Enhanced event-based surveillance: Epidemic Intelligence from Open Sources (EIOS) during FIFA World Cup 2022 Qatar
BackgroundPublic health threats can significantly impact mass gatherings and enhancing surveillance systems would thus be crucial. Epidemic Intelligence from Open Sources (EIOS) was introduced to Qatar to complement the existing surveillance measures in preparation to the FIFA World Cup Qatar 2022 (...
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2024
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| Online Access: | http://dx.doi.org/10.1016/j.jiph.2024.102514 https://www.sciencedirect.com/science/article/pii/S187603412400248X http://hdl.handle.net/10576/61511 |
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| _version_ | 1857415084978995200 |
|---|---|
| author | Mohamed, Sallam |
| author2 | Jabbar, Raihana Mahadoon, Lylu K. Elshareif, Tasneem J. Darweesh, Mariam Ahmed, Hanaa S. Mohamed, Douaa O.A. Corpuz, Aura Sadek, Mahmoud Habibi, Muzhgan Abougazia, Farida Shami, Rula Mahmoud, Montaha Heikal, Sara Aqel, Sarah Himatt, Sayed Al-Shamali, Maha Al-Romaihi, Hamad |
| author2_role | author author author author author author author author author author author author author author author author author |
| author_facet | Mohamed, Sallam Jabbar, Raihana Mahadoon, Lylu K. Elshareif, Tasneem J. Darweesh, Mariam Ahmed, Hanaa S. Mohamed, Douaa O.A. Corpuz, Aura Sadek, Mahmoud Habibi, Muzhgan Abougazia, Farida Shami, Rula Mahmoud, Montaha Heikal, Sara Aqel, Sarah Himatt, Sayed Al-Shamali, Maha Al-Romaihi, Hamad |
| author_role | author |
| dc.creator.none.fl_str_mv | Mohamed, Sallam Jabbar, Raihana Mahadoon, Lylu K. Elshareif, Tasneem J. Darweesh, Mariam Ahmed, Hanaa S. Mohamed, Douaa O.A. Corpuz, Aura Sadek, Mahmoud Habibi, Muzhgan Abougazia, Farida Shami, Rula Mahmoud, Montaha Heikal, Sara Aqel, Sarah Himatt, Sayed Al-Shamali, Maha Al-Romaihi, Hamad |
| dc.date.none.fl_str_mv | 2024-11-26T10:45:23Z 2024-09 |
| dc.format.none.fl_str_mv | application/pdf |
| dc.identifier.none.fl_str_mv | http://dx.doi.org/10.1016/j.jiph.2024.102514 Sallam, M., Jabbar, R., Mahadoon, L. K., Elshareif, T. J., Darweesh, M., Ahmed, H. S., ... & Al-Romaihi, H. (2024). Enhanced event-based surveillance: Epidemic Intelligence from Open Sources (EIOS) during FIFA World Cup 2022 Qatar. Journal of Infection and Public Health, 17(9), 102514. 1876-0341 https://www.sciencedirect.com/science/article/pii/S187603412400248X http://hdl.handle.net/10576/61511 9 17 1876-035X |
| dc.language.none.fl_str_mv | en |
| dc.publisher.none.fl_str_mv | Elsevier |
| dc.rights.none.fl_str_mv | http://creativecommons.org/licenses/by-nc-nd/4.0/ info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | FIFA World Cup 2022 Qatar Epidemic Intelligence from Open Sources Digital disease surveillance Risk prediction |
| dc.title.none.fl_str_mv | Enhanced event-based surveillance: Epidemic Intelligence from Open Sources (EIOS) during FIFA World Cup 2022 Qatar |
| dc.type.none.fl_str_mv | Article info:eu-repo/semantics/publishedVersion info:eu-repo/semantics/article |
| description | BackgroundPublic health threats can significantly impact mass gatherings and enhancing surveillance systems would thus be crucial. Epidemic Intelligence from Open Sources (EIOS) was introduced to Qatar to complement the existing surveillance measures in preparation to the FIFA World Cup Qatar 2022 (FWC22). This study estimated the empirical probability of EIOS detecting signals of public health relevance. It also looked at the factors responsible for discerning a moderate-high risk signal during a mass gathering event. MethodsThis cross-sectional descriptive study used data collected between November 8th and December 25th, 2022, through an EIOS dashboard that filtered open-source articles using specific keywords. Triage criteria and scoring scheme were developed to capture signals and these were maintained in MS Excel. EIOS’ contribution to epidemic intelligence was assessed by the empirical probability estimation of relevant public health signals. Chi-squared tests of independence were performed to check for associations between various hazard categories and other independent variables. A multivariate logistic regression evaluated the predictors of moderate-high risk signals that required prompt action. ResultsThe probability of EIOS capturing a signal relevant to public health was estimated at 0.85 % (95 % confidence interval (CI) [0.82 %−0.88 %]) with three signals requiring a national response. The hazard category of the signal had significant association to the region of occurrence (χ2 (5, N = 2543) = 1021.6, p < .001). The hazard category also showed significant association to its detection during matchdays of the tournament (χ2 (5, N = 2543) = 11.2, p < .05). The triage criteria developed was able to discern between low and moderate-high risk signals with an acceptable discrimination (Area Under the Curve=0.79). ConclusionEIOS proved useful in the early warning of public health threats. |
| eu_rights_str_mv | openAccess |
| format | article |
| id | qu_2b818df4746d0a59c70ef4d6b5643478 |
| identifier_str_mv | Sallam, M., Jabbar, R., Mahadoon, L. K., Elshareif, T. J., Darweesh, M., Ahmed, H. S., ... & Al-Romaihi, H. (2024). Enhanced event-based surveillance: Epidemic Intelligence from Open Sources (EIOS) during FIFA World Cup 2022 Qatar. Journal of Infection and Public Health, 17(9), 102514. 1876-0341 9 17 1876-035X |
| language_invalid_str_mv | en |
| network_acronym_str | qu |
| network_name_str | Qatar University repository |
| oai_identifier_str | oai:qspace.qu.edu.qa:10576/61511 |
| publishDate | 2024 |
| publisher.none.fl_str_mv | Elsevier |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | http://creativecommons.org/licenses/by-nc-nd/4.0/ |
| spelling | Enhanced event-based surveillance: Epidemic Intelligence from Open Sources (EIOS) during FIFA World Cup 2022 QatarMohamed, SallamJabbar, RaihanaMahadoon, Lylu K.Elshareif, Tasneem J.Darweesh, MariamAhmed, Hanaa S.Mohamed, Douaa O.A.Corpuz, AuraSadek, MahmoudHabibi, MuzhganAbougazia, FaridaShami, RulaMahmoud, MontahaHeikal, SaraAqel, SarahHimatt, SayedAl-Shamali, MahaAl-Romaihi, HamadFIFA World Cup 2022 QatarEpidemic Intelligence from Open SourcesDigital disease surveillanceRisk predictionBackgroundPublic health threats can significantly impact mass gatherings and enhancing surveillance systems would thus be crucial. Epidemic Intelligence from Open Sources (EIOS) was introduced to Qatar to complement the existing surveillance measures in preparation to the FIFA World Cup Qatar 2022 (FWC22). This study estimated the empirical probability of EIOS detecting signals of public health relevance. It also looked at the factors responsible for discerning a moderate-high risk signal during a mass gathering event. MethodsThis cross-sectional descriptive study used data collected between November 8th and December 25th, 2022, through an EIOS dashboard that filtered open-source articles using specific keywords. Triage criteria and scoring scheme were developed to capture signals and these were maintained in MS Excel. EIOS’ contribution to epidemic intelligence was assessed by the empirical probability estimation of relevant public health signals. Chi-squared tests of independence were performed to check for associations between various hazard categories and other independent variables. A multivariate logistic regression evaluated the predictors of moderate-high risk signals that required prompt action. ResultsThe probability of EIOS capturing a signal relevant to public health was estimated at 0.85 % (95 % confidence interval (CI) [0.82 %−0.88 %]) with three signals requiring a national response. The hazard category of the signal had significant association to the region of occurrence (χ2 (5, N = 2543) = 1021.6, p < .001). The hazard category also showed significant association to its detection during matchdays of the tournament (χ2 (5, N = 2543) = 11.2, p < .05). The triage criteria developed was able to discern between low and moderate-high risk signals with an acceptable discrimination (Area Under the Curve=0.79). ConclusionEIOS proved useful in the early warning of public health threats.Elsevier2024-11-26T10:45:23Z2024-09Articleinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://dx.doi.org/10.1016/j.jiph.2024.102514Sallam, M., Jabbar, R., Mahadoon, L. K., Elshareif, T. J., Darweesh, M., Ahmed, H. S., ... & Al-Romaihi, H. (2024). Enhanced event-based surveillance: Epidemic Intelligence from Open Sources (EIOS) during FIFA World Cup 2022 Qatar. Journal of Infection and Public Health, 17(9), 102514.1876-0341https://www.sciencedirect.com/science/article/pii/S187603412400248Xhttp://hdl.handle.net/10576/615119171876-035Xenhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessoai:qspace.qu.edu.qa:10576/615112025-01-15T08:46:06Z |
| spellingShingle | Enhanced event-based surveillance: Epidemic Intelligence from Open Sources (EIOS) during FIFA World Cup 2022 Qatar Mohamed, Sallam FIFA World Cup 2022 Qatar Epidemic Intelligence from Open Sources Digital disease surveillance Risk prediction |
| status_str | publishedVersion |
| title | Enhanced event-based surveillance: Epidemic Intelligence from Open Sources (EIOS) during FIFA World Cup 2022 Qatar |
| title_full | Enhanced event-based surveillance: Epidemic Intelligence from Open Sources (EIOS) during FIFA World Cup 2022 Qatar |
| title_fullStr | Enhanced event-based surveillance: Epidemic Intelligence from Open Sources (EIOS) during FIFA World Cup 2022 Qatar |
| title_full_unstemmed | Enhanced event-based surveillance: Epidemic Intelligence from Open Sources (EIOS) during FIFA World Cup 2022 Qatar |
| title_short | Enhanced event-based surveillance: Epidemic Intelligence from Open Sources (EIOS) during FIFA World Cup 2022 Qatar |
| title_sort | Enhanced event-based surveillance: Epidemic Intelligence from Open Sources (EIOS) during FIFA World Cup 2022 Qatar |
| topic | FIFA World Cup 2022 Qatar Epidemic Intelligence from Open Sources Digital disease surveillance Risk prediction |
| url | http://dx.doi.org/10.1016/j.jiph.2024.102514 https://www.sciencedirect.com/science/article/pii/S187603412400248X http://hdl.handle.net/10576/61511 |