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This dissertation, Diagnosis and Surveillance of Human Influenza Virus Infection by Ka-yeung, Cheng, 鄭家揚, was obtained from The University of Hong Kong (Pokfulam, Hong Kong) and is being sold pursuant to Creative Commons: Attribution 3.0 Hong Kong License. The content of this dissertation has not been altered in any way. We have altered the formatting in order to facilitate the ease of printing and reading of the dissertation. All rights not granted by the above license are retained by the author. Abstract: 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 DOI: 10.5353/th_b4807981 Subjects: Influenza

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