New methods for analyzing serological data with applications to influenza surveillance
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- Department of Immunology, Weizmann Institute of Science, Rehovoth, Israel
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- Document Type:
- Manuscript
- Date:
- Received 05 October 2009 08:10 UTC; Posted 05 October 2009
- Subjects:
- Immunology, Evolutionary Biology
- Abstract:
Two important challenges to the use of serological assays for influenza surveillance include the substantial amount of experimental effort involved, and the inherent noisiness of serological data. Here, informed by the observation that log-transformed serological data (obtained from the hemagglutination-inhibition assay) exist in an effectively one-dimensional space, computational methods are developed for accurately and efficiently recovering unmeasured serological data from a sample of measured data, and systematically minimizing noise found in the measured data. Careful application of these methods would enable the collection of better-quality serological data on a greater number of circulating influenza viruses than is currently possible, and improve the ability to identify potential epidemic/pandemic viruses before they become widespread. Although the focus here is on influenza surveillance, the described methods are more widely applicable.
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- License:
- This document is licensed to the public under the Creative Commons Attribution 3.0 License
- How to cite this document:
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Ndifon, Wilfred. New methods for analyzing serological data with applications to influenza surveillance. Available from Nature Precedings <http://hdl.handle.net/10101/npre.2009.3820.1> (2009)
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