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Modelling respiratory infection control measure effects
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| Zeitschriftentitel: | Epidemiology and Infection |
|---|---|
| Personen und Körperschaften: | , , |
| In: | Epidemiology and Infection, 136, 2008, 3, S. 299-308 |
| Format: | E-Article |
| Sprache: | Englisch |
| veröffentlicht: |
Cambridge University Press (CUP)
|
| Schlagwörter: |
| author_facet |
LIAO, C. M. CHEN, S. C. CHANG, C. F. LIAO, C. M. CHEN, S. C. CHANG, C. F. |
|---|---|
| author |
LIAO, C. M. CHEN, S. C. CHANG, C. F. |
| spellingShingle |
LIAO, C. M. CHEN, S. C. CHANG, C. F. Epidemiology and Infection Modelling respiratory infection control measure effects Infectious Diseases Epidemiology |
| author_sort |
liao, c. m. |
| spelling |
LIAO, C. M. CHEN, S. C. CHANG, C. F. 0950-2688 1469-4409 Cambridge University Press (CUP) Infectious Diseases Epidemiology http://dx.doi.org/10.1017/s0950268807008631 <jats:title>SUMMARY</jats:title><jats:p>One of the most pressing issues in facing emerging and re-emerging respiratory infections is how to bring them under control with current public health measures. Approaches such as the Wells–Riley equation, competing-risks model, and Von Foerster equation are used to prioritize control-measure efforts. Here we formulate how to integrate those three different types of functional relationship to construct easy-to-use and easy-to-interpret critical-control lines that help determine optimally the intervention strategies for containing airborne infections. We show that a combination of assigned effective public health interventions and enhanced engineering control measures would have a high probability for containing airborne infection. We suggest that integrated analysis to enhance modelling the impact of potential control measures against airborne infections presents an opportunity to assess risks and benefits. We demonstrate the approach with examples of optimal control measures to prioritize respiratory infections of severe acute respiratory syndrome (SARS), influenza, measles, and chickenpox.</jats:p> Modelling respiratory infection control measure effects Epidemiology and Infection |
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Modelling respiratory infection control measure effects |
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Modelling respiratory infection control measure effects |
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Modelling respiratory infection control measure effects |
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Modelling respiratory infection control measure effects |
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Modelling respiratory infection control measure effects |
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Modelling respiratory infection control measure effects |
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modelling respiratory infection control measure effects |
| topic |
Infectious Diseases Epidemiology |
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http://dx.doi.org/10.1017/s0950268807008631 |
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2008 |
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299-308 |
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<jats:title>SUMMARY</jats:title><jats:p>One of the most pressing issues in facing emerging and re-emerging respiratory infections is how to bring them under control with current public health measures. Approaches such as the Wells–Riley equation, competing-risks model, and Von Foerster equation are used to prioritize control-measure efforts. Here we formulate how to integrate those three different types of functional relationship to construct easy-to-use and easy-to-interpret critical-control lines that help determine optimally the intervention strategies for containing airborne infections. We show that a combination of assigned effective public health interventions and enhanced engineering control measures would have a high probability for containing airborne infection. We suggest that integrated analysis to enhance modelling the impact of potential control measures against airborne infections presents an opportunity to assess risks and benefits. We demonstrate the approach with examples of optimal control measures to prioritize respiratory infections of severe acute respiratory syndrome (SARS), influenza, measles, and chickenpox.</jats:p> |
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| author | LIAO, C. M., CHEN, S. C., CHANG, C. F. |
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| description | <jats:title>SUMMARY</jats:title><jats:p>One of the most pressing issues in facing emerging and re-emerging respiratory infections is how to bring them under control with current public health measures. Approaches such as the Wells–Riley equation, competing-risks model, and Von Foerster equation are used to prioritize control-measure efforts. Here we formulate how to integrate those three different types of functional relationship to construct easy-to-use and easy-to-interpret critical-control lines that help determine optimally the intervention strategies for containing airborne infections. We show that a combination of assigned effective public health interventions and enhanced engineering control measures would have a high probability for containing airborne infection. We suggest that integrated analysis to enhance modelling the impact of potential control measures against airborne infections presents an opportunity to assess risks and benefits. We demonstrate the approach with examples of optimal control measures to prioritize respiratory infections of severe acute respiratory syndrome (SARS), influenza, measles, and chickenpox.</jats:p> |
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| series | Epidemiology and Infection |
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| spelling | LIAO, C. M. CHEN, S. C. CHANG, C. F. 0950-2688 1469-4409 Cambridge University Press (CUP) Infectious Diseases Epidemiology http://dx.doi.org/10.1017/s0950268807008631 <jats:title>SUMMARY</jats:title><jats:p>One of the most pressing issues in facing emerging and re-emerging respiratory infections is how to bring them under control with current public health measures. Approaches such as the Wells–Riley equation, competing-risks model, and Von Foerster equation are used to prioritize control-measure efforts. Here we formulate how to integrate those three different types of functional relationship to construct easy-to-use and easy-to-interpret critical-control lines that help determine optimally the intervention strategies for containing airborne infections. We show that a combination of assigned effective public health interventions and enhanced engineering control measures would have a high probability for containing airborne infection. We suggest that integrated analysis to enhance modelling the impact of potential control measures against airborne infections presents an opportunity to assess risks and benefits. We demonstrate the approach with examples of optimal control measures to prioritize respiratory infections of severe acute respiratory syndrome (SARS), influenza, measles, and chickenpox.</jats:p> Modelling respiratory infection control measure effects Epidemiology and Infection |
| spellingShingle | LIAO, C. M., CHEN, S. C., CHANG, C. F., Epidemiology and Infection, Modelling respiratory infection control measure effects, Infectious Diseases, Epidemiology |
| title | Modelling respiratory infection control measure effects |
| title_full | Modelling respiratory infection control measure effects |
| title_fullStr | Modelling respiratory infection control measure effects |
| title_full_unstemmed | Modelling respiratory infection control measure effects |
| title_short | Modelling respiratory infection control measure effects |
| title_sort | modelling respiratory infection control measure effects |
| title_unstemmed | Modelling respiratory infection control measure effects |
| topic | Infectious Diseases, Epidemiology |
| url | http://dx.doi.org/10.1017/s0950268807008631 |