Modelling forces of infection for mumps, rubella and parvovirus: a statistical perspective

The force of infection is a fundamental epidemiological parameter of infectious diseases. For many infectious diseases it is assumed that the force of infection is age-dependant. Although the force of infection can be estimated directly from a follow up study, it is much more common to have cross-sectional seroprevalence data from which the seroprevalence and the force of infection can be estimated. Here we propose to model the seroprevalence with three different parametric models: a nonlinear least squares model proposed by Farrington (1990); a logistic model, estimated using the generalized linear models; two fractional polynomial models of different order. We illustrate the methods on three seroprevalence samples, taken by the literature, regarding the following infectious diseases: mumps, rubella and parvovirus. Besides, in order to determine the optimal sample size for a serological survey, we show the serious problems of the standard confidence interval for a binomial proportion and we introduce some alternative confidence intervals proposed by the literature

Poststratification for Facebook Surveys

Exercise on multilevel regression and poststratification (MRP) for nonprobability samples, with respondents recruited on Facebook

Modelling multivariate, overdispersed binomial data with additive and multiplicative random effects

When modelling multivariate binomial data, it often occurs that it is necessary to take into consideration both clustering and overdispersion, the former arising from the dependence between data, and the latter due to the additional variability in the data not prescribed by the distribution. If interest lies in accommodating both phenomena at the same time, we can use separate sets of random effects that capture the within-cluster association and the extra variability. In particular, the random effects for overdispersion can be included in the model either additively or multiplicatively. For this purpose, we propose a series of Bayesian hierarchical models that deal simultaneously with both phenomena. The proposed models are applied to bivariate repeated prevalence data for hepatitis C virus (HCV) and human immunodeficiency virus (HIV) infection in injecting drug users in Italy from 1998 to 2007

Towards measles elimination in Italy: monitoring herd immunity by Bayesian mixture modelling of serological data.

The analysis of post-vaccination serological data poses nontrivial issues to the epidemiologists and policy makers who want to assess the effects of immunisation programmes. This is especially true for infections on the path to elimination as is the case for measles. We address these problems by using Bayesian Normal mixture models fitted to antibody counts data. This methodology allows us to estimate the seroprevalence of measles by age and, in contrast to conventional methods based on fixed cut-off points, to also distinguish between groups of individuals with different degrees of immunisation. We applied our methodology to two serological samples collected in Tuscany (Italy) in 2003 and in 2005-2006 respectively, i.e., before and after a large vaccination campaign targeted to school-age children. Besides showing the impact of the campaign, we were able to accurately identify a large pocket of susceptible individuals aged about 13-14 in 2005-2006, and a larger group of weakly immune individuals aged about 20 in 2005-2006. These cohorts therefore represent possible targets for further interventions towards measles elimination. (C) 2012 Elsevier B.V. All rights reserved. Infectious Diseases; measles elimination; vaccination; monitoring herd immunity; seroprevalence data; bayesian mixture model

Characteristics of men included in the sample.

<p>Descriptive statistics on socio-demographic and sexual behaviour characteristics for men, stratified by sequence, and overall, Manicaland (Zimbabwe), 2000–2011. Sequences are sorted in descending order by HIV prevalence.</p

Adjusted prevalence odds ratios for women.

<p>Adjusted odds ratios and 95% confidence intervals for the probability of being HIV-infected, based on logistic regression models, among women, Manicaland (Zimbabwe), 2000–2011. Sample size is varying because of missing values.</p

Relationship between sequences and HIV across birth cohorts.

<p>Change in the distribution across birth cohorts of the sequences and of their HIV prevalence, Manicaland (Zimbabwe), 2000–2011. Top row: bar plot of the distribution of sequences across birth cohorts, for women (left panel) and for men (right panel). Bottom row: bar plot (with 95% confidence interval) of the HIV prevalence associated with each sequence across birth cohorts, for women (left panel) and for men (right panel).</p