Joint quantification of transmission dynamics and diagnostic accuracy applied to influenza
The influenza A (H1N1) pandemic 2009 posed an epidemiological challenge in ascertaining all cases. Although the counting of all influenza cases in real time is often not feasible, empirical observations always involve diagnostic test procedures. This offers an opportunity to jointly quantify transmi...
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| Format: | Article |
| Language: | English |
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AIMS Press
2010-12-01
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| Series: | Mathematical Biosciences and Engineering |
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| Online Access: | https://www.aimspress.com/article/doi/10.3934/mbe.2011.8.49 |
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| author | Hiroshi Nishiura |
| author_facet | Hiroshi Nishiura |
| author_sort | Hiroshi Nishiura |
| collection | DOAJ |
| description | The influenza A (H1N1) pandemic 2009 posed an epidemiological challenge in ascertaining all cases. Although the counting of all influenza cases in real time is often not feasible, empirical observations always involve diagnostic test procedures. This offers an opportunity to jointly quantify transmission dynamics and diagnostic accuracy. We have developed a joint estimation procedure that exploits parsimonious models to describe the epidemic dynamics and that parameterizes the number of test positives and test negatives as a function of time. Our analyses of simulated data and data from the empirical observation of interpandemic influenza A (H1N1) from 2007-08 in Japan indicate that the proposed approach permits a more precise quantification of the transmission dynamics compared to methods that rely on test positive cases alone. The analysis of entry screening data for the H1N1 pandemic 2009 at Tokyo-Narita airport helped us quantify the very limited specificity of influenza-like illness in detecting actual influenza cases in the passengers. The joint quantification does not require us to condition diagnostic accuracy on any pre-defined study population. Our study suggests that by consistently reporting both test positive and test negative cases, the usefulness of extractable information from routine surveillance record of infectious diseases would be maximized. |
| format | Article |
| id | doaj-art-c5678bdc61434795813454ad80bee972 |
| institution | Kabale University |
| issn | 1551-0018 |
| language | English |
| publishDate | 2010-12-01 |
| publisher | AIMS Press |
| record_format | Article |
| series | Mathematical Biosciences and Engineering |
| spelling | doaj-art-c5678bdc61434795813454ad80bee9722025-01-24T02:01:20ZengAIMS PressMathematical Biosciences and Engineering1551-00182010-12-0181496410.3934/mbe.2011.8.49Joint quantification of transmission dynamics and diagnostic accuracy applied to influenzaHiroshi Nishiura0PRESTO, Japan Science and Technology Agency (JST), 4-1-8 Honcho Kawaguchi, Saitama 332-0012The influenza A (H1N1) pandemic 2009 posed an epidemiological challenge in ascertaining all cases. Although the counting of all influenza cases in real time is often not feasible, empirical observations always involve diagnostic test procedures. This offers an opportunity to jointly quantify transmission dynamics and diagnostic accuracy. We have developed a joint estimation procedure that exploits parsimonious models to describe the epidemic dynamics and that parameterizes the number of test positives and test negatives as a function of time. Our analyses of simulated data and data from the empirical observation of interpandemic influenza A (H1N1) from 2007-08 in Japan indicate that the proposed approach permits a more precise quantification of the transmission dynamics compared to methods that rely on test positive cases alone. The analysis of entry screening data for the H1N1 pandemic 2009 at Tokyo-Narita airport helped us quantify the very limited specificity of influenza-like illness in detecting actual influenza cases in the passengers. The joint quantification does not require us to condition diagnostic accuracy on any pre-defined study population. Our study suggests that by consistently reporting both test positive and test negative cases, the usefulness of extractable information from routine surveillance record of infectious diseases would be maximized.https://www.aimspress.com/article/doi/10.3934/mbe.2011.8.49diagnosisinfluenza.transmissionepidemiologymodel |
| spellingShingle | Hiroshi Nishiura Joint quantification of transmission dynamics and diagnostic accuracy applied to influenza Mathematical Biosciences and Engineering diagnosis influenza. transmission epidemiology model |
| title | Joint quantification of transmission dynamics and diagnostic accuracy applied to influenza |
| title_full | Joint quantification of transmission dynamics and diagnostic accuracy applied to influenza |
| title_fullStr | Joint quantification of transmission dynamics and diagnostic accuracy applied to influenza |
| title_full_unstemmed | Joint quantification of transmission dynamics and diagnostic accuracy applied to influenza |
| title_short | Joint quantification of transmission dynamics and diagnostic accuracy applied to influenza |
| title_sort | joint quantification of transmission dynamics and diagnostic accuracy applied to influenza |
| topic | diagnosis influenza. transmission epidemiology model |
| url | https://www.aimspress.com/article/doi/10.3934/mbe.2011.8.49 |
| work_keys_str_mv | AT hiroshinishiura jointquantificationoftransmissiondynamicsanddiagnosticaccuracyappliedtoinfluenza |