Biomarker dynamics: estimating infection rates from serological data

Livestock-associated spatial risk factors for human salmonellosis and campylobacteriosis

AIMS

Most human infections with non-typhoid Salmonella (NTS) or Campylobacter are zoonotic in nature and acquired though consumption of contaminated food of mainly animal origin. However, individuals may also acquire salmonellosis or campylobacteriosis through non-foodborne transmission pathways, such as those mediated by the environment. This emphasizes the need to consider both direct and indirect exposure to livestock sources as a possible transmission route for NTS and Campylobacter. Therefore, this study aimed at assessing whether salmonellosis and campylobacteriosis incidence is spatially associated with exposure to livestock (i.e. small ruminants, dairy cows, veal calves, laying hens, broiler chickens and pigs) in the Netherlands for the years 2007-2019 and 2014-2019 respectively.

METHODS AND RESULTS

Risk factors (population-weighted number of animals) and their population attributable fractions were determined using a Poisson regression model with a log-link function fitted using integrated nested Laplace approximation. The analyses were performed for different hexagonal sizes (90, 50, 25 and 10 km2) and accounted for geographical coverage of the diagnostic laboratory catchment areas. Moreover, serological data were used to look into the possible effects of acquired immunity due to repeated exposure to the pathogen through the environment that would potentially hinder the analyses based on the incidence of reported cases. A linear mixed-effects model was then fitted where the postal code areas were included as a random effect. Livestock was not consistently significantly associated with acquiring salmonellosis or campylobacteriosis in the Netherlands.

CONCLUSIONS

Results showed that living in livestock-rich areas in the Netherlands is not a consistently significant, spatially restricted risk factor for acquiring salmonellosis or campylobacteriosis, thereby supporting current knowledge that human infections with Salmonella and Campylobacter are mainly foodborne.

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.

SARS-CoV-2 Serology Across Scales: A Framework for Unbiased Estimation of Cumulative Incidence Incorporating Antibody Kinetics and Epidemic Recency

Serosurveys are a key resource for measuring severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) population exposure. A growing body of evidence suggests that asymptomatic and mild infections (together making up over 95% of all infections) are associated with lower antibody titers than severe infections. Antibody levels also peak a few weeks after infection and decay gradually. We developed a statistical approach to produce estimates of cumulative incidence from raw seroprevalence survey results that account for these sources of spectrum bias. We incorporate data on antibody responses on multiple assays from a postinfection longitudinal cohort, along with epidemic time series to account for the timing of a serosurvey relative to how recently individuals may have been infected. We applied this method to produce estimates of cumulative incidence from 5 large-scale SARS-CoV-2 serosurveys across different settings and study designs. We identified substantial differences between raw seroprevalence and cumulative incidence of over 2-fold in the results of some surveys, and we provide a tool for practitioners to generate cumulative incidence estimates with preset or custom parameter values. While unprecedented efforts have been launched to generate SARS-CoV-2 seroprevalence estimates over this past year, interpretation of results from these studies requires properly accounting for both population-level epidemiologic context and individual-level immune dynamics.

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.

Severe Acute Respiratory Syndrome Coronavirus 2 Neutralizing Antibody Responses After Community Infections in Children and Adults

Background

We compared postinfection severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) neutralizing antibody (nAb) responses among children and adults while the D614G-like strain and Alpha, Iota, and Delta variants circulated.

Methods

During August 2020-October 2021, households with adults and children were enrolled and followed in Utah, New York City, and Maryland. Participants collected weekly respiratory swabs that were tested for SARS-CoV-2 and had sera collected during enrollment and follow-up. Sera were tested for SARS-CoV-2 nAb by pseudovirus assay. Postinfection titers were characterized with biexponential decay models.

Results

Eighty participants had SARS-CoV-2 infection during the study (47 with D614G-like virus, 17 with B.1.1.7, and 8 each with B.1.617.2 and B.1.526 virus). Homologous nAb geometric mean titers (GMTs) trended higher in adults (GMT = 2320) versus children 0-4 (GMT = 425, P = .33) and 5-17 years (GMT = 396, P = .31) at 1-5 weeks postinfection but were similar from 6 weeks. Timing of peak titers was similar by age. Results were consistent when participants with self-reported infection before enrollment were included (n = 178).

Conclusions

The SARS-CoV-2 nAb titers differed in children compared to adults early after infection but were similar by 6 weeks postinfection. If postvaccination nAb kinetics have similar trends, vaccine immunobridging studies may need to compare nAb responses in adults and children 6 weeks or more after vaccination.

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.

Serology as a Tool to Assess Infectious Disease Landscapes and Guide Public Health Policy

Understanding the local burden and epidemiology of infectious diseases is crucial to guide public health policy and prioritize interventions. Typically, infectious disease surveillance relies on capturing clinical cases within a healthcare system, classifying cases by etiology and enumerating cases over a period of time. Disease burden is often then extrapolated to the general population. Serology (i.e., examining serum for the presence of pathogen-specific antibodies) has long been used to inform about individuals past exposure and immunity to specific pathogens. However, it has been underutilized as a tool to evaluate the infectious disease burden landscape at the population level and guide public health decisions. In this review, we outline how serology provides a powerful tool to complement case-based surveillance for determining disease burden and epidemiology of infectious diseases, highlighting its benefits and limitations. We describe the current serology-based technologies and illustrate their use with examples from both the pre- and post- COVID-19-pandemic context. In particular, we review the challenges to and opportunities in implementing serological surveillance in low- and middle-income countries (LMICs), which bear the brunt of the global infectious disease burden. Finally, we discuss the relevance of serology data for public health decision-making and describe scenarios in which this data could be used, either independently or in conjunction with case-based surveillance. We conclude that public health systems would greatly benefit from the inclusion of serology to supplement and strengthen existing case-based infectious disease surveillance strategies.

Challenges for modelling interventions for future pandemics

Mathematical modelling and statistical inference provide a framework to evaluate different non-pharmaceutical and pharmaceutical interventions for the control of epidemics that has been widely used during the COVID-19 pandemic. In this paper, lessons learned from this and previous epidemics are used to highlight the challenges for future pandemic control. We consider the availability and use of data, as well as the need for correct parameterisation and calibration for different model frameworks. We discuss challenges that arise in describing and distinguishing between different interventions, within different modelling structures, and allowing both within and between host dynamics. We also highlight challenges in modelling the health economic and political aspects of interventions. Given the diversity of these challenges, a broad variety of interdisciplinary expertise is needed to address them, combining mathematical knowledge with biological and social insights, and including health economics and communication skills. Addressing these challenges for the future requires strong cross-disciplinary collaboration together with close communication between scientists and policy makers.

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.

Circulation of pertussis and poor protection against diphtheria among middle-aged adults in 18 European countries

Reported incidence of pertussis in the European Union (EU) and the European Economic Area (EEA) varies and may not reflect the real situation, while vaccine-induced protection against diphtheria and tetanus seems sufficient. We aimed to determine the seroprevalence of DTP antibodies in EU/EEA countries within the age groups of 40-49 and 50-59 years. Eighteen countries collected around 500 samples between 2015 and 2018 (N = 10,302) which were analysed for IgG-DTP specific antibodies. The proportion of sera with pertussis toxin antibody levels ≥100 IU/mL, indicative of recent exposure to pertussis was comparable for 13/18 countries, ranging between 2.7-5.8%. For diphtheria the proportion of sera lacking the protective level (<0.1 IU/mL) varied between 22.8-82.0%. For tetanus the protection was sufficient. Here, we report that the seroprevalence of pertussis in these age groups indicates circulation of B. pertussis across EU/EEA while the lack of vaccine-induced seroprotection against diphtheria is of concern and deserves further attention.

Effects of Accounting for Interval-Censored Antibody Titer Decay on Seroincidence in a Longitudinal Cohort Study of Leptospirosis

Accurate measurements of seroincidence are critical for infections undercounted by reported cases, such as influenza, arboviral diseases, and leptospirosis. However, conventional methods of interpreting paired serological samples do not account for antibody titer decay, resulting in underestimated seroincidence rates. To improve interpretation of paired sera, we modeled exponential decay of interval-censored microscopic agglutination test titers using a historical data set of leptospirosis cases traced to a point source exposure in Italy in 1984. We then applied that decay rate to a longitudinal cohort study conducted in a high-transmission setting in Salvador, Brazil (2013-2015). We estimated a decay constant of 0.926 (95% confidence interval: 0.918, 0.934) titer dilutions per month. Accounting for decay in the cohort increased the mean infection rate to 1.21 times the conventionally defined rate over 6-month intervals (range, 1.10-1.36) and 1.82 times that rate over 12-month intervals (range, 1.65-2.07). Improved estimates of infection in longitudinal data have broad epidemiologic implications, including comparing studies with different sampling intervals, improving sample size estimation, and determining risk factors for infection and the role of acquired immunity. Our method of estimating and accounting for titer decay is generalizable to other infections defined using interval-censored serological assays.

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.

Demonstrating the Benefits of Predictive Bayesian Dose-Response Relationships Using Six Exposure Studies of Cryptosporidium parvum

A conventional dose-response function can be refitted as additional data become available. A predictive dose-response function in contrast does not require a curve-fitting step, only additional data and presents the unconditional probabilities of illness, reflecting the level of information it contains. In contrast, the predictive Bayesian dose-response function becomes progressively less conservative as more information is included. This investigation evaluated the potential for using predictive Bayesian methods to develop a dose-response for human infection that improves on existing models, to show how predictive Bayesian statistical methods can utilize additional data, and expand the Bayesian methods for a broad audience including those concerned about an oversimplification of dose-response curve use in quantitative microbial risk assessment (QMRA). This study used a dose-response relationship incorporating six separate data sets for Cryptosporidium parvum. A Pareto II distribution with known priors was applied to one of the six data sets to calibrate the model, while the others were used for subsequent updating. While epidemiological principles indicate that local variations, host susceptibility, and organism strain virulence may vary, the six data sets all appear to be well characterized using the Bayesian approach. The adaptable model was applied to an existing data set for Campylobacter jejuni for model validation purposes, which yielded results that demonstrate the ability to analyze a dose-response function with limited data using and update those relationships with new data. An analysis of the goodness of fit compared to the beta-Poisson methods also demonstrated correlation between the predictive Bayesian model and the data.

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."

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.

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.

Kinetics of serum antibodies in response to infection with Yersinia enterocolitica

Information on the kinetics of the serum antibody response to infection with Yersinia enterocolitica is essential to allow the estimation and comparison of seroconversion rates in a diversity of pools of cross-sectional serum antibody measurements. Data from 94 patients with acute enteritis caused by Yersinia infection were used. The follow-up period for the longitudinal study was 36 months, addressed by questionnaire. An indirect enzyme-linked immunosorbent assay method was adapted to determine the concentration of antibodies against Y. enterocolitica in human sera. A mathematical within-host model was used to describe the interaction between pathogen and immune system and the waning of immunity after clearing of the pathogen. All observed antibodies (IgG, IgM, IgA) reached peak levels shortly after infection and then decayed slowly indicating that the median levels decreased only little during the observation period. Estimated maximum peak antibody levels were highest in IgG. Seroresponse curves of all antibodies showed large individual variation between patients. There was no apparent pattern of variation with age, nor any notable difference between genders. Estimated half-times were very long for all antibodies, and their posterior distributions were highly skewed. IgA appeared to have the most persistent antibody response, compared with IgG and IgM. Median peak levels of all three antibodies were similar. There was no significance found between peak antibody levels and severity of symptoms of gastrointestinal infection and severity of joint pain. Our findings allow the use of cross-sectional serum antibody measurements as biomarkers, to estimate seroconversion rates. Such seroincidence estimates include asymptomatic seroconversions, thereby avoiding under-reporting, and allows the comparison of infection pressures among countries, independent of their healthcare and surveillance systems.

Waning and boosting: on the dynamics of immune status

The aim is to describe the distribution of immune status (as captured by antibody level) on the basis of a within-host submodel for continuous waning and occasional boosting. Inspired by Feller's fundamental work and the more recent delay equation formulation of models for the dynamics of physiologically structured populations, we derive, for given force of infection, a linear renewal equation. The solution is obtained by generation expansion, with the generation number corresponding to the number of times the individual became infected. Our main result provides a precise characterization of the stable distribution of immune status.

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.

Acute illness from Campylobacter jejuni may require high doses while infection occurs at low doses

Data from a set of different studies on the infectivity and pathogenicity of Campylobacter jejuni were analyzed with a multilevel model, allowing for effects of host species (nonhuman primates and humans) and different strains of the pathogen. All challenge studies involved high doses of the pathogen, resulting in all exposed subjects to become infected. In only one study a dose response effect (increasing trend with dose) for infection was observed. High susceptibility to infection with C. jejuni was found in a joint analysis of outbreaks and challenge studies. For that reason four outbreaks, associated with raw milk consumption, were also included in the present study. The high doses used for inoculation did not cause all infected subjects to develop acute enteric symptoms. The observed outcomes are consistent with a dose response effect for acute symptoms among infected subjects: a conditional illness dose response relation. Nonhuman primates and human volunteers did not appear to have different susceptibilities for developing enteric symptoms, but exposure in outbreaks (raw milk) did lead to a higher probability of symptomatic campylobacteriosis.

Estimating the incidence of rotavirus infection in children from India and Malawi from serial anti-rotavirus IgA titres

Accurate estimates of rotavirus incidence in infants are crucial given disparities in rotavirus vaccine effectiveness from low-income settings. Sero-surveys are a pragmatic means of estimating incidence however serological data is prone to misclassification. This study used mixture models to estimate incidence of rotavirus infection from anti-rotavirus immunoglobulin A (IgA) titres in infants from Vellore, India, and Karonga, Malawi. IgA titres were measured using serum samples collected at 6 month intervals for 36 months from 373 infants from Vellore and 12 months from 66 infants from Karonga. Mixture models (two component Gaussian mixture distributions) were fit to the difference in titres between time points to estimate risk of sero-positivity and derive incidence estimates. A peak incidence of 1.05(95% confidence interval [CI]: 0.64, 1.64) infections per child-year was observed in the first 6 months of life in Vellore. This declined incrementally with each subsequent time interval. Contrastingly in Karonga incidence was greatest in the second 6 months of life (1.41 infections per child year [95% CI: 0.79, 2.29]). This study demonstrates that infants from Vellore experience peak rotavirus incidence earlier than those from Karonga. Identifying such differences in transmission patterns is important in informing vaccine strategy, particularly where vaccine effectiveness is modest.

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.

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.

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.

Impact of waning acquired immunity and asymptomatic infections on case-control studies for enteric pathogens

Case-control studies of outbreaks and of sporadic cases of infectious diseases may provide a biased estimate of the infection rate ratio, due to selecting controls that are not at risk of disease. We use a dynamic mathematical model to explore biases introduced in results drawn from case-control studies of enteric pathogens by waning and boosting of immunity, and by asymptomatic infections, using Campylobacter jejuni as an example. Individuals in the population are either susceptible (at risk of infection and disease), fully protected (not at risk of either) or partially protected (at risk of infection but not of disease). The force of infection is a function of the exposure frequency and the exposure dose. We show that the observed disease odds ratios are indeed strongly biased towards the null, i.e. much lower than the infection rate ratio, and furthermore even not proportional to it. The bias could theoretically be controlled by sampling controls only from the reservoir of susceptible individuals. The population at risk is in a dynamic equilibrium, and cannot be identified as those who are not and have never experienced disease. Individual-level samples to measure protective immunity would be required, complicating the design, cost and execution of case-control studies.

Reassessment of the Enteropathogenicity of Mesophilic Aeromonas Species

Cases of Aeromonas diarrhea have been described all over the world. The genus Aeromonas includes ca. 30 species, of which 10 have been isolated in association with gastroenteritis. The dominating species that account for ca. 96% of the identified strains are Aeromonas caviae, A. veronii, A. dhakensis, and A. hydrophila. However, the role of Aeromonas as a true enteropathogen has been questioned on the basis of the lack of outbreaks, the non-fulfillment of Koch's postulates and the low numbers of acute illnesses in the only existing human challenge study. In the present study we reassess the enteropathogenicity of Aeromonas using dose response models for microbial infection and acute illness. The analysis uses the data from the human challenge study and additional data from selected outbreak investigations where the numbers exposed and the dose were reported, allowing their inclusion as "natural experiments". In the challenge study several cases of asymptomatic shedding were found (26.3%, 15/57), however, only 3.5% (2/57) of those challenged with Aeromonas developed acute enteric symptoms (i.e., diarrhea). The "natural experiments" showed a much higher risk of illness associated with exposure to Aeromonas, even at moderate to low doses. The median dose required for 1% illness risk, was ~1.4 × 104 times higher in the challenge study (1.24 × 104 cfu) compared to natural exposure events (0.9 cfu). The dose response assessment presented in this study shows that the combined challenge and outbreak data are consistent with high infectivity of Aeromonas, and a wide range of susceptibility to acute enteric illness. To illustrate the outcomes, we simulate the risk associated with concentrations of Aeromonas found in different water and food matrices, indicating the disease burden potentially associated with these bacteria. In conclusion this study showed that Aeromonas is highly infectious, and that human susceptibility to illness may be high, similar to undisputed enteropathogens like Campylobacter or Salmonella.

Use of Pathogen-Specific Antibody Biomarkers to Estimate Waterborne Infections in Population-Based Settings

PURPOSE OF REVIEW

This review discusses the utility of pathogen-specific antibody biomarkers for improving estimates of the population burden of waterborne infections, assessing the fraction of infections that can be prevented by specific water treatments, and understanding transmission routes and the natural history and ecology of disease in different populations (including asymptomatic infection rates).

RECENT FINDINGS

We review recent literature on the application of pathogen-specific antibody response data to estimate incidence and prevalence of acute infections and their utility to assess the contributions of waterborne transmission pathways. Advantages and technical challenges associated with the use of serum versus minimally invasive salivary antibody biomarkers in cross-sectional and prospective surveys are discussed. We highlight recent advances and challenges and outline future directions for research, development, and application of antibody-based and other immunological biomarkers of waterborne infections.

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.

High Prevalence of Antibodies against the Bacterium Treponema pallidum in Senegalese Guinea Baboons (Papio papio)

The bacterium Treponema pallidum is known to cause syphilis (ssp. pallidum), yaws (ssp. pertenue), and endemic syphilis (ssp. endemicum) in humans. Nonhuman primates have also been reported to be infected with the bacterium with equally versatile clinical manifestations, from severe skin ulcerations to asymptomatic. At present all simian strains are closely related to human yaws-causing strains, an important consideration for yaws eradication. We tested clinically healthy Guinea baboons (Papio papio) at Parc National Niokolo Koba in south eastern Senegal for the presence of anti-T. pallidum antibodies. Since T. pallidum infection in this species was identified 50 years ago, and there has been no attempt to treat non-human primates for infection, it was hypothesized that a large number of West African baboons are still infected with simian strains of the yaws-bacterium. All animals were without clinical signs of treponematoses, but 18 of 20 (90%) baboons tested positive for antibodies against T. pallidum based on treponemal tests. Yet, Guinea baboons seem to develop no clinical symptoms, though it must be assumed that infection is chronic or comparable to the latent stage in human yaws infection. The non-active character is supported by the low anti-T. pallidum serum titers in Guinea baboons (median = 1:2,560) versus serum titers that are found in genital-ulcerated olive baboons with active infection in Tanzania (range of medians among the groups of initial, moderate, and severe infected animals = 1:15,360 to 1:2.097e+7). Our findings provide evidence for simian infection with T. pallidum in wild Senegalese baboons. Potentially, Guinea baboons in West Africa serve as a natural reservoir for human infection, as the West African simian strain has been shown to cause sustainable yaws infection when inoculated into humans. The present study pinpoints an area where further research is needed to support the currently on-going second WHO led yaws eradication campaign with its goal to eradicate yaws by 2020.

Kinetics of antibody response to Coxiella burnetii infection (Q fever): Estimation of the seroresponse onset from antibody levels

BACKGROUND

From 2007 to 2009, the Netherlands experienced a major Q fever epidemic. Long-term serological follow-up of acute Q fever patients enabled the investigation of longitudinal antibody responses and estimating the onset of the seroresponse in individual patients.

METHODS

All available IgG and IgM phase I and II antibody measurements determined by immunofluorescence assay at month 3, 6, 12, and 48 from 2321 acute Q fever patients were retrospectively analyzed. Characteristic features of the antibody response were calculated. To model the seroresponse onset, serological data from patients diagnosed with a positive C. burnetii PCR test (n=364), and therefore with a known time of infection, were used as reference.

RESULTS

In 9083 IgG samples and 3260 IgM samples large heterogeneity in shape and magnitude of antibody responses was observed. Phase II reached higher levels than phase I, and IgG antibodies were more persistent than IgM. The estimated seroresponse latency allowed for determining the time since start of the seroresponse from the concentrations of the different antibodies against C. burnetii.

CONCLUSIONS

The extraordinary large serological dataset provides new insight into the kinetics of the immunoglobulins against C. burnetii antigens. This knowledge is useful for seroprevalence studies and helps to better understand infection dynamics.

WEST NILE VIRUS ANTIBODY DECAY RATE IN FREE-RANGING BIRDS

Antibody duration, following a humoral immune response to West Nile virus (WNV) infection, is poorly understood in free-ranging avian hosts. Quantifying antibody decay rate is important for interpreting serologic results and for understanding the potential for birds to serorevert and become susceptible again. We sampled free-ranging birds in Chicago, Illinois, US, from 2005 to 2011 and Atlanta, Georgia, US, from 2010 to 2012 to examine the dynamics of antibody decay following natural WNV infection. Using serial dilutions in a blocking enzyme-linked immunosorbent assay, we quantified WNV antibody titer in repeated blood samples from individual birds over time. We quantified a rate of antibody decay for 23 Northern Cardinals (Cardinalis cardinalis) of 0.198 natural log units per month and 24 individuals of other bird species of 0.178 natural log units per month. Our results suggest that juveniles had a higher rate of antibody decay than adults, which is consistent with nonlinear antibody decay at different times postexposure. Overall, most birds had undetectable titers 2 yr postexposure. Nonuniform WNV antibody decay rates in free-ranging birds underscore the need for cautious interpretation of avian serology results in the context of arbovirus surveillance and epidemiology.

Validation of serological tests for the detection of antibodies against Treponema pallidum in nonhuman primates

There is evidence to suggest that the yaws bacterium (Treponema pallidum ssp. pertenue) may exist in non-human primate populations residing in regions where yaws is endemic in humans. Especially in light of the fact that the World Health Organizaiton (WHO) recently launched its second yaws eradication campaign, there is a considerable need for reliable tools to identify treponemal infection in our closest relatives, African monkeys and great apes. It was hypothesized that commercially available serological tests detect simian anti-T. pallidum antibody in serum samples of baboons, with comparable sensitivity and specificity to their results on human sera. Test performances of five different treponemal tests (TTs) and two non-treponemal tests (NTTs) were evaluated using serum samples of 57 naturally T. pallidum-infected olive baboons (Papio anubis) from Lake Manyara National Park in Tanzania. The T. pallidum particle agglutination assay (TP-PA) was used as a gold standard for comparison. In addition, the overall infection status of the animals was used to further validate test performances. For most accurate results, only samples that originated from baboons of known infection status, as verified in a previous study by clinical inspection, PCR and immunohistochemistry, were included. All tests, TTs and NTTs, used in this study were able to reliably detect antibodies against T. pallidum in serum samples of infected baboons. The sensitivity of TTs ranged from 97.7-100%, while specificity was between 88.0-100.0%. The two NTTs detected anti-lipoidal antibodies in serum samples of infected baboons with a sensitivity of 83.3% whereas specificity was 100%. For screening purposes, the TT Espline TP provided the highest sensitivity and specificity and at the same time provided the most suitable format for use in the field. The enzyme immune assay Mastblot TP (IgG), however, could be considered as a confirmatory test.

Impact of acquired immunity and dose-dependent probability of illness on quantitative microbial risk assessment

Dose-response models in microbial risk assessment consider two steps in the process ultimately leading to illness: from exposure to (asymptomatic) infection, and from infection to (symptomatic) illness. Most data and theoretical approaches are available for the exposure-infection step; the infection-illness step has received less attention. Furthermore, current microbial risk assessment models do not account for acquired immunity. These limitations may lead to biased risk estimates. We consider effects of both dose dependency of the conditional probability of illness given infection, and acquired immunity to risk estimates, and demonstrate their effects in a case study on exposure to Campylobacter jejuni. To account for acquired immunity in risk estimates, an inflation factor is proposed. The inflation factor depends on the relative rates of loss of protection over exposure. The conditional probability of illness given infection is based on a previously published model, accounting for the within-host dynamics of illness. We find that at low (average) doses, the infection-illness model has the greatest impact on risk estimates, whereas at higher (average) doses and/or increased exposure frequencies, the acquired immunity model has the greatest impact. The proposed models are strongly nonlinear, and reducing exposure is not expected to lead to a proportional decrease in risk and, under certain conditions, may even lead to an increase in risk. The impact of different dose-response models on risk estimates is particularly pronounced when introducing heterogeneity in the population exposure distribution.

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.

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.

Preventing Campylobacter at the source: why is it so difficult?

Campylobacteriosis in humans, caused by Campylobacter jejuni and Campylobacter coli, is the most common recognized bacterial zoonosis in the European Union and the United States. The acute phase is characterized by gastrointestinal symptoms. The long-term sequelae (Guillain-Barré syndrome, reactive arthritis, and postinfectious irritable bowel syndrome) contribute considerably to the disease burden. Attribution studies identified poultry as the reservoir responsible for up to 80% of the human Campylobacter infections. In the European Union, an estimated 30% of the human infections are associated with consumption and preparation of poultry meat. Until now, interventions in the poultry meat production chain have not been effectively introduced except for targeted interventions in Iceland and New Zealand. Intervention measures (eg, biosecurity) have limited effect or are hampered by economic aspects or consumer acceptance. In the future, a multilevel approach should be followed, aiming at reducing the level of contamination of consumer products rather than complete absence of Campylobacter.

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.

The protective effects of temporary immunity under imposed infection pressure

The aim of this paper is to show in explicit detail that, due to the effects of waning and boosting of immunity, an increasing force of infection does not necessarily lead to an increase in the incidence of disease. Under certain conditions, a decrease of the force of infection may in fact lead to an increase of the incidence of disease. Thus we confirm and reinforce the conclusions from Águas et al. (2006), concerning pertussis. We do so, however, in the context of Campylobacter infections in humans deriving from animal reservoirs. For such an externally 'driven' epidemic we can ignore the transmission feedback cycle and treat the force of infection as a parameter. As this parameter is, to a certain extent, under public health control, our findings constitute an important warning: reducing exposure may not necessarily lead to a reduction in the occurrence of clinical illness. In a second part of the paper we relate the model parameters to the available data concerning campylobacteriosis.