Estimating seroconversion rates accounting for repeated infections by approximate Bayesian computation

This study presents a novel approach for inferring the incidence of infections by employing a quantitative model of the serum antibody response. Current methodologies often overlook the cumulative effect of an individual's infection history, making it challenging to obtain a marginal distribution for antibody concentrations. Our proposed approach leverages approximate Bayesian computation to simulate cross-sectional antibody responses and compare these to observed data, factoring in the impact of repeated infections. We then assess the empirical distribution functions of the simulated and observed antibody data utilizing Kolmogorov deviance, thereby incorporating a goodness-of-fit check. This new method not only matches the computational efficiency of preceding likelihood-based analyses but also facilitates the joint estimation of antibody noise parameters. The results affirm that the predictions generated by our within-host model closely align with the observed distributions from cross-sectional samples of a well-characterized population. Our findings mirror those of likelihood-based methodologies in scenarios of low infection pressure, such as the transmission of pertussis in Europe. However, our simulations reveal that in settings of higher infection pressure, likelihood-based approaches tend to underestimate the force of infection. Thus, our novel methodology presents significant advancements in estimating infection incidence, thereby enhancing our understanding of disease dynamics in the field of epidemiology

Noroviruses are highly infectious but there is strong variation in host susceptibility and virus pathogenicity

Noroviruses are a major public health concern: their high infectivity and environmental persistence have been documented in several studies. Genetic sequencing shows that noroviruses are highly variable, and exhibit rapid evolution. A few human challenge studies have been performed with norovirus, leading to estimates of their infectivity. However, such incidental estimates do not provide insight into the biological variation of the virus and the interaction with its human host. To study the variation in infectivity and pathogenicity of norovirus, multiple challenge studies must be analysed jointly, to compare their differences and describe how virus infectivity and host susceptibility vary. Since challenge studies can only provide a small sample of the diversity in the natural norovirus population, outbreaks should be exploited as an additional source of information. The present study shows how challenge studies and ‘natural experiments’ can be combined in a multilevel dose response framework. Infectivity and pathogenicity are analysed by secretor status as a host factor, and genogroup as a pathogen factor. Infectivity, characterized as the estimated mean infection risk when exposed to 1 genomic copy (qPCR unit)is 0.28 for GI norovirus, and 0.076 for GII virus, both in Se+ subjects. The corresponding risks of acute enteric illness are somewhat lower, about 0.2 (GI) and 0.035 (GII), in outbreaks. Se− subjects are protected, with substantially lower risks of infection (0.00007 and 0.015 at a dose of 1 GC of GI and GII virus, respectively). The present study shows there is considerable variability in risk of infection and especially risk of acute symptoms following infection with norovirus. These challenge and outbreak data consistently indicate high infectivity among secretor positives and protection in secretor negatives

Identifying the source of food-borne disease outbreaks: An application of Bayesian variable selection.

Early identification of contaminated food products is crucial in reducing health burdens of food-borne disease outbreaks. Analytic case-control studies are primarily used in this identification stage by comparing exposures in cases and controls using logistic regression. Standard epidemiological analysis practice is not formally defined and the combination of currently applied methods is subject to issues such as response misclassification, missing values, multiple testing problems and small sample estimation problems resulting in biased and possibly misleading results. In this paper, we develop a formal Bayesian variable selection method to account for misclassified responses and missing covariates, which are common complications in food-borne outbreak investigations. We illustrate the implementation and performance of our method on a Salmonella Thompson outbreak in the Netherlands in 2012. Our method is shown to perform better than the standard logistic regression approach with respect to earlier identification of contaminated food products. It also allows relatively easy implementation of otherwise complex methodological issues

Identifying the source of food-borne disease outbreaks: An application of Bayesian variable selection.

Early identification of contaminated food products is crucial in reducing health burdens of food-borne disease outbreaks. Analytic case-control studies are primarily used in this identification stage by comparing exposures in cases and controls using logistic regression. Standard epidemiological analysis practice is not formally defined and the combination of currently applied methods is subject to issues such as response misclassification, missing values, multiple testing problems and small sample estimation problems resulting in biased and possibly misleading results. In this paper, we develop a formal Bayesian variable selection method to account for misclassified responses and missing covariates, which are common complications in food-borne outbreak investigations. We illustrate the implementation and performance of our method on a Salmonella Thompson outbreak in the Netherlands in 2012. Our method is shown to perform better than the standard logistic regression approach with respect to earlier identification of contaminated food products. It also allows relatively easy implementation of otherwise complex methodological issues

Serological cross-sectional studies on salmonella incidence in eight European countries: no correlation with incidence of reported cases

<p>Abstract</p> <p>Background</p> <p>Published incidence rates of human salmonella infections are mostly based on numbers of stool culture-confirmed cases reported to public health surveillance. These cases constitute only a small fraction of all cases occurring in the community. The extent of underascertainment is influenced by health care seeking behaviour and sensitivity of surveillance systems, so that reported incidence rates from different countries are not comparable. We performed serological cross-sectional studies to compare infection risks in eight European countries independent of underascertainment.</p> <p>Methods</p> <p>A total of 6,393 sera from adults in Denmark, Finland, France, Italy, Poland, Romania, Sweden, and The Netherlands were analysed, mostly from existing serum banks collected in the years 2003 to 2008. Immunoglobulin A (IgA), IgM, and IgG against salmonella lipopolysaccharides were measured by in-house mixed ELISA. We converted antibody concentrations to estimates of infection incidence (‘sero-incidence’) using a Bayesian backcalculation model, based on previously studied antibody decay profiles in persons with culture-confirmed salmonella infections. We compared sero-incidence with incidence of cases reported through routine public health surveillance and with published incidence estimates derived from infection risks in Swedish travellers to those countries.</p> <p>Results</p> <p>Sero-incidence of salmonella infections ranged from 56 (95% credible interval 8–151) infections per 1,000 person-years in Finland to 547 (343–813) in Poland. Depending on country, sero-incidence was approximately 100 to 2,000 times higher than incidence of culture-confirmed cases reported through routine surveillance, with a trend for an inverse correlation. Sero-incidence was significantly correlated with incidence estimated from infection risks in Swedish travellers.</p> <p>Conclusions</p> <p>Sero-incidence estimation is a new method to estimate and compare the incidence of salmonella infections in human populations independent of surveillance artefacts. Our results confirm that comparison of reported incidence between countries can be grossly misleading, even within the European Union. Because sero-incidence includes asymptomatic infections, it is not a direct measure of burden of illness. But, pending further validation of this novel method, it may be a promising and cost-effective way to assess infection risks and to evaluate the effectiveness of salmonella control programmes across countries or over time.</p