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Age-specific incidence of A/H1N1 2009 influenza infection in England from sequential antibody prevalence data using likelihood-based estimation.

Baguelin M, Hoschler K, Stanford E, Waight P, Hardelid P, Andrews N, Miller E - PLoS ONE (2011)

Bottom Line: This method is applied to derive the cumulative and weekly incidence of A/H1N1 pandemic influenza in England during the second wave using sera taken between September 2009 and February 2010 in four age groups (1-4, 5-14, 15-24, 25-44 years).The highest cumulative incidence was in 5-14 year olds (59%, 95% credible interval (CI): 52%, 68%) followed by 1-4 year olds (49%, 95% CI: 38%, 61%), rates 20 and 40 times higher respectively than estimated from clinical surveillance.The method provides a more accurate and continuous measure of incidence than achieved by comparing prevalence in samples grouped by time period.

View Article: PubMed Central - PubMed

Affiliation: Immunisation, Hepatitis and Blood Safety Department, Health Protection Agency, London, United Kingdom. marc.baguelin@hpa.org.uk

ABSTRACT
Estimating the age-specific incidence of an emerging pathogen is essential for understanding its severity and transmission dynamics. This paper describes a statistical method that uses likelihoods to estimate incidence from sequential serological data. The method requires information on seroconversion intervals and allows integration of information on the temporal distribution of cases from clinical surveillance. Among a family of candidate incidences, a likelihood function is derived by reconstructing the change in seroprevalence from seroconversion following infection and comparing it with the observed sequence of positivity among the samples. This method is applied to derive the cumulative and weekly incidence of A/H1N1 pandemic influenza in England during the second wave using sera taken between September 2009 and February 2010 in four age groups (1-4, 5-14, 15-24, 25-44 years). The highest cumulative incidence was in 5-14 year olds (59%, 95% credible interval (CI): 52%, 68%) followed by 1-4 year olds (49%, 95% CI: 38%, 61%), rates 20 and 40 times higher respectively than estimated from clinical surveillance. The method provides a more accurate and continuous measure of incidence than achieved by comparing prevalence in samples grouped by time period.

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Related in: MedlinePlus

Estimation of the interval to seroconversion.a) Proportion of individuals with HI titer <32 by interval since symptom onset: blue lines and points show the proportion in four-day intervals with confidence intervals and the red curve show the fitted parametric inverse cumulative distributions with the 95% CI (credible intervals) and b) distribution of the time to seroconversion since symptoms with 95% CI.
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pone-0017074-g002: Estimation of the interval to seroconversion.a) Proportion of individuals with HI titer <32 by interval since symptom onset: blue lines and points show the proportion in four-day intervals with confidence intervals and the red curve show the fitted parametric inverse cumulative distributions with the 95% CI (credible intervals) and b) distribution of the time to seroconversion since symptoms with 95% CI.

Mentions: The posterior mean values for the scaled Weibull distribution parameters are  = 0.87 [0.77, 0.94], shape = 2.42 [1.44, 4.26] and scale = 12.87 [9.55, 16.98]. This means that, among those who do seroconvert (87%), 50% will have seroconverted by the 12th day and 95% by the 21st day (Figure 2). The posterior covariance matrix for the parameters (, shape and scale) is:


Age-specific incidence of A/H1N1 2009 influenza infection in England from sequential antibody prevalence data using likelihood-based estimation.

Baguelin M, Hoschler K, Stanford E, Waight P, Hardelid P, Andrews N, Miller E - PLoS ONE (2011)

Estimation of the interval to seroconversion.a) Proportion of individuals with HI titer <32 by interval since symptom onset: blue lines and points show the proportion in four-day intervals with confidence intervals and the red curve show the fitted parametric inverse cumulative distributions with the 95% CI (credible intervals) and b) distribution of the time to seroconversion since symptoms with 95% CI.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3044152&req=5

pone-0017074-g002: Estimation of the interval to seroconversion.a) Proportion of individuals with HI titer <32 by interval since symptom onset: blue lines and points show the proportion in four-day intervals with confidence intervals and the red curve show the fitted parametric inverse cumulative distributions with the 95% CI (credible intervals) and b) distribution of the time to seroconversion since symptoms with 95% CI.
Mentions: The posterior mean values for the scaled Weibull distribution parameters are  = 0.87 [0.77, 0.94], shape = 2.42 [1.44, 4.26] and scale = 12.87 [9.55, 16.98]. This means that, among those who do seroconvert (87%), 50% will have seroconverted by the 12th day and 95% by the 21st day (Figure 2). The posterior covariance matrix for the parameters (, shape and scale) is:

Bottom Line: This method is applied to derive the cumulative and weekly incidence of A/H1N1 pandemic influenza in England during the second wave using sera taken between September 2009 and February 2010 in four age groups (1-4, 5-14, 15-24, 25-44 years).The highest cumulative incidence was in 5-14 year olds (59%, 95% credible interval (CI): 52%, 68%) followed by 1-4 year olds (49%, 95% CI: 38%, 61%), rates 20 and 40 times higher respectively than estimated from clinical surveillance.The method provides a more accurate and continuous measure of incidence than achieved by comparing prevalence in samples grouped by time period.

View Article: PubMed Central - PubMed

Affiliation: Immunisation, Hepatitis and Blood Safety Department, Health Protection Agency, London, United Kingdom. marc.baguelin@hpa.org.uk

ABSTRACT
Estimating the age-specific incidence of an emerging pathogen is essential for understanding its severity and transmission dynamics. This paper describes a statistical method that uses likelihoods to estimate incidence from sequential serological data. The method requires information on seroconversion intervals and allows integration of information on the temporal distribution of cases from clinical surveillance. Among a family of candidate incidences, a likelihood function is derived by reconstructing the change in seroprevalence from seroconversion following infection and comparing it with the observed sequence of positivity among the samples. This method is applied to derive the cumulative and weekly incidence of A/H1N1 pandemic influenza in England during the second wave using sera taken between September 2009 and February 2010 in four age groups (1-4, 5-14, 15-24, 25-44 years). The highest cumulative incidence was in 5-14 year olds (59%, 95% credible interval (CI): 52%, 68%) followed by 1-4 year olds (49%, 95% CI: 38%, 61%), rates 20 and 40 times higher respectively than estimated from clinical surveillance. The method provides a more accurate and continuous measure of incidence than achieved by comparing prevalence in samples grouped by time period.

Show MeSH
Related in: MedlinePlus