Estimating SARS-CoV-2 infection incidence and detection rates: Demonstrating a novel surveillance method
<h3>Objectives</h3><p dir="ltr">Assessing the cumulative incidence of infection conventionally relies on documented infections or serological surveys, both of which have limitations. This study introduces a novel and practical method leveraging testing variation in a popu...
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2025
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| _version_ | 1864513533260070912 |
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| author | Houssein H. Ayoub (9262512) |
| author2 | Hiam Chemaitelly (439114) Patrick Tang (239534) Mohammad R. Hasan (13777597) Hadi M. Yassine (4675846) Asmaa A. Al Thani (10494576) Peter Coyle (787159) Zaina Al-Kanaani (4557205) Einas Al-Kuwari (13777606) Anvar Hassan Kaleeckal (11847034) Ali Nizar Latif (11570540) Hanan F. Abdul-Rahim (13777600) Gheyath K. Nasrallah (9200525) Mohamed Ghaith Al-Kuwari (4264192) Adeel A. Butt (3697705) Hamad Eid Al-Romaihi (6837251) Mohamed H. Al-Thani (11847049) Abdullatif Al-Khal (11721410) Roberto Bertollini (9538620) Laith J. Abu-Raddad (9262524) |
| author2_role | author author author author author author author author author author author author author author author author author author author |
| author_facet | Houssein H. Ayoub (9262512) Hiam Chemaitelly (439114) Patrick Tang (239534) Mohammad R. Hasan (13777597) Hadi M. Yassine (4675846) Asmaa A. Al Thani (10494576) Peter Coyle (787159) Zaina Al-Kanaani (4557205) Einas Al-Kuwari (13777606) Anvar Hassan Kaleeckal (11847034) Ali Nizar Latif (11570540) Hanan F. Abdul-Rahim (13777600) Gheyath K. Nasrallah (9200525) Mohamed Ghaith Al-Kuwari (4264192) Adeel A. Butt (3697705) Hamad Eid Al-Romaihi (6837251) Mohamed H. Al-Thani (11847049) Abdullatif Al-Khal (11721410) Roberto Bertollini (9538620) Laith J. Abu-Raddad (9262524) |
| author_role | author |
| dc.creator.none.fl_str_mv | Houssein H. Ayoub (9262512) Hiam Chemaitelly (439114) Patrick Tang (239534) Mohammad R. Hasan (13777597) Hadi M. Yassine (4675846) Asmaa A. Al Thani (10494576) Peter Coyle (787159) Zaina Al-Kanaani (4557205) Einas Al-Kuwari (13777606) Anvar Hassan Kaleeckal (11847034) Ali Nizar Latif (11570540) Hanan F. Abdul-Rahim (13777600) Gheyath K. Nasrallah (9200525) Mohamed Ghaith Al-Kuwari (4264192) Adeel A. Butt (3697705) Hamad Eid Al-Romaihi (6837251) Mohamed H. Al-Thani (11847049) Abdullatif Al-Khal (11721410) Roberto Bertollini (9538620) Laith J. Abu-Raddad (9262524) |
| dc.date.none.fl_str_mv | 2025-10-29T15:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1016/j.puhe.2025.106016 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Estimating_SARS-CoV-2_infection_incidence_and_detection_rates_Demonstrating_a_novel_surveillance_method/30539987 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Health sciences Epidemiology Public health Mathematical sciences Applied mathematics Statistics Incidence Detection rate Surveillance Mathematical model SARS-CoV-2 |
| dc.title.none.fl_str_mv | Estimating SARS-CoV-2 infection incidence and detection rates: Demonstrating a novel surveillance method |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <h3>Objectives</h3><p dir="ltr">Assessing the cumulative incidence of infection conventionally relies on documented infections or serological surveys, both of which have limitations. This study introduces a novel and practical method leveraging testing variation in a population to estimate SARS-CoV-2 infection rates in the population of Qatar. </p><h3>Study design</h3><p dir="ltr">Cohort study and mathematical modeling. </p><h3>Methods</h3><p dir="ltr">A cohort study was conducted from February 28, 2020, to March 04, 2024, to derive testing rates and estimate cumulative incidence of documented infection and hazard rates of documented infection in different testing groups. A deterministic mathematical model, applied to the cohort study data, was employed to simulate infection transmission, testing and infection documentation, and estimate the cumulative incidence of documented and undocumented infections, along with the infection detection rate. </p><h3>Results</h3><p dir="ltr">At the conclusion of the pre-Omicron phase, the model-estimated cumulative incidence of documented infection, undocumented infection, and all infections was 9.8 %, 29.7 %, and 39.5 %, respectively. By the end of the first-Omicron wave, cumulatively from the onset of the pandemic, these figures rose to 16.9 %, 56.3 %, and 73.2 %, and in the post-first Omicron phase, to 18.8 %, 77.9 %, and 96.7 %, respectively. The infection detection rate in the population was 24.9 %, 21.0 %, and 9.1 % in each of the pre-Omicron phase, first-Omicron wave, and post-first Omicron phase, respectively. Uncertainty and sensitivity analyses confirmed these results. </p><h3>Conclusions</h3><p dir="ltr">Leveraging readily available testing data, the introduced method was validated in a real-world setting and has the potential for diverse applications to enhance infectious disease surveillance for both emerging and endemic infections.</p><h2>Other Information</h2><p dir="ltr">Published in: Public Health<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.puhe.2025.106016" target="_blank">https://dx.doi.org/10.1016/j.puhe.2025.106016</a></p><p dir="ltr">Other institutions affiliated with: College of Health and Life Sciences - Hamad Bin Khalifa University</p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_33a3646d003a382ab4a01ffedafff746 |
| identifier_str_mv | 10.1016/j.puhe.2025.106016 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/30539987 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Estimating SARS-CoV-2 infection incidence and detection rates: Demonstrating a novel surveillance methodHoussein H. Ayoub (9262512)Hiam Chemaitelly (439114)Patrick Tang (239534)Mohammad R. Hasan (13777597)Hadi M. Yassine (4675846)Asmaa A. Al Thani (10494576)Peter Coyle (787159)Zaina Al-Kanaani (4557205)Einas Al-Kuwari (13777606)Anvar Hassan Kaleeckal (11847034)Ali Nizar Latif (11570540)Hanan F. Abdul-Rahim (13777600)Gheyath K. Nasrallah (9200525)Mohamed Ghaith Al-Kuwari (4264192)Adeel A. Butt (3697705)Hamad Eid Al-Romaihi (6837251)Mohamed H. Al-Thani (11847049)Abdullatif Al-Khal (11721410)Roberto Bertollini (9538620)Laith J. Abu-Raddad (9262524)Health sciencesEpidemiologyPublic healthMathematical sciencesApplied mathematicsStatisticsIncidenceDetection rateSurveillanceMathematical modelSARS-CoV-2<h3>Objectives</h3><p dir="ltr">Assessing the cumulative incidence of infection conventionally relies on documented infections or serological surveys, both of which have limitations. This study introduces a novel and practical method leveraging testing variation in a population to estimate SARS-CoV-2 infection rates in the population of Qatar. </p><h3>Study design</h3><p dir="ltr">Cohort study and mathematical modeling. </p><h3>Methods</h3><p dir="ltr">A cohort study was conducted from February 28, 2020, to March 04, 2024, to derive testing rates and estimate cumulative incidence of documented infection and hazard rates of documented infection in different testing groups. A deterministic mathematical model, applied to the cohort study data, was employed to simulate infection transmission, testing and infection documentation, and estimate the cumulative incidence of documented and undocumented infections, along with the infection detection rate. </p><h3>Results</h3><p dir="ltr">At the conclusion of the pre-Omicron phase, the model-estimated cumulative incidence of documented infection, undocumented infection, and all infections was 9.8 %, 29.7 %, and 39.5 %, respectively. By the end of the first-Omicron wave, cumulatively from the onset of the pandemic, these figures rose to 16.9 %, 56.3 %, and 73.2 %, and in the post-first Omicron phase, to 18.8 %, 77.9 %, and 96.7 %, respectively. The infection detection rate in the population was 24.9 %, 21.0 %, and 9.1 % in each of the pre-Omicron phase, first-Omicron wave, and post-first Omicron phase, respectively. Uncertainty and sensitivity analyses confirmed these results. </p><h3>Conclusions</h3><p dir="ltr">Leveraging readily available testing data, the introduced method was validated in a real-world setting and has the potential for diverse applications to enhance infectious disease surveillance for both emerging and endemic infections.</p><h2>Other Information</h2><p dir="ltr">Published in: Public Health<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.puhe.2025.106016" target="_blank">https://dx.doi.org/10.1016/j.puhe.2025.106016</a></p><p dir="ltr">Other institutions affiliated with: College of Health and Life Sciences - Hamad Bin Khalifa University</p>2025-10-29T15:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1016/j.puhe.2025.106016https://figshare.com/articles/journal_contribution/Estimating_SARS-CoV-2_infection_incidence_and_detection_rates_Demonstrating_a_novel_surveillance_method/30539987CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/305399872025-10-29T15:00:00Z |
| spellingShingle | Estimating SARS-CoV-2 infection incidence and detection rates: Demonstrating a novel surveillance method Houssein H. Ayoub (9262512) Health sciences Epidemiology Public health Mathematical sciences Applied mathematics Statistics Incidence Detection rate Surveillance Mathematical model SARS-CoV-2 |
| status_str | publishedVersion |
| title | Estimating SARS-CoV-2 infection incidence and detection rates: Demonstrating a novel surveillance method |
| title_full | Estimating SARS-CoV-2 infection incidence and detection rates: Demonstrating a novel surveillance method |
| title_fullStr | Estimating SARS-CoV-2 infection incidence and detection rates: Demonstrating a novel surveillance method |
| title_full_unstemmed | Estimating SARS-CoV-2 infection incidence and detection rates: Demonstrating a novel surveillance method |
| title_short | Estimating SARS-CoV-2 infection incidence and detection rates: Demonstrating a novel surveillance method |
| title_sort | Estimating SARS-CoV-2 infection incidence and detection rates: Demonstrating a novel surveillance method |
| topic | Health sciences Epidemiology Public health Mathematical sciences Applied mathematics Statistics Incidence Detection rate Surveillance Mathematical model SARS-CoV-2 |