candidates are under development, and some are becoming available including two promising drugs from Merck and Pfizer. This study provides conceptual frameworks for the effects of three types of treatments, both therapeutic and prophylactic, and to investigate t...
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| مؤلفون آخرون: | , , , , |
| منشور في: |
2022
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إضافة وسم
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| _version_ | 1864513548311330816 |
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| author | Monia Makhoul (9262521) |
| author2 | Farah Abu-Hijleh (21348506) Houssein H. Ayoub (9262512) Shaheen Seedat (9262515) Hiam Chemaitelly (439114) Laith J. Abu-Raddad (9262524) |
| author2_role | author author author author author |
| author_facet | Monia Makhoul (9262521) Farah Abu-Hijleh (21348506) Houssein H. Ayoub (9262512) Shaheen Seedat (9262515) Hiam Chemaitelly (439114) Laith J. Abu-Raddad (9262524) |
| author_role | author |
| dc.creator.none.fl_str_mv | Monia Makhoul (9262521) Farah Abu-Hijleh (21348506) Houssein H. Ayoub (9262512) Shaheen Seedat (9262515) Hiam Chemaitelly (439114) Laith J. Abu-Raddad (9262524) |
| dc.date.none.fl_str_mv | 2022-04-22T12:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1016/j.epidem.2022.100567 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Modeling_the_population-level_impact_of_treatment_on_COVID-19_disease_and_SARS-CoV-2_transmission/29046242 |
| 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 Pharmacology and pharmaceutical sciences Health sciences Epidemiology Public health Mathematical sciences Applied mathematics SARS-CoV-2 COVID-19 Treatment Transmission Mathematical model Population-level |
| dc.title.none.fl_str_mv | Modeling the population-level impact of treatment on COVID-19 disease and SARS-CoV-2 transmission |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p>Different COVID-19 treatment candidates are under development, and some are becoming available including two promising drugs from Merck and Pfizer. This study provides conceptual frameworks for the effects of three types of treatments, both therapeutic and prophylactic, and to investigate their population-level impact, to inform drug development, licensure, decision-making, and implementation. Different drug efficacies were assessed using an age-structured mathematical model describing SARS-CoV-2 transmission and disease progression, with application to the United States as an illustrative example. Severe and critical infection treatment reduces progression to COVID-19 severe and critical disease and death with small number of treatments needed to avert one disease or death. Post-exposure prophylaxis treatment had a large impact on flattening the epidemic curve, with large reductions in infection, disease, and death, but the impact was strongly age dependent. Pre-exposure prophylaxis treatment had the best impact and effectiveness, with immense reductions in infection, disease, and death, driven by the robust control of infection transmission. Effectiveness of both pre-exposure and post-exposure prophylaxis treatments was disproportionally larger when a larger segment of the population was targeted than a specific age group. Additional downstream potential effects of treatment, beyond the primary outcome, enhance the population-level impact of both treatments. COVID-19 treatments are an important modality in controlling SARS-CoV-2 disease burden. Different types of treatment act synergistically for a larger impact, for these treatments and vaccination.</p><h2>Other Information</h2> <p> Published in: Epidemics<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.epidem.2022.100567" target="_blank">https://dx.doi.org/10.1016/j.epidem.2022.100567</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_be4b45d3621eb4021cced7deb962c4ad |
| identifier_str_mv | 10.1016/j.epidem.2022.100567 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/29046242 |
| publishDate | 2022 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Modeling the population-level impact of treatment on COVID-19 disease and SARS-CoV-2 transmissionMonia Makhoul (9262521)Farah Abu-Hijleh (21348506)Houssein H. Ayoub (9262512)Shaheen Seedat (9262515)Hiam Chemaitelly (439114)Laith J. Abu-Raddad (9262524)Biomedical and clinical sciencesPharmacology and pharmaceutical sciencesHealth sciencesEpidemiologyPublic healthMathematical sciencesApplied mathematicsSARS-CoV-2COVID-19TreatmentTransmissionMathematical modelPopulation-level<p>Different COVID-19 treatment candidates are under development, and some are becoming available including two promising drugs from Merck and Pfizer. This study provides conceptual frameworks for the effects of three types of treatments, both therapeutic and prophylactic, and to investigate their population-level impact, to inform drug development, licensure, decision-making, and implementation. Different drug efficacies were assessed using an age-structured mathematical model describing SARS-CoV-2 transmission and disease progression, with application to the United States as an illustrative example. Severe and critical infection treatment reduces progression to COVID-19 severe and critical disease and death with small number of treatments needed to avert one disease or death. Post-exposure prophylaxis treatment had a large impact on flattening the epidemic curve, with large reductions in infection, disease, and death, but the impact was strongly age dependent. Pre-exposure prophylaxis treatment had the best impact and effectiveness, with immense reductions in infection, disease, and death, driven by the robust control of infection transmission. Effectiveness of both pre-exposure and post-exposure prophylaxis treatments was disproportionally larger when a larger segment of the population was targeted than a specific age group. Additional downstream potential effects of treatment, beyond the primary outcome, enhance the population-level impact of both treatments. COVID-19 treatments are an important modality in controlling SARS-CoV-2 disease burden. Different types of treatment act synergistically for a larger impact, for these treatments and vaccination.</p><h2>Other Information</h2> <p> Published in: Epidemics<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.epidem.2022.100567" target="_blank">https://dx.doi.org/10.1016/j.epidem.2022.100567</a></p>2022-04-22T12:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1016/j.epidem.2022.100567https://figshare.com/articles/journal_contribution/Modeling_the_population-level_impact_of_treatment_on_COVID-19_disease_and_SARS-CoV-2_transmission/29046242CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/290462422022-04-22T12:00:00Z |
| spellingShingle | Modeling the population-level impact of treatment on COVID-19 disease and SARS-CoV-2 transmission Monia Makhoul (9262521) Biomedical and clinical sciences Pharmacology and pharmaceutical sciences Health sciences Epidemiology Public health Mathematical sciences Applied mathematics SARS-CoV-2 COVID-19 Treatment Transmission Mathematical model Population-level |
| status_str | publishedVersion |
| title | Modeling the population-level impact of treatment on COVID-19 disease and SARS-CoV-2 transmission |
| title_full | Modeling the population-level impact of treatment on COVID-19 disease and SARS-CoV-2 transmission |
| title_fullStr | Modeling the population-level impact of treatment on COVID-19 disease and SARS-CoV-2 transmission |
| title_full_unstemmed | Modeling the population-level impact of treatment on COVID-19 disease and SARS-CoV-2 transmission |
| title_short | Modeling the population-level impact of treatment on COVID-19 disease and SARS-CoV-2 transmission |
| title_sort | Modeling the population-level impact of treatment on COVID-19 disease and SARS-CoV-2 transmission |
| topic | Biomedical and clinical sciences Pharmacology and pharmaceutical sciences Health sciences Epidemiology Public health Mathematical sciences Applied mathematics SARS-CoV-2 COVID-19 Treatment Transmission Mathematical model Population-level |