File Download
 
 
Supplementary

Postgraduate Thesis: Diagnosis and surveillance of human influenza virus infection
  • Basic View
  • Metadata View
  • XML View
TitleDiagnosis and surveillance of human influenza virus infection
 
AuthorsCheng, Ka-yeung.
鄭家揚.
 
Issue Date2012
 
PublisherThe University of Hong Kong (Pokfulam, Hong Kong)
 
AbstractBackground: Early and accurate diagnosis of influenza helps start correct treatment and prevention strategies at individual level. Ongoing systematic collection, analysis and dissemination of the surveillance data from aggregated diagnostic results and other early indicators help gather the foremost disease information for all subsequent control and mitigation strategies in the community. Disease information from surveillance results then feed back to medical practitioners for improving diagnosis. By improving this loop of disease information transfer in terms of accuracy and timeliness, interventions for disease control can be applied efficiently and effectively. Methods: Several new influenza diagnosis and surveillance methods were explored and evaluated by comparing with laboratory reference test results. Logistic regression models were applied to synthesize a refined clinical guideline for human influenza infections. The performance of QuickVue rapid diagnostic test was evaluated in a community setting. Weekly positive rates from the above two diagnostic methods, together with three other different syndromic surveillance systems, including data from school absenteeism, active telephone survey and internet based survey were evaluated according to the US CDC public health surveillance systems guideline in terms of their utility, correlations and aberration detection performance. Different combinations of surveillance data streams and aberration detection algorithms were evaluated to delineate the optimal use of multi-stream influenza surveillance data. A framework of efficient surveillance data dissemination was synthesized by incorporating the merits of the online national surveillance websites and the principles of efficient data presentation and dashboard design. Results: A refined clinical diagnostic rule for influenza infection using fever, cough runny nose and clinic visit during high influenza activity months as predictors was scored the highest amount all other current clinical definitions. Time series weekly positive rate from this rule showed better correlation with reference community influenza activity than many other current clinical influenza definitions. The QuickVue rapid diagnostic test has an overall diagnostic sensitivity of 68% and specificity 96%, with an analytic sensitivity threshold of 105 to106 viral copies per ml. Weekly aggregated QuickVue and school absenteeism surveillance data was found to be highly correlated with hospital laboratory and community sentinel surveillance data, but the telephone and internet survey was only moderately correlated. Multiple univariate methods performed slightly better than multivariate methods for aberration detections in general. More sophisticated outbreak detection algorithms did not result in significant improvement of outbreak detection
 
AdvisorsCowling, BJ
Leung, GM
Ip, DKM
 
DegreeDoctor of Philosophy
 
SubjectInfluenza.
 
Dept/ProgramCommunity Medicine
 
DC FieldValue
dc.contributor.advisorCowling, BJ
 
dc.contributor.advisorLeung, GM
 
dc.contributor.advisorIp, DKM
 
dc.contributor.authorCheng, Ka-yeung.
 
dc.contributor.author鄭家揚.
 
dc.date.hkucongregation2012
 
dc.date.issued2012
 
dc.description.abstractBackground: Early and accurate diagnosis of influenza helps start correct treatment and prevention strategies at individual level. Ongoing systematic collection, analysis and dissemination of the surveillance data from aggregated diagnostic results and other early indicators help gather the foremost disease information for all subsequent control and mitigation strategies in the community. Disease information from surveillance results then feed back to medical practitioners for improving diagnosis. By improving this loop of disease information transfer in terms of accuracy and timeliness, interventions for disease control can be applied efficiently and effectively. Methods: Several new influenza diagnosis and surveillance methods were explored and evaluated by comparing with laboratory reference test results. Logistic regression models were applied to synthesize a refined clinical guideline for human influenza infections. The performance of QuickVue rapid diagnostic test was evaluated in a community setting. Weekly positive rates from the above two diagnostic methods, together with three other different syndromic surveillance systems, including data from school absenteeism, active telephone survey and internet based survey were evaluated according to the US CDC public health surveillance systems guideline in terms of their utility, correlations and aberration detection performance. Different combinations of surveillance data streams and aberration detection algorithms were evaluated to delineate the optimal use of multi-stream influenza surveillance data. A framework of efficient surveillance data dissemination was synthesized by incorporating the merits of the online national surveillance websites and the principles of efficient data presentation and dashboard design. Results: A refined clinical diagnostic rule for influenza infection using fever, cough runny nose and clinic visit during high influenza activity months as predictors was scored the highest amount all other current clinical definitions. Time series weekly positive rate from this rule showed better correlation with reference community influenza activity than many other current clinical influenza definitions. The QuickVue rapid diagnostic test has an overall diagnostic sensitivity of 68% and specificity 96%, with an analytic sensitivity threshold of 105 to106 viral copies per ml. Weekly aggregated QuickVue and school absenteeism surveillance data was found to be highly correlated with hospital laboratory and community sentinel surveillance data, but the telephone and internet survey was only moderately correlated. Multiple univariate methods performed slightly better than multivariate methods for aberration detections in general. More sophisticated outbreak detection algorithms did not result in significant improvement of outbreak detection
 
dc.description.naturepublished_or_final_version
 
dc.description.thesisdisciplineCommunity Medicine
 
dc.description.thesisleveldoctoral
 
dc.description.thesisnameDoctor of Philosophy
 
dc.identifier.hkulb4807981
 
dc.languageeng
 
dc.publisherThe University of Hong Kong (Pokfulam, Hong Kong)
 
dc.relation.ispartofHKU Theses Online (HKUTO)
 
dc.rightsThe author retains all proprietary rights, (such as patent rights) and the right to use in future works.
 
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License
 
dc.source.urihttp://hub.hku.hk/bib/B48079819
 
dc.subject.lcshInfluenza.
 
dc.titleDiagnosis and surveillance of human influenza virus infection
 
dc.typePG_Thesis
 
<?xml encoding="utf-8" version="1.0"?>
<item><contributor.advisor>Cowling, BJ</contributor.advisor>
<contributor.advisor>Leung, GM</contributor.advisor>
<contributor.advisor>Ip, DKM</contributor.advisor>
<contributor.author>Cheng, Ka-yeung.</contributor.author>
<contributor.author>&#37165;&#23478;&#25562;.</contributor.author>
<date.issued>2012</date.issued>
<description.abstract>&#65279;Background: Early and accurate diagnosis of influenza helps start correct treatment and

prevention strategies at individual level. Ongoing systematic collection, analysis and

dissemination of the surveillance data from aggregated diagnostic results and other early

indicators help gather the foremost disease information for all subsequent control and

mitigation strategies in the community. Disease information from surveillance results then

feed back to medical practitioners for improving diagnosis. By improving this loop of

disease information transfer in terms of accuracy and timeliness, interventions for disease

control can be applied efficiently and effectively.

Methods: Several new influenza diagnosis and surveillance methods were explored and

evaluated by comparing with laboratory reference test results. Logistic regression models

were applied to synthesize a refined clinical guideline for human influenza infections. The performance of QuickVue rapid diagnostic test was evaluated in a community setting.

Weekly positive rates from the above two diagnostic methods, together with three other

different syndromic surveillance systems, including data from school absenteeism, active

telephone survey and internet based survey were evaluated according to the US CDC

public health surveillance systems guideline in terms of their utility, correlations and

aberration detection performance. Different combinations of surveillance data streams and

aberration detection algorithms were evaluated to delineate the optimal use of multi-stream

influenza surveillance data. A framework of efficient surveillance data dissemination was

synthesized by incorporating the merits of the online national surveillance websites and the

principles of efficient data presentation and dashboard design.

Results: A refined clinical diagnostic rule for influenza infection using fever, cough runny

nose and clinic visit during high influenza activity months as predictors was scored the

highest amount all other current clinical definitions. Time series weekly positive rate from

this rule showed better correlation with reference community influenza activity than many

other current clinical influenza definitions. The QuickVue rapid diagnostic test has an

overall diagnostic sensitivity of 68% and specificity 96%, with an analytic sensitivity

threshold of 105 to106 viral copies per ml. Weekly aggregated QuickVue and school

absenteeism surveillance data was found to be highly correlated with hospital laboratory

and community sentinel surveillance data, but the telephone and internet survey was only

moderately correlated. Multiple univariate methods performed slightly better than

multivariate methods for aberration detections in general. More sophisticated outbreak

detection algorithms did not result in significant improvement of outbreak detection</description.abstract>
<language>eng</language>
<publisher>The University of Hong Kong (Pokfulam, Hong Kong)</publisher>
<relation.ispartof>HKU Theses Online (HKUTO)</relation.ispartof>
<rights>The author retains all proprietary rights, (such as patent rights) and the right to use in future works.</rights>
<rights>Creative Commons: Attribution 3.0 Hong Kong License</rights>
<source.uri>http://hub.hku.hk/bib/B48079819</source.uri>
<subject.lcsh>Influenza.</subject.lcsh>
<title>Diagnosis and surveillance of human influenza virus infection</title>
<type>PG_Thesis</type>
<identifier.hkul>b4807981</identifier.hkul>
<description.thesisname>Doctor of Philosophy</description.thesisname>
<description.thesislevel>doctoral</description.thesislevel>
<description.thesisdiscipline>Community Medicine</description.thesisdiscipline>
<description.nature>published_or_final_version</description.nature>
<date.hkucongregation>2012</date.hkucongregation>
<bitstream.url>http://hub.hku.hk/bitstream/10722/161576/1/FullText.pdf</bitstream.url>
</item>