Relationship among epidemiological parameters of six childhood infections in a non-immunized Brazilian community

Amaku, Marcos; Azevedo, Raymundo Soares; Castro, R. M.; Massad, Eduardo; Coutinho, Francisco Antônio Bezerra

doi:10.1590/s0074-02762009000600013

Cited by 10 publications

(13 citation statements)

References 18 publications

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“…Amaku et al [20] fit a catalytic model with an age-dependent force of infection function to serological data from Brazil, the same data used by Cox et al [14]. The analysis estimated an average age at infection of 19.08 months (95% CI 16.68–21.48) and the force of infection function peaked at about 0.9/person/year in the 2-year age group.…”

Section: Discussionmentioning

confidence: 99%

“…The simple catalytic model can be modified to allow for varied structures for non-immunizing infections[18], and different forms of the force of infection function[19–21]. Previously, catalytic models have been used to provide estimates of the rate of decay of maternal antibodies and the per capita rate at which susceptible individuals acquire infection (the force of infection) [19, 20, 22, 23]. Subsequently, one can establish the average age at primary infection and a “window” of vaccination.…”

Section: Introductionmentioning

confidence: 99%

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Defining the vaccination window for respiratory syncytial virus (RSV) using age-seroprevalence data for children in Kilifi, Kenya

et al. 2017

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BackgroundRespiratory syncytial virus (RSV) is an important cause of lower respiratory tract disease in early life and a target for vaccine prevention. Data on the age-prevalence of RSV specific antibodies will inform on optimizing vaccine delivery.MethodsArchived plasma samples were randomly selected within age strata from 960 children less than 145 months of age admitted to Kilifi County Hospital pediatric wards between 2007 and 2010. Samples were tested for antibodies to RSV using crude virus IgG ELISA. Seroprevalence (and 95% confidence intervals) was estimated as the proportion of children with specific antibodies above a defined cut-off level. Nested catalytic models were used to explore different assumptions on antibody dynamics and estimate the rates of decay of RSV specific maternal antibody and acquisition of infection with age, and the average age of infection.ResultsRSV specific antibody prevalence was 100% at age 0-<1month, declining rapidly over the first 6 months of life, followed by an increase in the second half of the first year of life and beyond. Seroprevalence was lowest throughout the age range 5–11 months; all children were seropositive beyond 3 years of age. The best fit model to the data yielded estimates for the rate of infection of 0.78/person/year (95% CI 0.65–0.97) and 1.69/person/year (95% CI 1.27–2.04) for ages 0-<1 year and 1-<12 years, respectively. The rate of loss of maternal antibodies was estimated as 2.54/year (95% CI 2.30–2.90), i.e. mean duration 4.7 months. The mean age at primary infection was estimated at 15 months (95% CI 13–18).ConclusionsThe rate of decay of maternal antibody prevalence and subsequent age-acquisition of infection are rapid, and the average age at primary infection early. The vaccination window is narrow, and suggests optimal targeting of vaccine to infants 5 months and above to achieve high seroconversion.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Defining the vaccination window for respiratory syncytial virus (RSV) using age-seroprevalence data for children in Kilifi, Kenya

et al. 2017

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“…To compare the seroprevalence fitted curves for the North and South regions and the corresponding force of infection curves, we used Monte Carlo simulations to generate CIs for the curves based on the approach proposed by Amaku et al [33]. In this case, the Monte Carlo method is a straightforward and accurate alternative for estimating CIs because the fitted parameters for the seroprevalence functions and the corresponding covariance matrices are available.…”

Section: Methodsmentioning

confidence: 99%

“…More details on how this algorithm works may be found in Amaku et al [33]. We have implemented the computational routines using the R statistical software, version 2.10 (Institute for Statistics and Mathematics, Vienna, Austria) [34].…”

Section: Methodsmentioning

confidence: 99%

Modelling the Force of Infection for Hepatitis A in an Urban Population-Based Survey: A Comparison of Transmission Patterns in Brazilian Macro-Regions

et al. 2014

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BackgroundThis study aimed to identify the transmission pattern of hepatitis A (HA) infection based on a primary dataset from the Brazilian National Hepatitis Survey in a pre-vaccination context. The national survey conducted in urban areas disclosed two epidemiological scenarios with low and intermediate HA endemicity.MethodsA catalytic model of HA transmission was built based on a national seroprevalence survey (2005 to 2009). The seroprevalence data from 7,062 individuals aged 5–69 years from all the Brazilian macro-regions were included. We built up three models: fully homogeneous mixing model, with constant contact pattern; the highly assortative model and the highly assortative model with the additional component accounting for contacts with infected food/water. Curves of prevalence, force of infection (FOI) and the number of new infections with 99% confidence intervals (CIs) were compared between the intermediate (North, Northeast, Midwest and Federal District) and low (South and Southeast) endemicity areas. A contour plot was also constructed.ResultsThe anti- HAV IgG seroprevalence was 68.8% (95% CI, 64.8%–72.5%) and 33.7% (95% CI, 32.4%–35.1%) for the intermediate and low endemicity areas, respectively, according to the field data analysis. The models showed that a higher force of infection was identified in the 10- to 19-year-old age cohort (∼9,000 infected individuals per year per 100,000 susceptible persons) in the intermediate endemicity area, whereas a higher force of infection occurred in the 15- to 29-year-old age cohort (∼6,000 infected individuals per year per 100,000 susceptible persons) for the other macro-regions.ConclusionOur findings support the shift of Brazil toward intermediate and low endemicity levels with the shift of the risk of infection to older age groups. These estimates of HA force of infection stratified by age and endemicity levels are useful information to characterize the pre-vaccination scenario in Brazil.

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“…±1%, as in Massad et al, 2009) or by evaluating the effects across a range of values defined by plausible probability density functions (e.g. Amaku et al, 2003, 2009). However, because the values of other parameters are held fixed at best point estimates, these strategies do not account for interaction effects in non-linear dynamic models, and do not assess global uncertainty in parameters or outcome.…”

Section: Introductionmentioning

confidence: 99%

Probabilistic uncertainty analysis of epidemiological modeling to guide public health intervention policy

et al. 2014

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Mathematical modeling of disease transmission has provided quantitative predictions for health policy, facilitating the evaluation of epidemiological outcomes and the cost-effectiveness of interventions. However, typical sensitivity analyses of deterministic dynamic infectious disease models focus on model architecture and the relative importance of parameters but neglect parameter uncertainty when reporting model predictions. Consequently, model results that identify point estimates of intervention levels necessary to terminate transmission yield limited insight into the probability of success. We apply probabilistic uncertainty analysis to a dynamic model of influenza transmission and assess global uncertainty in outcome. We illustrate that when parameter uncertainty is not incorporated into outcome estimates, levels of vaccination and treatment predicted to prevent an influenza epidemic will only have an approximately 50% chance of terminating transmission and that sensitivity analysis alone is not sufficient to obtain this information. We demonstrate that accounting for parameter uncertainty yields probabilities of epidemiological outcomes based on the degree to which data support the range of model predictions. Unlike typical sensitivity analyses of dynamic models that only address variation in parameters, the probabilistic uncertainty analysis described here enables modelers to convey the robustness of their predictions to policy makers, extending the power of epidemiological modeling to improve public health.

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Relationship among epidemiological parameters of six childhood infections in a non-immunized Brazilian community

Cited by 10 publications

References 18 publications

Defining the vaccination window for respiratory syncytial virus (RSV) using age-seroprevalence data for children in Kilifi, Kenya

Defining the vaccination window for respiratory syncytial virus (RSV) using age-seroprevalence data for children in Kilifi, Kenya

Modelling the Force of Infection for Hepatitis A in an Urban Population-Based Survey: A Comparison of Transmission Patterns in Brazilian Macro-Regions

Probabilistic uncertainty analysis of epidemiological modeling to guide public health intervention policy

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