Estimation of incidences of infectious diseases based on antibody measurements

Navigating cross-reactivity and host species effects in a serological assay: A case study of the microscopic agglutination test for Leptospira serology

BACKGROUND

Serology (the detection of antibodies formed by the host against an infecting pathogen) is frequently used to assess current infections and past exposure to specific pathogens. However, the presence of cross-reactivity among host antibodies in serological data makes it challenging to interpret the patterns and draw reliable conclusions about the infecting pathogen or strain.

METHODOLOGY/PRINCIPAL FINDINGS

In our study, we use microscopic agglutination test (MAT) serological data from three host species [California sea lion (Zalophus californianus), island fox (Urocyon littoralis), and island spotted skunk (Spilogale gracilis)] with confirmed infections to assess differences in cross-reactivity by host species and diagnostic laboratory. All host species are known to be infected with the same serovar of Leptospira interrogans. We find that absolute and relative antibody titer magnitudes vary systematically across host species and diagnostic laboratories. Despite being infected by the same Leptospira serovar, three host species exhibit different cross-reactivity profiles to a 5-serovar diagnostic panel. We also observe that the cross-reactive antibody titer against a non-infecting serovar can remain detectable after the antibody titer against the infecting serovar declines below detectable levels.

CONCLUSIONS/SIGNIFICANCE

Cross-reactivity in serological data makes interpretation difficult and can lead to common pitfalls. Our results show that the highest antibody titer is not a reliable indicator of infecting serovar and highlight an intriguing role of host species in shaping reactivity patterns. On the other side, seronegativity against a given serovar does not rule out that serovar as the cause of infection. We show that titer magnitudes can be influenced by both host species and diagnostic laboratory, indicating that efforts to interpret absolute titers (e.g., as indicators of recent infection) must be calibrated to the system under study. Thus, we implore scientists and health officials using serological data for surveillance to interpret the data with caution.

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.

Salmonella infection acts as an environmental risk factor for human colon cancer

Emerging evidence has demonstrated that perturbations of host-microbial interactions by pathogens can lead to an altered microenvironment that promotes tumorigenesis. A recent study provides new evidence and mechanisms on how repetitive exposure to non-Typhoidal Salmonella (NTS) increases the risk for colon cancer. This study integrated a serological and epidemiological approach with both in vivo and in vitro analyses, showed that the magnitude of exposure to NTS is associated with colonic tumorigenesis. In vivo exposure to repetitive low doses of NTS led to colonic tumors similar as a single high NTS dose in mice. Repetitive NTS infections significantly increase the proliferation of transformed cells in tissue cultures. The research results open new possibilities for the diagnosis, prevention, and treatment of colon cancer. The unanswered questions remain, including validation of the current findings in other cohorts, differences in lifestyle, and changes of gut microbiome after Salmonella infection. Salmonellae exposure can be limited by eating cooked meats and washing vegetables well. It is necessary to develop guidelines and criteria for screenings and follow-ups in people with exposure history to Salmonella and other cancer-associated pathogens.

serosim: An R package for simulating serological data arising from vaccination, epidemiological and antibody kinetics processes

serosim is an open-source R package designed to aid inference from serological studies, by simulating data arising from user-specified vaccine and antibody kinetics processes using a random effects model. Serological data are used to assess population immunity by directly measuring individuals' antibody titers. They uncover locations and/or populations which are susceptible and provide evidence of past infection or vaccination to help inform public health measures and surveillance. Both serological data and new analytical techniques used to interpret them are increasingly widespread. This creates a need for tools to simulate serological studies and the processes underlying observed titer values, as this will enable researchers to identify best practices for serological study design, and provide a standardized framework to evaluate the performance of different inference methods. serosim allows users to specify and adjust model inputs representing underlying processes responsible for generating the observed titer values like time-varying patterns of infection and vaccination, population demography, immunity and antibody kinetics, and serological sampling design in order to best represent the population and disease system(s) of interest. This package will be useful for planning sampling design of future serological studies, understanding determinants of observed serological data, and validating the accuracy and power of new statistical methods.

Seroepidemiology for Enteric Fever: Emerging Approaches and Opportunities

Safe and effective typhoid conjugate vaccines (TCVs) are available, but many countries lack the high-resolution data needed to prioritize TCV introduction to the highest-risk communities. Here we discuss seroepidemiology-an approach using antibody response data to characterize infection burden-as a potential tool to fill this data gap. Serologic tests for typhoid have existed for over a hundred years, but only recently were antigens identified that were sensitive and specific enough to use as epidemiologic markers. These antigens, coupled with new methodological developments, permit estimating seroincidence-the rate at which new infections occur in a population-from cross-sectional serosurveys. These new tools open up many possible applications for enteric fever seroepidemiology, including generating high-resolution surveillance data, monitoring vaccine impact, and integrating with other serosurveillance initiatives. Challenges remain, including distinguishing Salmonella Typhi from Salmonella Paratyphi infections and accounting for reinfections. Enteric fever seroepidemiology can be conducted at a fraction of the cost, time, and sample size of surveillance blood culture studies and may enable more efficient and scalable surveillance for this important infectious disease.

Repetitive non-typhoidal Salmonella exposure is an environmental risk factor for colon cancer and tumor growth

During infection, Salmonella hijacks essential host signaling pathways. These molecular manipulations disrupt cellular integrity and may induce oncogenic transformation. Systemic S. Typhi infections are linked to gallbladder cancer, whereas severe non-typhoidal Salmonella (NTS) infections are associated with colon cancer (CC). These diagnosed infections, however, represent only a small fraction of all NTS infections as many infections are mild and go unnoticed. To assess the overall impact of NTS infections, we performed a retrospective serological study on NTS exposure in patients with CC. The magnitude of exposure to NTS, as measured by serum antibody titer, is significantly positively associated with CC. Repetitively infecting mice with low NTS exposure showed similar accelerated tumor growth to that observed after high NTS exposure. At the cellular level, NTS preferably infects (pre-)transformed cells, and each infection round exponentially increases the rate of transformed cells. Thus, repetitive exposure to NTS associates with CC risk and accelerates tumor growth.

Building an integrated serosurveillance platform to inform public health interventions: Insights from an experts' meeting on serum biomarkers

The use of biomarkers to measure immune responses in serum is crucial for understanding population-level exposure and susceptibility to human pathogens. Advances in sample collection, multiplex testing, and computational modeling are transforming serosurveillance into a powerful tool for public health program design and response to infectious threats. In July 2018, 70 scientists from 16 countries met to perform a landscape analysis of approaches that support an integrated serosurveillance platform, including the consideration of issues for successful implementation. Here, we summarize the group's insights and proposed roadmap for implementation, including objectives, technical requirements, ethical issues, logistical considerations, and monitoring and evaluation.

Estimating typhoid incidence from community-based serosurveys: a multicohort study

BACKGROUND

The incidence of enteric fever, an invasive bacterial infection caused by typhoidal Salmonellae (Salmonella enterica serovars Typhi and Paratyphi), is largely unknown in regions without blood culture surveillance. The aim of this study was to evaluate whether new diagnostic serological markers for typhoidal Salmonella can reliably estimate population-level incidence.

METHODS

We collected longitudinal blood samples from patients with blood culture-confirmed enteric fever enrolled from surveillance studies in Bangladesh, Nepal, Pakistan, and Ghana between 2016 and 2021 and conducted cross-sectional serosurveys in the catchment areas of each surveillance site. We used ELISAs to measure quantitative IgA and IgG antibody responses to hemolysin E and S Typhi lipopolysaccharide. We used Bayesian hierarchical models to fit two-phase power-function decay models to the longitudinal antibody responses among enteric fever cases and used the joint distributions of the peak antibody titres and decay rate to estimate population-level incidence rates from cross-sectional serosurveys.

FINDINGS

The longitudinal antibody kinetics for all antigen-isotypes were similar across countries and did not vary by clinical severity. The seroincidence of typhoidal Salmonella infection among children younger than 5 years ranged between 58·5 per 100 person-years (95% CI 42·1-81·4) in Dhaka, Bangladesh, to 6·6 per 100 person-years (4·3-9·9) in Kavrepalanchok, Nepal, and followed the same rank order as clinical incidence estimates.

INTERPRETATION

The approach described here has the potential to expand the geographical scope of typhoidal Salmonella surveillance and generate incidence estimates that are comparable across geographical regions and time.

FUNDING

Bill & Melinda Gates Foundation.

TRANSLATIONS

For the Nepali, Bengali and Urdu translations of the abstract see Supplementary Materials section.

Spatial dynamics of inflammation-causing and commensal bacteria in the gastrointestinal tract

In recent years, new research programmes have been initiated to understand the role of gut bacteria in health and disease, enabled in large part by the emergence of high-throughput sequencing. As new genomic and other data emerge it will become important to explain observations in terms of underlying population mechanisms; for instance, it is of interest to understand how resident bacteria interact with their hosts and pathogens, and how they play a protective role. Connecting underlying processes with observed patterns is aided by the development of mathematical models. Here, we develop a spatial model of microbial populations in the gastrointestinal tract to explore conditions under which inflammation-causing bacteria can invade the gut and under which such pathogens become persistent. We find that pathogens invade both small and large intestine from even a relatively small inoculum size but are usually eliminated by the host response. When the immune response is weak, the pathogen is able to persist for a long period. Spatial structure affects these dynamics by creating moving refugia which facilitate bouts of pathogen resurgence and inflammation in persistent infections. Space also plays a role in repopulation by commensals after infection. We further find that the rate of decay of inflammation has a stronger effect on outcomes than the initiation of inflammation or other parameters. Finally, we explore the impact of partially inflammation-resistant commensals on these dynamics.

Trend in sensitivity of SARS-CoV-2 serology one year after mild and asymptomatic COVID-19: unpacking potential bias in seroprevalence studies

A key aim of serosurveillance during the coronavirus disease 2019 (COVID-19) pandemic has been to estimate the prevalence of prior infection, by correcting crude seroprevalence against estimated test performance for polymerase chain reaction (PCR)-confirmed COVID-19. We show that poor generalizability of sensitivity estimates to some target populations may lead to substantial underestimation of case numbers.

Predicting the trend of infectious diseases using grey self-memory system model: a case study of the incidence of tuberculosis

OBJECTIVES

The prediction and early warning of infectious diseases is an important work in the field of public health. This study constructed the grey self-memory system model to predict the incidence trend of infectious diseases affected by many uncertain factors.

STUDY DESIGN

The design of this study is a combination of the prediction method and empirical analysis.

METHODS

By organically coupling the self-memory algorithm with the mean GM(1,1) model, the tuberculosis incidence statistics of China from 2004 to 2018 were selected for prediction analysis. Meanwhile, by comparing with the other traditional prediction methods, three representative accuracy check indexes (MSE, AME, MAPE) were conducting for error analysis.

RESULTS

Owing to the multiple time-points initial fields, which replace the single time-points, the limitation of the traditional grey prediction model, which is sensitive to the initial value, is overcome in the self-memory equation. Consequently, compared with the mean GM model and other statistical methods, the grey self-memory model shows significant forecasting advantages, and its single-step rolling prediction accuracy is superior to other prediction methods. Therefore, the incidence of tuberculosis in China in the next year can be predicted as 55.30 (unit: 1/10⁵).

CONCLUSIONS

The grey self-memory system model can closely capture the individual random fluctuation in the whole evolution trend of the uncertain system. It is appropriate for predicting the future incidence trend of infectious diseases and is worth popularizing to other similar public health prediction problems.

Estimating epidemiologic dynamics from cross-sectional viral load distributions

Estimating an epidemic's trajectory is crucial for developing public health responses to infectious diseases, but case data used for such estimation are confounded by variable testing practices. We show that the population distribution of viral loads observed under random or symptom-based surveillance-in the form of cycle threshold (Ct) values obtained from reverse transcription quantitative polymerase chain reaction testing-changes during an epidemic. Thus, Ct values from even limited numbers of random samples can provide improved estimates of an epidemic's trajectory. Combining data from multiple such samples improves the precision and robustness of this estimation. We apply our methods to Ct values from surveillance conducted during the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic in a variety of settings and offer alternative approaches for real-time estimates of epidemic trajectories for outbreak management and response.

Establishing causality in Salmonella-microbiota-host interaction: The use of gnotobiotic mouse models and synthetic microbial communities

Colonization resistance (CR), the ability to block infections by potentially harmful microbes, is a fundamental function of host-associated microbial communities and highly conserved between animals and humans. Environmental factors such as antibiotics and diet can disturb microbial community composition and thereby predispose to opportunistic infections. The most prominent is Clostridioides difficile, the causative agent of diarrhea and pseudomembranous colitis. In addition, the risk to succumb to infections with genuine human enteric pathogens like nontyphoidal Salmonella (NTS) is also increased by a low-diverse, diet or antibiotic-disrupted microbiota. Despite extensive microbial community profiling efforts, only a limited set of microorganisms have been causally linked with protection against enteric pathogens. Furthermore, it remains a challenge to predict colonization resistance from complex microbiome signatures due to context-dependent action of microorganisms. In the past decade, the study of NTS infection has led to the description of several fundamental principles of microbiota-host-pathogen interaction. In this review, I will give an overview on the current state of knowledge in this field and outline experimental approaches to gain functional insight to the role of specific microbes, functions and metabolites in Salmonella-microbiota-host interaction. In particular, I will highlight the value of mouse infection models, which, in combination with culture collections, synthetic communities and gnotobiotic models have become essential tools to screen for protective members of the microbiota and establishing causal relationship and mechanisms in infection research.

Estimating epidemiologic dynamics from cross-sectional viral load distributions

Estimating an epidemic's trajectory is crucial for developing public health responses to infectious diseases, but incidence data used for such estimation are confounded by variable testing practices. We show instead that the population distribution of viral loads observed under random or symptom-based surveillance, in the form of cycle threshold (Ct) values, changes during an epidemic and that Ct values from even limited numbers of random samples can provide improved estimates of an epidemic's trajectory. Combining multiple such samples and the fraction positive improves the precision and robustness of such estimation. We apply our methods to Ct values from surveillance conducted during the SARS-CoV-2 pandemic in a variety of settings and demonstrate new approaches for real-time estimates of epidemic trajectories for outbreak management and response.

How Food Affects Colonization Resistance Against Enteropathogenic Bacteria

Food has a major impact on all aspects of health. Recent data suggest that food composition can also affect susceptibility to infections by enteropathogenic bacteria. Here, we discuss how food may alter the microbiota as well as mucosal defenses and how this can affect infection. Salmonella Typhimurium diarrhea serves as a paradigm, and complementary evidence comes from other pathogens. We discuss the effects of food composition on colonization resistance, host defenses, and the infection process as well as the merits and limitations of mouse models and experimental foods, which are available to decipher the underlying mechanisms.

Estimation of seroconversion rates for infectious diseases: Effects of age and noise

The presence of serum antibodies is a biomarker of past infection. Instead of seroclassification aimed at measuring seroprevalence a population sample of serum antibody levels may be used to estimate the incidence of seroconversion. This article expands an earlier study into seroincidence estimation, employing models of the seroresponse that include probability of escaping infection, as well as nonexponential decay kinetics and different sources of noise. As previously, a constant force of infection is assumed. When the seroconversion rate is low, a substantial fraction of the population may not be old enough to have experienced any seroconversions, causing underestimation of seroconversion rates that may be substantial at young ages. A correction is given that can be shown to remove such age dependent bias. Simulation studies show that the updated models provide accurate estimates of seroconversion rates, but also that the presence of noise, when unaccounted for, may introduce considerable bias, especially at low (< 0.1/yr) seroconversion rates and young ages. The revised serocalculator scripts can be used to update the R package "seroincidence."

Import of Aspartate and Malate by DcuABC Drives H2/Fumarate Respiration to Promote Initial Salmonella Gut-Lumen Colonization in Mice

Initial enteropathogen growth in the microbiota-colonized gut is poorly understood. Salmonella Typhimurium is metabolically adaptable and can harvest energy by anaerobic respiration using microbiota-derived hydrogen (H2) as an electron donor and fumarate as an electron acceptor. As fumarate is scarce in the gut, the source of this electron acceptor is unclear. Here, transposon sequencing analysis along the colonization trajectory of S. Typhimurium implicates the C4-dicarboxylate antiporter DcuABC in early murine gut colonization. In competitive colonization assays, DcuABC and enzymes that convert the C4-dicarboxylates aspartate and malate into fumarate (AspA, FumABC), are required for fumarate/H2-dependent initial growth. Thus, S. Typhimurium obtains fumarate by DcuABC-mediated import and conversion of L-malate and L-aspartate. Fumarate reduction yields succinate, which is exported by DcuABC in exchange for L-aspartate and L-malate. This cycle allows S. Typhimurium to harvest energy by H2/fumarate respiration in the microbiota-colonized gut. This strategy may also be relevant for commensal E. coli diminishing the S. Typhimurium infection.

Linking longitudinal and cross-sectional biomarker data to understand host-pathogen dynamics: Leptospira in California sea lions (Zalophus californianus) as a case study

Confronted with the challenge of understanding population-level processes, disease ecologists and epidemiologists often simplify quantitative data into distinct physiological states (e.g. susceptible, exposed, infected, recovered). However, data defining these states often fall along a spectrum rather than into clear categories. Hence, the host-pathogen relationship is more accurately defined using quantitative data, often integrating multiple diagnostic measures, just as clinicians do to assess their patients. We use quantitative data on a major neglected tropical disease (Leptospira interrogans) in California sea lions (Zalophus californianus) to improve individual-level and population-level understanding of this Leptospira reservoir system. We create a "host-pathogen space" by mapping multiple biomarkers of infection (e.g. serum antibodies, pathogen DNA) and disease state (e.g. serum chemistry values) from 13 longitudinally sampled, severely ill individuals to characterize changes in these values through time. Data from these individuals describe a clear, unidirectional trajectory of disease and recovery within this host-pathogen space. Remarkably, this trajectory also captures the broad patterns in larger cross-sectional datasets of 1456 wild sea lions in all states of health but sampled only once. Our framework enables us to determine an individual's location in their time-course since initial infection, and to visualize the full range of clinical states and antibody responses induced by pathogen exposure. We identify predictive relationships between biomarkers and outcomes such as survival and pathogen shedding, and use these to impute values for missing data, thus increasing the size of the useable dataset. Mapping the host-pathogen space using quantitative biomarker data enables more nuanced understanding of an individual's time course of infection, duration of immunity, and probability of being infectious. Such maps also make efficient use of limited data for rare or poorly understood diseases, by providing a means to rapidly assess the range and extent of potential clinical and immunological profiles. These approaches yield benefits for clinicians needing to triage patients, prevent transmission, and assess immunity, and for disease ecologists or epidemiologists working to develop appropriate risk management strategies to reduce transmission risk on a population scale (e.g. model parameterization using more accurate estimates of duration of immunity and infectiousness) and to assess health impacts on a population scale.

Individual-Level Antibody Dynamics Reveal Potential Drivers of Influenza A Seasonality in Wild Pig Populations

Swine are important in the ecology of influenza A virus (IAV) globally. Understanding the ecological role of wild pigs in IAV ecology has been limited because surveillance in wild pigs is often for antibodies (serosurveillance) rather than IAVs, as in humans and domestic swine. As IAV antibodies can persist long after an infection, serosurveillance data are not necessarily indicative of current infection risk. However, antibody responses to IAV infections cause a predictable antibody response, thus time of infection can be inferred from antibody levels in serological samples, enabling identification of risk factors of infection at estimated times of infection. Recent work demonstrates that these quantitative antibody methods (QAMs) can accurately recover infection dates, even when individual-level variation in antibody curves is moderately high. Also, the methodology can be implemented in a survival analysis (SA) framework to reduce bias from opportunistic sampling. Here we integrated QAMs and SA and applied this novel QAM-SA framework to understand the dynamics of IAV infection risk in wild pigs seasonally and spatially, and identify risk factors. We used national-scale IAV serosurveillance data from 15 US states. We found that infection risk was highest during January-March (54% of 61 estimated peaks), with 24% of estimated peaks occurring from May to July, and some low-level of infection risk occurring year-round. Time-varying IAV infection risk in wild pigs was positively correlated with humidity and IAV infection trends in domestic swine and humans, and did not show wave-like spatial spread of infection among states, nor more similar levels of infection risk among states with more similar meteorological conditions. Effects of host sex on IAV infection risk in wild pigs were generally not significant. Because most of the variation in infection risk was explained by state-level factors or infection risk at long-distances, our results suggested that predicting IAV infection risk in wild pigs is complicated by local ecological factors and potentially long-distance translocation of infection. In addition to revealing factors of IAV infection risk in wild pigs, our framework is broadly applicable for quantifying risk factors of disease transmission using opportunistic serosurveillance sampling, a common methodology in wildlife disease surveillance. Future research on the factors that determine individual-level antibody kinetics will facilitate the design of serosurveillance systems that can extract more accurate estimates of time-varying disease risk from quantitative antibody data.

Inferring seasonal infection risk at population and regional scales from serology samples

Accurate estimates of seasonal infection risk can be used by animal health officials to predict future disease risk and improve understanding of the mechanisms driving disease dynamics. It can be difficult to estimate seasonal infection risk in wildlife disease systems because surveillance assays typically target antibodies (serosurveillance), which are not necessarily indicative of current infection, and serosurveillance sampling is often opportunistic. Recently developed methods estimate past time of infection from serosurveillance data using quantitative serological assays that indicate the amount of antibodies in a serology sample. However, current methods do not account for common opportunistic and uneven sampling associated with serosurveillance data. We extended the framework of survival analysis to improve estimates of seasonal infection risk from serosurveillance data across population and regional scales. We found that accounting for the right-censored nature of quantitative serology samples greatly improved estimates of seasonal infection risk, even when sampling was uneven in time. Survival analysis can also be used to account for common challenges when estimating infection risk from serology data, such as biases induced by host demography and continually elevated antibodies following infection. The framework developed herein is widely applicable for estimating seasonal infection risk from serosurveillance data in humans, wildlife, and livestock.

Enteropathogen antibody dynamics and force of infection among children in low-resource settings

Little is known about enteropathogen seroepidemiology among children in low-resource settings. We measured serological IgG responses to eight enteropathogens (Giardia intestinalis, Cryptosporidium parvum, Entamoeba histolytica, Salmonella enterica, enterotoxigenic Escherichia coli, Vibrio cholerae, Campylobacter jejuni, norovirus) in cohorts from Haiti, Kenya, and Tanzania. We studied antibody dynamics and force of infection across pathogens and cohorts. Enteropathogens shared common seroepidemiologic features that enabled between-pathogen comparisons of transmission. Overall, exposure was intense: for most pathogens the window of primary infection was <3 years old; for highest transmission pathogens primary infection occurred within the first year. Longitudinal profiles demonstrated significant IgG boosting and waning above seropositivity cutoffs, underscoring the value of longitudinal designs to estimate force of infection. Seroprevalence and force of infection were rank-preserving across pathogens, illustrating the measures provide similar information about transmission heterogeneity. Our findings suggest antibody response can be used to measure population-level transmission of diverse enteropathogens in serologic surveillance.

Diarrhea, which is caused by bacteria such as Salmonella or by viruses like norovirus, is the fourth leading cause of death among children worldwide, with children in low-resource settings being at highest risk. The pathogens that cause diarrhea spread when stool from infected people comes into contact with new hosts, for example, through inadequate sanitation or by drinking contaminated water. Currently, the best way to track these infections is to collect stool samples from people and test them for the presence of the pathogens. Unfortunately, this is costly and difficult to do on a large scale outside of clinical settings, making it hard to track the spread of diarrhea-causing pathogens.

The body produces antibodies – small proteins that can detect specific pathogens – in response to an infection. These antibodies help ward off future infections by the same pathogen, so if they are present in the blood, this indicates a current or previous infection. Scientists already collect blood samples to track malaria, HIV and vaccine-preventable diseases in low-resource settings. These samples could be tested more broadly to measure the levels of antibodies against diarrhea-causing pathogens.

Now, Arnold et al. have used blood samples collected from children in Haiti, Kenya, and Tanzania to measure antibody responses to 8 diarrhea-causing pathogens. The results showed that many children in these settings had been infected with all 8 pathogens before age three, and that all of the pathogens shared similar age-dependent patterns of antibody response. This finding enabled Arnold et al. to combine antibody measurements with statistical models to estimate each pathogen’s force of infection, that is, the rate at which susceptible individuals in the population become infected. This is a key step for epidemiologists to understand which pathogens cause the most infections in a population.

The experiments show that testing blood samples for antibodies could provide scientists with a new tool to track the transmission of diarrhea-causing pathogens in low-resource settings. This information could help public health officials design and test efforts to prevent diarrhea, for example, by improving water treatment or developing vaccines.

Visitors to a Tropical Marine Beach Show Evidence of Immunoconversions to Multiple Waterborne Pathogens

Determining infections from environmental exposures, particularly from waterborne pathogens is a challenging proposition. The study design must be rigorous and account for numerous factors including study population selection, sample collection, storage, and processing, as well as data processing and analysis. These challenges are magnified when it is suspected that individuals may potentially be infected by multiple pathogens at the same time. Previous work demonstrated the effectiveness of a salivary antibody multiplex immunoassay in detecting the prevalence of immunoglobulin G (IgG) antibodies to multiple waterborne pathogens and helped identify asymptomatic norovirus infections in visitors to Boquerón Beach, Puerto Rico. In this study, we applied the immunoassay to three serially collected samples from study participants within the same population to assess immunoconversions (incident infections) to six waterborne pathogens: Helicobacter pylori, Campylobacter jejuni, Toxoplasma gondii, hepatitis A virus, and noroviruses GI. I and GII.4. Further, we examined the impact of sampling on the detection of immunoconversions by comparing the traditional immunoconversion definition based on two samples to criteria developed to capture trends in three sequential samples collected from study participants. The expansion to three samples makes it possible to capture the IgG antibody responses within the survey population to more accurately assess the frequency of immunoconversions to target pathogens. Based on the criteria developed, results showed that when only two samples from each participant were used in the analysis, 25.9% of the beachgoers immunoconverted to at least one pathogen; however, the addition of the third sample reduced immunoconversions to 6.5%. Of these incident infections, the highest levels were to noroviruses followed by T. gondii. Moreover, many individuals displayed evidence of immunoconversions to multiple pathogens. This study suggests that detection of simultaneous infections is possible, with far reaching consequences for the population. The results may lead to further studies to understand the complex interactions that occur within the body as the immune system attempts to ward off these infections. Such an approach is critical to our understanding of medically important synergistic or antagonistic interactions and may provide valuable and critical information to public health officials, water treatment personnel, and environmental managers.

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.

Immune response-eliciting exposure to Campylobacter vastly exceeds the incidence of clinically overt campylobacteriosis but is associated with similar risk factors: A nationwide serosurvey in the Netherlands

BACKGROUND

We aimed to estimate population-level exposure to Campylobacter and associated risk factors, using three approaches for serological data analysis.

METHODS

Nationwide, population-based serosurvey in the Netherlands (Feb 2006-Jun 2007). Anti-Campylobacter IgG, IgM and IgA were measured using ELISA, and analysed via: a) seroincidence estimation, using reference values of antibody peak levels and decay rates over-time after Campylobacter exposure; b) two normal distributions of true positives/negatives fitted to the IgG distribution to derive seroprevalence and individual probability of being positive/negative; and c) IgG levels. Risk factors were analysed using multiple linear regressions.

RESULTS

From 1559 respondents, seroincidence was estimated at 1.61 infections/person-year (95%CI:1.58-1.64) and seroprevalence at 68.1% (65.4-70.9). The three approaches identified similar risk factors, although seroincidence had higher power and results were interpretable as risk: seroincidence was higher in females [exp(b) = 1.07(1.04-1.11)], older ages [vs. 15-34 years; for < 5, 5-14, 35-54 and 55-70 years: 0.60(0.58-0.63), 0.74(0.71-0.78), 1.08(1.03-1.13) and 1.08(1.01-1.16), respectively], non-Dutch background [Moroccan/Turkish: 1.25(1.14-1.37); Caribbean: 1.14(1.03-1.25)], low socioeconomic status [1.05(1.01-1.10)], traveling outside Europe [1.05(1.01-1.09)], and eating undercooked meat [1.04(1.01-1.08)].

CONCLUSION

Campylobacter exposure is much higher than clinical infection rates, but risk factors are similar to those previously described.Seroincidence is a powerful measure to study Campylobacter epidemiology, and is preferred over other methods.

Structure of general-population antibody titer distributions to influenza A virus

Seroepidemiological studies aim to understand population-level exposure and immunity to infectious diseases. Their results are normally presented as binary outcomes describing the presence or absence of pathogen-specific antibody, despite the fact that many assays measure continuous quantities. A population's natural distribution of antibody titers to an endemic infectious disease may include information on multiple serological states - naiveté, recent infection, non-recent infection, childhood infection - depending on the disease in question and the acquisition and waning patterns of immunity. In this study, we investigate 20,152 general-population serum samples from southern Vietnam collected between 2009 and 2013 from which we report antibody titers to the influenza virus HA1 protein using a continuous titer measurement from a protein microarray assay. We describe the distributions of antibody titers to subtypes 2009 H1N1 and H3N2. Using a model selection approach to fit mixture distributions, we show that 2009 H1N1 antibody titers fall into four titer subgroups and that H3N2 titers fall into three subgroups. For H1N1, our interpretation is that the two highest-titer subgroups correspond to recent and historical infection, which is consistent with 2009 pandemic attack rates. Similar interpretations are available for H3N2, but right-censoring of titers makes these interpretations difficult to validate.

Development of an LPS-based ELISA for diagnosis of Yersinia enterocolitica O:3 infections in Danish patients: a follow-up study

BACKGROUND

The bacterium Yersinia enterocolitica causes gastroenteritis in humans. The study aimed to develop a diagnostic enzyme-linked immunosorbent assay (ELISA) for detection of Yersinia enterocolitica O:3 LPS antibodies in sera from Danish patients with suspected Yersinia enterocolitica O:3 gastrointestinal infection. As a part of this, antibody decay profiles after culture confirmed Yersinia enteritis were studied.

RESULTS

An ELISA using Yersinia enterocolitica O:3 LPS as the coating antigen was developed for measuring IgA, IgG and IgM specific antibodies. A longitudinal collection of 220 sera drawn between 20 and 1053 days after onset of symptoms from 85 adult Danish patients with verified Yersinia enteritis were examined. A control group of 100 sera from healthy Danish blood-donors were analysed in order to determine the cut-off for interpretation of results. Serum samples from 62 out of 81 patients who delivered either the first or the second sample were found positive for specific antibodies against Yersinia enterocolitica O:3 LPS (77%). For samples collected within 60 days after onset of symptoms (n = 48) sensitivities of 58%, 42% and 79% for IgA, IgG and IgM antibodies were found. A sensitivity of 81% was found for these samples when using the definition of a positive result in either IgA, IgG or IgM as a combined positive. All samples received up to 36 days after onset of symptoms (n = 10) were found to be positive using this definition. For the period 61 to 90 days after onset of symptoms (n = 32), a combined sensitivity of 63% was found. The antibody levels as well as decay profiles for the three different immunoglobulin classes for the individual patients exhibited a large degree of variation.

CONCLUSIONS

Using a definition of positive as a positive result for either IgA, IgG or IgM antibodies, a diagnostic sensitivity of 81% was achieved for samples received within 60 days after onset of symptoms. In particular, the levels of specific IgM antibodies were elevated. In comparison, the standard tube-agglutination assay achieved a sensitivity of 60% on the same samples. The sensitivity of the ELISA decreased the longer the duration of time since onset of symptoms. The ELISA was highly specific for Yersinia when testing sera from individuals with confirmed gastrointestinal infections by other bacteria. Moreover, the knowledge gained from this longitudinal study of antibody decay profiles can be used in future epidemiological studies of seroprevalence.

Measuring changes in transmission of neglected tropical diseases, malaria, and enteric pathogens from quantitative antibody levels

BACKGROUND

Serological antibody levels are a sensitive marker of pathogen exposure, and advances in multiplex assays have created enormous potential for large-scale, integrated infectious disease surveillance. Most methods to analyze antibody measurements reduce quantitative antibody levels to seropositive and seronegative groups, but this can be difficult for many pathogens and may provide lower resolution information than quantitative levels. Analysis methods have predominantly maintained a single disease focus, yet integrated surveillance platforms would benefit from methodologies that work across diverse pathogens included in multiplex assays.

METHODS/PRINCIPAL FINDINGS

We developed an approach to measure changes in transmission from quantitative antibody levels that can be applied to diverse pathogens of global importance. We compared age-dependent immunoglobulin G curves in repeated cross-sectional surveys between populations with differences in transmission for multiple pathogens, including: lymphatic filariasis (Wuchereria bancrofti) measured before and after mass drug administration on Mauke, Cook Islands, malaria (Plasmodium falciparum) before and after a combined insecticide and mass drug administration intervention in the Garki project, Nigeria, and enteric protozoans (Cryptosporidium parvum, Giardia intestinalis, Entamoeba histolytica), bacteria (enterotoxigenic Escherichia coli, Salmonella spp.), and viruses (norovirus groups I and II) in children living in Haiti and the USA. Age-dependent antibody curves fit with ensemble machine learning followed a characteristic shape across pathogens that aligned with predictions from basic mechanisms of humoral immunity. Differences in pathogen transmission led to shifts in fitted antibody curves that were remarkably consistent across pathogens, assays, and populations. Mean antibody levels correlated strongly with traditional measures of transmission intensity, such as the entomological inoculation rate for P. falciparum (Spearman's rho = 0.75). In both high- and low transmission settings, mean antibody curves revealed changes in population mean antibody levels that were masked by seroprevalence measures because changes took place above or below the seropositivity cutoff.

CONCLUSIONS/SIGNIFICANCE

Age-dependent antibody curves and summary means provided a robust and sensitive measure of changes in transmission, with greatest sensitivity among young children. The method generalizes to pathogens that can be measured in high-throughput, multiplex serological assays, and scales to surveillance activities that require high spatiotemporal resolution. Our results suggest quantitative antibody levels will be particularly useful to measure differences in exposure for pathogens that elicit a transient antibody response or for monitoring populations with very high- or very low transmission, when seroprevalence is less informative. The approach represents a new opportunity to conduct integrated serological surveillance for neglected tropical diseases, malaria, and other infectious diseases with well-defined antigen targets.

Salmonella Typhimurium Diarrhea Reveals Basic Principles of Enteropathogen Infection and Disease-Promoted DNA Exchange

Despite decades of research, efficient therapies for most enteropathogenic bacteria are still lacking. In this review, we focus on Salmonella enterica Typhimurium (S. Typhimurium), a frequent cause of acute, self-limiting food-borne diarrhea and a model that has revealed key principles of enteropathogen infection. We review the steps of gut infection and the mucosal innate-immune defenses limiting pathogen burdens, and we discuss how inflammation boosts gut luminal S. Typhimurium growth. We also discuss how S. Typhimurium-induced inflammation accelerates the transfer of plasmids and phages, which may promote the transmission of antibiotic resistance and facilitate emergence of pathobionts and pathogens with enhanced virulence. The targeted manipulation of the microbiota and vaccination might offer strategies to prevent this evolution. As gut luminal microbes impact various aspects of the host's physiology, improved strategies for preventing enteropathogen infection and disease-inflicted DNA exchange may be of broad interest well beyond the acute infection.

Inferring infection hazard in wildlife populations by linking data across individual and population scales

Our ability to infer unobservable disease-dynamic processes such as force of infection (infection hazard for susceptible hosts) has transformed our understanding of disease transmission mechanisms and capacity to predict disease dynamics. Conventional methods for inferring FOI estimate a time-averaged value and are based on population-level processes. Because many pathogens exhibit epidemic cycling and FOI is the result of processes acting across the scales of individuals and populations, a flexible framework that extends to epidemic dynamics and links within-host processes to FOI is needed. Specifically, within-host antibody kinetics in wildlife hosts can be short-lived and produce patterns that are repeatable across individuals, suggesting individual-level antibody concentrations could be used to infer time since infection and hence FOI. Using simulations and case studies (influenza A in lesser snow geese and Yersinia pestis in coyotes), we argue that with careful experimental and surveillance design, the population-level FOI signal can be recovered from individual-level antibody kinetics, despite substantial individual-level variation. In addition to improving inference, the cross-scale quantitative antibody approach we describe can reveal insights into drivers of individual-based variation in disease response, and the role of poorly understood processes such as secondary infections, in population-level dynamics of disease.

Epidemiological and serological investigation of a waterborneCampylobacter jejunioutbreak in a Danish town

Following an unusually heavy rainfall in June 2009, a community-wide outbreak ofCampylobactergastroenteritis occurred in a small Danish town. The outbreak investigation consisted of (1) a cohort study using an e-questionnaire of disease determinants, (2) microbiological study of stool samples, (3) serological study of blood samples from cases and asymptomatic members of case households, and (4) environmental analyses of the water distribution system. The questionnaire study identified 163 cases (respondent attack rate 16%). Results showed a significant dose-response relationship between consumption of tap water and risk of gastroenteritis.Campylobacter jejunibelonging to two relatedflaAtypes were isolated from stool samples. Serum antibody levels againstCampylobacterwere significantly higher in cases than in asymptomatic persons. Water samples were positive for coliform bacteria, and the likely mode of contamination was found to be surface water leaking into the drinking-water system. This geographically constrained outbreak presented an ideal opportunity to study the serological response in persons involved in aCampylobacteroutbreak. The serology indicated that asymptomatic persons from the same household may have been exposed, during the outbreak period, toCampylobacterat doses that did not elicit symptoms or alternatively had been exposed toCampylobacterat a time prior to the outbreak, resulting in residual immunity and thus absence of clinical signs.

Using Combined Diagnostic Test Results to Hindcast Trends of Infection from Cross-Sectional Data

Infectious disease surveillance is key to limiting the consequences from infectious pathogens and maintaining animal and public health. Following the detection of a disease outbreak, a response in proportion to the severity of the outbreak is required. It is thus critical to obtain accurate information concerning the origin of the outbreak and its forward trajectory. However, there is often a lack of situational awareness that may lead to over- or under-reaction. There is a widening range of tests available for detecting pathogens, with typically different temporal characteristics, e.g. in terms of when peak test response occurs relative to time of exposure. We have developed a statistical framework that combines response level data from multiple diagnostic tests and is able to 'hindcast' (infer the historical trend of) an infectious disease epidemic. Assuming diagnostic test data from a cross-sectional sample of individuals infected with a pathogen during an outbreak, we use a Bayesian Markov Chain Monte Carlo (MCMC) approach to estimate time of exposure, and the overall epidemic trend in the population prior to the time of sampling. We evaluate the performance of this statistical framework on simulated data from epidemic trend curves and show that we can recover the parameter values of those trends. We also apply the framework to epidemic trend curves taken from two historical outbreaks: a bluetongue outbreak in cattle, and a whooping cough outbreak in humans. Together, these results show that hindcasting can estimate the time since infection for individuals and provide accurate estimates of epidemic trends, and can be used to distinguish whether an outbreak is increasing or past its peak. We conclude that if temporal characteristics of diagnostics are known, it is possible to recover epidemic trends of both human and animal pathogens from cross-sectional data collected at a single point in time.

Estimating Time of Infection Using Prior Serological and Individual Information Can Greatly Improve Incidence Estimation of Human and Wildlife Infections

Diseases of humans and wildlife are typically tracked and studied through incidence, the number of new infections per time unit. Estimating incidence is not without difficulties, as asymptomatic infections, low sampling intervals and low sample sizes can introduce large estimation errors. After infection, biomarkers such as antibodies or pathogens often change predictably over time, and this temporal pattern can contain information about the time since infection that could improve incidence estimation. Antibody level and avidity have been used to estimate time since infection and to recreate incidence, but the errors on these estimates using currently existing methods are generally large. Using a semi-parametric model in a Bayesian framework, we introduce a method that allows the use of multiple sources of information (such as antibody level, pathogen presence in different organs, individual age, season) for estimating individual time since infection. When sufficient background data are available, this method can greatly improve incidence estimation, which we show using arenavirus infection in multimammate mice as a test case. The method performs well, especially compared to the situation in which seroconversion events between sampling sessions are the main data source. The possibility to implement several sources of information allows the use of data that are in many cases already available, which means that existing incidence data can be improved without the need for additional sampling efforts or laboratory assays.

Was the increase in culture-confirmed Campylobacter infections in Denmark during the 1990s a surveillance artefact?

In 1991, 1999 and 2006, randomly selected individuals from the Danish Central Personal Register provided a serum sample. From individuals aged 30 years and above, 500 samples from each year were analysed for Campylobacter IgG, IgA and IgM antibodies using a direct ELISA method. We applied a seroincidence calculator available from the European Centre for Disease Prevention and Control to perform a mathematical back-calculation to estimate the annual Campylobacter seroincidence in the Danish population. The estimated Campylobacter seroincidence did not differ significantly between the 1991, 1999 and 2006 studies although the reported number of culture-confirmed cases of Campylobacter infection increased 2.5 fold from 1993 to 1999 among individuals aged 30 years and above. This suggests that Campylobacter was widely present in the Danish population before the increase in poultry-associated clinical Campylobacter infections observed from 1993 to 2001 among individuals of this age groups.

Novel serologic biomarkers provide accurate estimates of recent Plasmodium falciparum exposure for individuals and communities

Tools to reliably measure Plasmodium falciparum (Pf) exposure in individuals and communities are needed to guide and evaluate malaria control interventions. Serologic assays can potentially produce precise exposure estimates at low cost; however, current approaches based on responses to a few characterized antigens are not designed to estimate exposure in individuals. Pf-specific antibody responses differ by antigen, suggesting that selection of antigens with defined kinetic profiles will improve estimates of Pf exposure. To identify novel serologic biomarkers of malaria exposure, we evaluated responses to 856 Pf antigens by protein microarray in 186 Ugandan children, for whom detailed Pf exposure data were available. Using data-adaptive statistical methods, we identified combinations of antibody responses that maximized information on an individual's recent exposure. Responses to three novel Pf antigens accurately classified whether an individual had been infected within the last 30, 90, or 365 d (cross-validated area under the curve = 0.86-0.93), whereas responses to six antigens accurately estimated an individual's malaria incidence in the prior year. Cross-validated incidence predictions for individuals in different communities provided accurate stratification of exposure between populations and suggest that precise estimates of community exposure can be obtained from sampling a small subset of that community. In addition, serologic incidence predictions from cross-sectional samples characterized heterogeneity within a community similarly to 1 y of continuous passive surveillance. Development of simple ELISA-based assays derived from the successful selection strategy outlined here offers the potential to generate rich epidemiologic surveillance data that will be widely accessible to malaria control programs.

Seroincidence of non-typhoid Salmonella infections: convenience vs. random community-based sampling

The incidence of reported infections of non-typhoid Salmonella is affected by biases inherent to passive laboratory surveillance, whereas analysis of blood sera may provide a less biased alternative to estimate the force of Salmonella transmission in humans. We developed a mathematical model that enabled a back-calculation of the annual seroincidence of Salmonella based on measurements of specific antibodies. The aim of the present study was to determine the seroincidence in two convenience samples from 2012 (Danish blood donors, n = 500, and pregnant women, n = 637) and a community-based sample of healthy individuals from 2006 to 2007 (n = 1780). The lowest antibody levels were measured in the samples from the community cohort and the highest in pregnant women. The annual Salmonella seroincidences were 319 infections/1000 pregnant women [90% credibility interval (CrI) 210-441], 182/1000 in blood donors (90% CrI 85-298) and 77/1000 in the community cohort (90% CrI 45-114). Although the differences between study populations decreased when accounting for different age distributions the estimates depend on the study population. It is important to be aware of this issue and define a certain population under surveillance in order to obtain consistent results in an application of serological measures for public health purposes.

A two-phase within-host model for immune response and its application to serological profiles of pertussis

We present a simple phenomenological within-host model describing both the interaction between a pathogen and the immune system and the waning of immunity after clearing of the pathogen. We implement the model into a Bayesian hierarchical framework to estimate its parameters for pertussis using Markov chain Monte Carlo methods. We show that the model captures some essential features of the kinetics of titers of IgG against pertussis toxin. We identify a threshold antibody level that separates a large increase in antibody level upon infection from a small increase and accordingly might be interpreted as a threshold separating clinical from subclinical infections. We contrast predictions of the model with observations reported in the literature and based on independent data and find a remarkable correspondence.

Seroincidence of human infections with nontyphoid Salmonella compared with data from public health surveillance and food animals in 13 European countries

We developed a model that enabled a back-calculation of the annual salmonellosis seroincidence from measurements of Salmonella antibodies and applied this model to 9677 serum samples collected from populations in 13 European countries. We found a 10-fold difference in the seroincidence, which was lowest in Sweden (0.06 infections per person-year), Finland (0.07), and Denmark (0.08) and highest in Spain (0.61), followed by Poland (0.55). These numbers were not correlated with the reported national incidence of Salmonella infections in humans but were correlated with prevalence data of Salmonella in laying hens (P < .001), broilers (P < .001), and slaughter pigs (P = .03). Seroincidence also correlated with Swedish data on the country-specific risk of travel-associated Salmonella infections (P = .001). Estimates based on seroepidemiological methods are well suited to measure the force of transmission of Salmonella to human populations, in particular relevant for assessments where data include notifications from areas, states or countries with diverse characteristics of the Salmonella surveillance.

Measuring underreporting and under-ascertainment in infectious disease datasets: a comparison of methods

BACKGROUND

Efficient and reliable surveillance and notification systems are vital for monitoring public health and disease outbreaks. However, most surveillance and notification systems are affected by a degree of underestimation (UE) and therefore uncertainty surrounds the 'true' incidence of disease affecting morbidity and mortality rates. Surveillance systems fail to capture cases at two distinct levels of the surveillance pyramid: from the community since not all cases seek healthcare (under-ascertainment), and at the healthcare-level, representing a failure to adequately report symptomatic cases that have sought medical advice (underreporting). There are several methods to estimate the extent of under-ascertainment and underreporting.

METHODS

Within the context of the ECDC-funded Burden of Communicable Diseases in Europe (BCoDE)-project, an extensive literature review was conducted to identify studies that estimate ascertainment or reporting rates for salmonellosis and campylobacteriosis in European Union Member States (MS) plus European Free Trade Area (EFTA) countries Iceland, Norway and Switzerland and four other OECD countries (USA, Canada, Australia and Japan). Multiplication factors (MFs), a measure of the magnitude of underestimation, were taken directly from the literature or derived (where the proportion of underestimated, under-ascertained, or underreported cases was known) and compared for the two pathogens.

RESULTS

MFs varied between and within diseases and countries, representing a need to carefully select the most appropriate MFs and methods for calculating them. The most appropriate MFs are often disease-, country-, age-, and sex-specific.

CONCLUSIONS

When routine data are used to make decisions on resource allocation or to estimate epidemiological parameters in populations, it becomes important to understand when, where and to what extent these data represent the true picture of disease, and in some instances (such as priority setting) it is necessary to adjust for underestimation. MFs can be used to adjust notification and surveillance data to provide more realistic estimates of incidence.

NADPH oxidase deficient mice develop colitis and bacteremia upon infection with normally avirulent, TTSS-1- and TTSS-2-deficient Salmonella Typhimurium

Infections, microbe sampling and occasional leakage of commensal microbiota and their products across the intestinal epithelial cell layer represent a permanent challenge to the intestinal immune system. The production of reactive oxygen species by NADPH oxidase is thought to be a key element of defense. Patients suffering from chronic granulomatous disease are deficient in one of the subunits of NADPH oxidase. They display a high incidence of Crohn’s disease-like intestinal inflammation and are hyper-susceptible to infection with fungi and bacteria, including a 10-fold increased risk of Salmonellosis. It is not completely understood which steps of the infection process are affected by the NADPH oxidase deficiency. We employed a mouse model for Salmonella diarrhea to study how NADPH oxidase deficiency (Cybb^−/−) affects microbe handling by the large intestinal mucosa. In this animal model, wild type S. Typhimurium causes pronounced enteropathy in wild type mice. In contrast, an avirulent S. Typhimurium mutant (S.Tm^avir; invGsseD), which lacks virulence factors boosting trans-epithelial penetration and growth in the lamina propria, cannot cause enteropathy in wild type mice. We found that Cybb^−/− mice are efficiently infected by S.Tm^avir and develop enteropathy by day 4 post infection. Cell depletion experiments and infections in Cybb^−/−Myd88^−/− mice indicated that the S.Tm^avir-inflicted disease in Cybb^−/− mice hinges on CD11c⁺CX₃CR1⁺ monocytic phagocytes mediating colonization of the cecal lamina propria and on Myd88-dependent proinflammatory immune responses. Interestingly, in mixed bone marrow chimeras a partial reconstitution of Cybb-proficiency in the bone marrow derived compartment was sufficient to ameliorate disease severity. Our data indicate that NADPH oxidase expression is of key importance for restricting the growth of S.Tm^avir in the mucosal lamina propria. This provides important insights into microbe handling by the large intestinal mucosa and the role of NADPH oxidase in maintaining microbe-host mutualism at this exposed body surface.

Seroepidemiology of pertussis in a cross-sectional study of an adult general population in Denmark

An increase in pertussis has been observed in several countries over the last decades, especially in adult populations. The seroprevalence of pertussis was determined in a cross-sectional study of the adult population in the Copenhagen area, Denmark, conducted between 2006 and 2008. Specific IgG antibodies against pertussis toxin (PT) were measured in 3440 persons resulting in an age-standardized seroprevalence of 3·0% (95% confidence interval 1·9–4·7) using an IgG anti-PT cut-off of 75 IU/ml. By using antibody decay profiles from longitudinal data the estimated seroincidence was 143/1000 person-years. In contrast, an incidence of 0·03/1000 person-years was estimated from the official data of notified cases during the same period. Of the investigated risk factors, only age and education were significantly associated with pertussis infection. This study indicates that pertussis is highly underestimated in the adult population in Denmark, which has implications for future prevention strategies, including raising the awareness of pertussis.

Campylobacter seroconversion rates in selected countries in the European Union

As a major foodborne pathogen, Campylobacter is frequently isolated from food sources of animal origin. In contrast, human Campylobacter illness is relatively rare, but has a considerable health burden due to acute enteric illness as well as severe sequelae. To study silent transmission, serum antibodies can be used as biomarkers to estimate seroconversion rates, as a proxy for infection pressure. This novel approach to serology shows that infections are much more common than disease, possibly because most infections remain asymptomatic. This study used antibody titres measured in serum samples collected from healthy subjects selected randomly in the general population from several countries in the European Union (EU). Estimates of seroconversion rates to Campylobacter were calculated for seven countries: Romania, Poland, Italy, France, Finland, Denmark and The Netherlands. Results indicate high infection pressures in all these countries, slightly increasing in Eastern EU countries. Of these countries, the differences in rates of notified illnesses are much greater, with low numbers in France and Poland, possibly indicating lower probability of detection due to differences in the notification systems, but in the latter case it cannot be excluded that more frequent exposure confers better protection due to acquired immunity.

A novel method for evaluating natural and vaccine induced serological responses to Bordetella pertussis antigens

We studied the time course of serum IgG antibodies against 3 different pertussis vaccine antigens: PT (pertussis toxin), FHA (filamentous hemagglutinin), Prn (pertactin) in sera from individuals vaccinated with four different pertussis vaccines at 4 years of age: (N=44, 44, 23 and 23, respectively,) and compared the responses to/after natural infection with Bordetella pertussis (N=44, age 1-8 years). These longitudinal data were analyzed with a novel method, using a mathematical model to describe the observed responses, and their variation among subjects. This allowed us to estimate biologically meaningful characteristics of the serum antibody response, like peak level and decay rate, and to compare these among natural infections and vaccine responses. Compared to natural infection, responses to PT after vaccination with the tested vaccines are smaller in magnitude and tend to decay slightly faster. When present in vaccines, FHA and Prn tend to produce high peak levels, higher than those in naturally infected patients, but these decay faster. As expected, the Dutch whole cell vaccine produced lower antibody responses than the acellular vaccines. This model allows a better comparison of the kinetics of vaccine induced antibody responses and after natural infection over a long follow up period.

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

BACKGROUND

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.

METHODS

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.

RESULTS

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.

CONCLUSIONS

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.

Time-course of antibody responses against Coxiella burnetii following acute Q fever

Large outbreaks of Q fever in The Netherlands have provided a unique opportunity for studying longitudinal serum antibody responses in patients. Results are presented of a cohort of 344 patients with acute symptoms of Q fever with three or more serum samples per patient. In all these serum samples IgM and IgG against phase 1 and 2 Coxiella burnetii were measured by an immunofluorescence assay. A mathematical model of the dynamic interaction of serum antibodies and pathogens was used in a mixed model framework to quantitatively analyse responses to C. burnetii infection. Responses show strong heterogeneity, with individual serum antibody responses widely different in magnitude and shape. Features of the response, peak titre and decay rate, are used to characterize the diversity of the observed responses. Binary mixture analysis of IgG peak levels (phases 1 and 2) reveals a class of patients with high IgG peak titres that decay slowly and may represent potential chronic cases. When combining the results of mixture analysis into an odds score, it is concluded that not only high IgG phase 1 may be predictive for chronic Q fever, but also that high IgG phase 2 may aid in detecting such putative chronic cases.

Biomarker dynamics: estimating infection rates from serological data

The marginal distribution of serum antibody titres in a cross-sectional population sample can be expressed as a function of the infection rate, taking into account heterogeneity in peak levels and decay rates. This marginal model allows estimation of incidences, as well as simple tests for homogeneity across age, gender or geographic strata, using likelihood ratio tests. An example is given using Campylobacter serum antibody data. Using a hierarchical dynamic model to analyse data from a follow-up study in patients with symptomatic Campylobacter infection, we show that the serum antibody response consists of a rapid increase to peak levels followed by a slow decline with a geometric mean halftime of 1.4, 0.6 and 0.3 years for IgG, IgM and IgA, respectively. Antibody peak levels and decay rates were highly variable among subjects. Incidence estimates are consistent among different antibody classes (IgG, IgM and IgA). High seroconversion rates indicate that Campylobacter infection is a frequent event, occurring approximately once every year in any adult person, in the Netherlands, supporting the conclusion that a small fraction of infections leads to symptoms severe enough for notification.

Towards an integrated approach in surveillance of vector-borne diseases in Europe

Vector borne disease (VBD) emergence is a complex and dynamic process. Interactions between multiple disciplines and responsible health and environmental authorities are often needed for an effective early warning, surveillance and control of vectors and the diseases they transmit. To fully appreciate this complexity, integrated knowledge about the human and the vector population is desirable. In the current paper, important parameters and terms of both public health and medical entomology are defined in order to establish a common language that facilitates collaboration between the two disciplines. Special focus is put on the different VBD contexts with respect to the current presence or absence of the disease, the pathogen and the vector in a given location. Depending on the context, whether a VBD is endemic or not, surveillance activities are required to assess disease burden or threat, respectively. Following a decision for action, surveillance activities continue to assess trends.

Enteric virus infection risk from intrusion of sewage into a drinking water distribution network

Contaminants from the soil surrounding drinking water distribution systems are thought to not enter the drinking water when sufficient internal pressure is maintained. Pressure transients may cause short intervals of negative pressure, and the soil near drinking water pipes often contains fecal material due to the proximity of sewage lines, so that a pressure event may cause intrusion of pathogens. This paper presents a risk model for predicting intrusion and dilution of viruses and their transport to consumers. Random entry and dilution of virus was simulated by embedding the hydraulic model into a Monte Carlo simulation. Special attention was given to adjusting for the coincidence of virus presence and use of tap water, as independently occurring short-term events within the longer interval that the virus is predicted to travel in any branch of the distribution system. The probability that a consumer drinks water contaminated with virus is small, but when this happens the virus concentration tends to be high and the risk of infection may be considerable. The spatial distribution of infection risk is highly heterogeneous. The presence of a chlorine residual reduces the infection risk.