Article: Inferring Influenza Infection Attack Rate from Seroprevalence Data

TitleInferring Influenza Infection Attack Rate from Seroprevalence Data
Authors
Issue Date2014
PublisherPublic Library of Science. The Journal's web site is located at http://pathogens.plosjournals.org/perlserv/?request=index-html&issn=1553-7374
Citation
PLoS Pathogens, 2014, v. 10 n. 4, p. e1004054 How to Cite?
AbstractSeroprevalence survey is the most practical method for accurately estimating infection attack rate (IAR) in an epidemic such as influenza. These studies typically entail selecting an arbitrary titer threshold for seropositivity (e.g. microneutralization [MN] 1∶40) and assuming the probability of seropositivity given infection (infection-seropositivity probability, ISP) is 100% or similar to that among clinical cases. We hypothesize that such conventions are not necessarily robust because different thresholds may result in different IAR estimates and serologic responses of clinical cases may not be representative. To illustrate our hypothesis, we used an age-structured transmission model to fully characterize the transmission dynamics and seroprevalence rises of 2009 influenza pandemic A/H1N1 (pdmH1N1) during its first wave in Hong Kong. We estimated that while 99% of pdmH1N1 infections became MN1∶20 seropositive, only 72%, 62%, 58% and 34% of infections among age 3–12, 13–19, 20–29, 30–59 became MN1∶40 seropositive, which was much lower than the 90%–100% observed among clinical cases. The fitted model was consistent with prevailing consensus on pdmH1N1 transmission characteristics (e.g. initial reproductive number of 1.28 and mean generation time of 2.4 days which were within the consensus range), hence our ISP estimates were consistent with the transmission dynamics and temporal buildup of population-level immunity. IAR estimates in influenza seroprevalence studies are sensitive to seropositivity thresholds and ISP adjustments which in current practice are mostly chosen based on conventions instead of systematic criteria. Our results thus highlighted the need for reexamining conventional practice to develop standards for analyzing influenza serologic data (e.g. real-time assessment of bias in ISP adjustments by evaluating the consistency of IAR across multiple thresholds and with mixture models), especially in the context of pandemics when robustness and comparability of IAR estimates are most needed for informing situational awareness and risk assessment. The same principles are broadly applicable for seroprevalence studies of other infectious disease outbreaks.
Persistent Identifierhttp://hdl.handle.net/10722/218504
ISSN
2017 Impact Factor: 6.158
2015 SCImago Journal Rankings: 5.185
PubMed Central ID
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorWu, JTK-
dc.contributor.authorPerera, RAPM-
dc.contributor.authorChu, KW-
dc.contributor.authorLee, CK-
dc.contributor.authorHung, IFN-
dc.contributor.authorLin, CK-
dc.contributor.authorLo, SV-
dc.contributor.authorLau, YL-
dc.contributor.authorLeung, GM-
dc.contributor.authorCowling, BJ-
dc.contributor.authorPeiris, JSM-
dc.date.accessioned2015-09-18T06:41:28Z-
dc.date.available2015-09-18T06:41:28Z-
dc.date.issued2014-
dc.identifier.citationPLoS Pathogens, 2014, v. 10 n. 4, p. e1004054-
dc.identifier.issn1553-7366-
dc.identifier.urihttp://hdl.handle.net/10722/218504-
dc.description.abstractSeroprevalence survey is the most practical method for accurately estimating infection attack rate (IAR) in an epidemic such as influenza. These studies typically entail selecting an arbitrary titer threshold for seropositivity (e.g. microneutralization [MN] 1∶40) and assuming the probability of seropositivity given infection (infection-seropositivity probability, ISP) is 100% or similar to that among clinical cases. We hypothesize that such conventions are not necessarily robust because different thresholds may result in different IAR estimates and serologic responses of clinical cases may not be representative. To illustrate our hypothesis, we used an age-structured transmission model to fully characterize the transmission dynamics and seroprevalence rises of 2009 influenza pandemic A/H1N1 (pdmH1N1) during its first wave in Hong Kong. We estimated that while 99% of pdmH1N1 infections became MN1∶20 seropositive, only 72%, 62%, 58% and 34% of infections among age 3–12, 13–19, 20–29, 30–59 became MN1∶40 seropositive, which was much lower than the 90%–100% observed among clinical cases. The fitted model was consistent with prevailing consensus on pdmH1N1 transmission characteristics (e.g. initial reproductive number of 1.28 and mean generation time of 2.4 days which were within the consensus range), hence our ISP estimates were consistent with the transmission dynamics and temporal buildup of population-level immunity. IAR estimates in influenza seroprevalence studies are sensitive to seropositivity thresholds and ISP adjustments which in current practice are mostly chosen based on conventions instead of systematic criteria. Our results thus highlighted the need for reexamining conventional practice to develop standards for analyzing influenza serologic data (e.g. real-time assessment of bias in ISP adjustments by evaluating the consistency of IAR across multiple thresholds and with mixture models), especially in the context of pandemics when robustness and comparability of IAR estimates are most needed for informing situational awareness and risk assessment. The same principles are broadly applicable for seroprevalence studies of other infectious disease outbreaks.-
dc.languageeng-
dc.publisherPublic Library of Science. The Journal's web site is located at http://pathogens.plosjournals.org/perlserv/?request=index-html&issn=1553-7374-
dc.relation.ispartofPLoS Pathogens-
dc.rightsThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.-
dc.titleInferring Influenza Infection Attack Rate from Seroprevalence Data-
dc.typeArticle-
dc.identifier.emailWu, JTK: joewu@hku.hk-
dc.identifier.emailPerera, RAPM: mahenp@hku.hk-
dc.identifier.emailChu, KW: dkwchu@hku.hk-
dc.identifier.emailHung, IFN: ivanhung@hkucc.hku.hk-
dc.identifier.emailLau, YL: lauylung@hku.hk-
dc.identifier.emailLeung, GM: gmleung@hku.hk-
dc.identifier.emailCowling, BJ: bcowling@hku.hk-
dc.identifier.emailPeiris, JSM: malik@hkucc.hku.hk-
dc.identifier.authorityWu, JTK=rp00517-
dc.identifier.authorityPerera, RAPM=rp02500-
dc.identifier.authorityChu, KW=rp02512-
dc.identifier.authorityHung, IFN=rp00508-
dc.identifier.authorityLau, YL=rp00361-
dc.identifier.authorityLeung, GM=rp00460-
dc.identifier.authorityCowling, BJ=rp01326-
dc.identifier.authorityPeiris, JSM=rp00410-
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.1371/journal.ppat.1004054-
dc.identifier.pmid24699693-
dc.identifier.pmcidPMC3974861-
dc.identifier.scopuseid_2-s2.0-84901293079-
dc.identifier.hkuros253132-
dc.identifier.hkuros287719-
dc.identifier.volume10-
dc.identifier.issue4-
dc.identifier.spagee1004054-
dc.identifier.epagee1004054-
dc.identifier.isiWOS:000335502100017-
dc.publisher.placeUnited States-

Export via OAI-PMH Interface in XML Formats


OR


Export to Other Non-XML Formats