Estimating infection attack rates and severity in real time during an influenza pandemic: analysis of serial cross-sectional serologic surveillance data.
<h4>Background</h4>In an emerging influenza pandemic, estimating severity (the probability of a severe outcome, such as hospitalization, if infected) is a public health priority. As many influenza infections are subclinical, sero-surveillance is needed to allow reliable real-time estimat...
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Public Library of Science (PLoS)
2011-10-01
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| Series: | PLoS Medicine |
| Online Access: | https://journals.plos.org/plosmedicine/article/file?id=10.1371/journal.pmed.1001103&type=printable |
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| author | Joseph T Wu Andrew Ho Edward S K Ma Cheuk Kwong Lee Daniel K W Chu Po-Lai Ho Ivan F N Hung Lai Ming Ho Che Kit Lin Thomas Tsang Su-Vui Lo Yu-Lung Lau Gabriel M Leung Benjamin J Cowling J S Malik Peiris |
| author_facet | Joseph T Wu Andrew Ho Edward S K Ma Cheuk Kwong Lee Daniel K W Chu Po-Lai Ho Ivan F N Hung Lai Ming Ho Che Kit Lin Thomas Tsang Su-Vui Lo Yu-Lung Lau Gabriel M Leung Benjamin J Cowling J S Malik Peiris |
| author_sort | Joseph T Wu |
| collection | DOAJ |
| description | <h4>Background</h4>In an emerging influenza pandemic, estimating severity (the probability of a severe outcome, such as hospitalization, if infected) is a public health priority. As many influenza infections are subclinical, sero-surveillance is needed to allow reliable real-time estimates of infection attack rate (IAR) and severity.<h4>Methods and findings</h4>We tested 14,766 sera collected during the first wave of the 2009 pandemic in Hong Kong using viral microneutralization. We estimated IAR and infection-hospitalization probability (IHP) from the serial cross-sectional serologic data and hospitalization data. Had our serologic data been available weekly in real time, we would have obtained reliable IHP estimates 1 wk after, 1-2 wk before, and 3 wk after epidemic peak for individuals aged 5-14 y, 15-29 y, and 30-59 y. The ratio of IAR to pre-existing seroprevalence, which decreased with age, was a major determinant for the timeliness of reliable estimates. If we began sero-surveillance 3 wk after community transmission was confirmed, with 150, 350, and 500 specimens per week for individuals aged 5-14 y, 15-19 y, and 20-29 y, respectively, we would have obtained reliable IHP estimates for these age groups 4 wk before the peak. For 30-59 y olds, even 800 specimens per week would not have generated reliable estimates until the peak because the ratio of IAR to pre-existing seroprevalence for this age group was low. The performance of serial cross-sectional sero-surveillance substantially deteriorates if test specificity is not near 100% or pre-existing seroprevalence is not near zero. These potential limitations could be mitigated by choosing a higher titer cutoff for seropositivity. If the epidemic doubling time is longer than 6 d, then serial cross-sectional sero-surveillance with 300 specimens per week would yield reliable estimates when IAR reaches around 6%-10%.<h4>Conclusions</h4>Serial cross-sectional serologic data together with clinical surveillance data can allow reliable real-time estimates of IAR and severity in an emerging pandemic. Sero-surveillance for pandemics should be considered. |
| format | Article |
| id | doaj-art-243f6596bfe0419a9b653e2cba487833 |
| institution | DOAJ |
| issn | 1549-1277 1549-1676 |
| language | English |
| publishDate | 2011-10-01 |
| publisher | Public Library of Science (PLoS) |
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| spelling | doaj-art-243f6596bfe0419a9b653e2cba4878332025-08-20T03:10:04ZengPublic Library of Science (PLoS)PLoS Medicine1549-12771549-16762011-10-01810e100110310.1371/journal.pmed.1001103Estimating infection attack rates and severity in real time during an influenza pandemic: analysis of serial cross-sectional serologic surveillance data.Joseph T WuAndrew HoEdward S K MaCheuk Kwong LeeDaniel K W ChuPo-Lai HoIvan F N HungLai Ming HoChe Kit LinThomas TsangSu-Vui LoYu-Lung LauGabriel M LeungBenjamin J CowlingJ S Malik Peiris<h4>Background</h4>In an emerging influenza pandemic, estimating severity (the probability of a severe outcome, such as hospitalization, if infected) is a public health priority. As many influenza infections are subclinical, sero-surveillance is needed to allow reliable real-time estimates of infection attack rate (IAR) and severity.<h4>Methods and findings</h4>We tested 14,766 sera collected during the first wave of the 2009 pandemic in Hong Kong using viral microneutralization. We estimated IAR and infection-hospitalization probability (IHP) from the serial cross-sectional serologic data and hospitalization data. Had our serologic data been available weekly in real time, we would have obtained reliable IHP estimates 1 wk after, 1-2 wk before, and 3 wk after epidemic peak for individuals aged 5-14 y, 15-29 y, and 30-59 y. The ratio of IAR to pre-existing seroprevalence, which decreased with age, was a major determinant for the timeliness of reliable estimates. If we began sero-surveillance 3 wk after community transmission was confirmed, with 150, 350, and 500 specimens per week for individuals aged 5-14 y, 15-19 y, and 20-29 y, respectively, we would have obtained reliable IHP estimates for these age groups 4 wk before the peak. For 30-59 y olds, even 800 specimens per week would not have generated reliable estimates until the peak because the ratio of IAR to pre-existing seroprevalence for this age group was low. The performance of serial cross-sectional sero-surveillance substantially deteriorates if test specificity is not near 100% or pre-existing seroprevalence is not near zero. These potential limitations could be mitigated by choosing a higher titer cutoff for seropositivity. If the epidemic doubling time is longer than 6 d, then serial cross-sectional sero-surveillance with 300 specimens per week would yield reliable estimates when IAR reaches around 6%-10%.<h4>Conclusions</h4>Serial cross-sectional serologic data together with clinical surveillance data can allow reliable real-time estimates of IAR and severity in an emerging pandemic. Sero-surveillance for pandemics should be considered.https://journals.plos.org/plosmedicine/article/file?id=10.1371/journal.pmed.1001103&type=printable |
| spellingShingle | Joseph T Wu Andrew Ho Edward S K Ma Cheuk Kwong Lee Daniel K W Chu Po-Lai Ho Ivan F N Hung Lai Ming Ho Che Kit Lin Thomas Tsang Su-Vui Lo Yu-Lung Lau Gabriel M Leung Benjamin J Cowling J S Malik Peiris Estimating infection attack rates and severity in real time during an influenza pandemic: analysis of serial cross-sectional serologic surveillance data. PLoS Medicine |
| title | Estimating infection attack rates and severity in real time during an influenza pandemic: analysis of serial cross-sectional serologic surveillance data. |
| title_full | Estimating infection attack rates and severity in real time during an influenza pandemic: analysis of serial cross-sectional serologic surveillance data. |
| title_fullStr | Estimating infection attack rates and severity in real time during an influenza pandemic: analysis of serial cross-sectional serologic surveillance data. |
| title_full_unstemmed | Estimating infection attack rates and severity in real time during an influenza pandemic: analysis of serial cross-sectional serologic surveillance data. |
| title_short | Estimating infection attack rates and severity in real time during an influenza pandemic: analysis of serial cross-sectional serologic surveillance data. |
| title_sort | estimating infection attack rates and severity in real time during an influenza pandemic analysis of serial cross sectional serologic surveillance data |
| url | https://journals.plos.org/plosmedicine/article/file?id=10.1371/journal.pmed.1001103&type=printable |
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