Seventy-five years of estimating the force of infection from current status data

Estimating age-time-dependent malaria force of infection accounting for unobserved heterogeneity

Despite well-recognized heterogeneity in malaria transmission, key parameters such as the force of infection (FOI) are generally estimated ignoring the intrinsic variability in individual infection risks. Given the potential impact of heterogeneity on the estimation of the FOI, we estimate this quantity accounting for both observed and unobserved heterogeneity. We used cohort data of children aged 0·5-10 years evaluated for the presence of malaria parasites at three sites in Uganda. Assuming a Susceptible-Infected-Susceptible model, we show how the FOI relates to the point prevalence, enabling the estimation of the FOI by modelling the prevalence using a generalized linear mixed model. We derive bounds for varying parasite clearance distributions. The resulting FOI varies significantly with age and is estimated to be highest among children aged 5-10 years in areas of high and medium malaria transmission and highest in children aged below 1 year in a low transmission setting. Heterogeneity is greater between than within households and it increases with decreasing risk of malaria infection. This suggests that next to the individual's age, heterogeneity in malaria FOI may be attributed to household conditions. When estimating the FOI, accounting for both observed and unobserved heterogeneity in malaria acquisition is important for refining malaria spread models.

Estimating Loss of Brucella Abortus Antibodies from Age-Specific Serological Data In Elk

Serological data are one of the primary sources of information for disease monitoring in wildlife. However, the duration of the seropositive status of exposed individuals is almost always unknown for many free-ranging host species. Directly estimating rates of antibody loss typically requires difficult longitudinal sampling of individuals following seroconversion. Instead, we propose a Bayesian statistical approach linking age and serological data to a mechanistic epidemiological model to infer brucellosis infection, the probability of antibody loss, and recovery rates of elk (Cervus canadensis) in the Greater Yellowstone Ecosystem. We found that seroprevalence declined above the age of ten, with no evidence of disease-induced mortality. The probability of antibody loss was estimated to be 0.70 per year after a five-year period of seropositivity and the basic reproduction number for brucellosis to 2.13. Our results suggest that individuals are unlikely to become re-infected because models with this mechanism were unable to reproduce a significant decline in seroprevalence in older individuals. This study highlights the possible implications of antibody loss, which could bias our estimation of critical epidemiological parameters for wildlife disease management based on serological data.

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.

Reconstruction of Rift Valley fever transmission dynamics in Madagascar: estimation of force of infection from seroprevalence surveys using Bayesian modelling

The force of infection (FOI) is one of the key parameters describing the dynamics of transmission of vector-borne diseases. Following the occurrence of two major outbreaks of Rift Valley fever (RVF) in Madagascar in 1990-91 and 2008-09, recent studies suggest that the pattern of RVF virus (RVFV) transmission differed among the four main eco-regions (East, Highlands, North-West and South-West). Using Bayesian hierarchical models fitted to serological data from cattle of known age collected during two surveys (2008 and 2014), we estimated RVF FOI and described its variations over time and space in Madagascar. We show that the patterns of RVFV transmission strongly differed among the eco-regions. In the North-West and Highlands regions, these patterns were synchronous with a high intensity in mid-2007/mid-2008. In the East and South-West, the peaks of transmission were later, between mid-2008 and mid-2010. In the warm and humid northwestern eco-region favorable to mosquito populations, RVFV is probably transmitted all year-long at low-level during inter-epizootic period allowing its maintenance and being regularly introduced in the Highlands through ruminant trade. The RVF surveillance of animals of the northwestern region could be used as an early warning indicator of an increased risk of RVF outbreak in Madagascar.

Mathematical Modelling of Trachoma Transmission, Control and Elimination

The World Health Organization has targeted the elimination of blinding trachoma by the year 2020. To this end, the Global Elimination of Blinding Trachoma (GET, 2020) alliance relies on a four-pronged approach, known as the SAFE strategy (S for trichiasis surgery; A for antibiotic treatment; F for facial cleanliness and E for environmental improvement). Well-constructed and parameterized mathematical models provide useful tools that can be used in policy making and forecasting in order to help to control trachoma and understand the feasibility of this large-scale elimination effort. As we approach this goal, the need to understand the transmission dynamics of infection within areas of different endemicities, to optimize available resources and to identify which strategies are the most cost-effective becomes more pressing. In this study, we conducted a review of the modelling literature for trachoma and identified 23 articles that included a mechanistic or statistical model of the transmission, dynamics and/or control of (ocular) Chlamydia trachomatis. Insights into the dynamics of trachoma transmission have been generated through both deterministic and stochastic models. A large body of the modelling work conducted to date has shown that, to varying degrees of effectiveness, antibiotic administration can reduce or interrupt trachoma transmission. However, very little analysis has been conducted to consider the effect of nonpharmaceutical interventions (and particularly the F and E components of the SAFE strategy) in helping to reduce transmission. Furthermore, very few of the models identified in the literature review included a structure that permitted tracking of the prevalence of active disease (in the absence of active infection) and the subsequent progression to disease sequelae (the morbidity associated with trachoma and ultimately the target of GET 2020 goals). This represents a critical gap in the current trachoma modelling literature, which makes it difficult to reliably link infection and disease. In addition, it hinders the application of modelling to assist the public health community in understanding whether trachoma programmes are on track to reach the GET goals by 2020. Another gap identified in this review was that of the 23 articles examined, only one considered the cost-effectiveness of the interventions implemented. We conclude that although good progress has been made towards the development of modelling frameworks for trachoma transmission, key components of disease sequelae representation and economic evaluation of interventions are currently missing from the available literature. We recommend that rapid advances in these areas should be urgently made to ensure that mathematical models for trachoma transmission can robustly guide elimination efforts and quantify progress towards GET 2020.

A dynamic transmission model with age-dependent infectiousness and reactivation for cytomegalovirus in the United States: Potential impact of vaccination strategies on congenital infection

We present an age-structured dynamic transmission model for cytomegalovirus (CMV) in the United States, based on natural history and available data, primarily aiming to combine the available qualitative and quantitative knowledge toward more complex modeling frameworks to better reflect the underlying biology and epidemiology of the CMV infection. The model structure explicitly accounts for primary infections, reactivations and re-infections. Duration of infectiousness and likelihood of reactivation were both assumed to be age-dependent, and natural reduction in the re-infection risk following primary infection was included. We used an empirical social contact matrix (POLYMOD-based) as support for CMV transmission between different age groups. The baseline model reproduced well the age-stratified seroprevalence data (National Health and Nutrition Examination Survey III) used for calibration. The model was further used to explore the potential impact of hypothetical vaccination on reducing congenital CMV infection under various vaccine profiles and vaccination scenarios. Our preliminary model-based simulations suggested that while infant vaccination may represent an attractive way to reduce congenital CMV infection over time, adolescent female vaccination with an adequate routine booster platform may, under certain conditions, provide an alternative. However, for such tools to be considered toward actual decision-making, enhanced validations based on additional studies and data would be further necessary. The modeling framework presented in this paper was designed to be sufficiently general and flexible, such that it can allow for further adaptations to reflect new knowledge or data that may become available in the future.

River Blindness: Mathematical Models for Control and Elimination

Human onchocerciasis (river blindness) is one of the few neglected tropical diseases (NTDs) whose control strategies have been informed by mathematical modelling. With the change in focus from elimination of the disease burden to elimination of Onchocerca volvulus, much remains to be done to refine, calibrate and validate existing models. Under the impetus of the NTD Modelling Consortium, the teams that developed EPIONCHO and ONCHOSIM have joined forces to compare and improve these frameworks to better assist ongoing elimination efforts. We review their current versions and describe how they are being used to address two key questions: (1) where can onchocerciasis be eliminated with current intervention strategies by 2020/2025? and (2) what alternative/complementary strategies could help to accelerate elimination where (1) cannot be achieved? The control and elimination of onchocerciasis from the African continent is at a crucial crossroad. The African Programme for Onchocerciasis Control closed at the end of 2015, and although a new platform for support and integration of NTD control has been launched, the disease will have to compete with a myriad of other national health priorities at a pivotal time in the road to elimination. However, never before had onchocerciasis control a better arsenal of intervention strategies as well as diagnostics. It is, therefore, timely to present two models of different geneses and modelling traditions as they come together to produce robust decision-support tools. We start by describing the structural and parametric assumptions of EPIONCHO and ONCHOSIM; we continue by summarizing the modelling of current treatment strategies with annual (or biannual) mass ivermectin distribution and introduce a number of alternative strategies, including other microfilaricidal therapies (such as moxidectin), macrofilaricidal (anti-wolbachial) treatments, focal vector control and the possibility of an onchocerciasis vaccine. We conclude by discussing challenges, opportunities and future directions.

Sample size and power calculations for detecting changes in malaria transmission using antibody seroconversion rate

BACKGROUND

Several studies have highlighted the use of serological data in detecting a reduction in malaria transmission intensity. These studies have typically used serology as an adjunct measure and no formal examination of sample size calculations for this approach has been conducted.

METHODS

A sample size calculator is proposed for cross-sectional surveys using data simulation from a reverse catalytic model assuming a reduction in seroconversion rate (SCR) at a given change point before sampling. This calculator is based on logistic approximations for the underlying power curves to detect a reduction in SCR in relation to the hypothesis of a stable SCR for the same data. Sample sizes are illustrated for a hypothetical cross-sectional survey from an African population assuming a known or unknown change point.

RESULTS

Overall, data simulation demonstrates that power is strongly affected by assuming a known or unknown change point. Small sample sizes are sufficient to detect strong reductions in SCR, but invariantly lead to poor precision of estimates for current SCR. In this situation, sample size is better determined by controlling the precision of SCR estimates. Conversely larger sample sizes are required for detecting more subtle reductions in malaria transmission but those invariantly increase precision whilst reducing putative estimation bias.

CONCLUSIONS

The proposed sample size calculator, although based on data simulation, shows promise of being easily applicable to a range of populations and survey types. Since the change point is a major source of uncertainty, obtaining or assuming prior information about this parameter might reduce both the sample size and the chance of generating biased SCR estimates.

Data-Driven Models of Foot-and-Mouth Disease Dynamics: A Review

Foot-and-mouth disease virus (FMDV) threatens animal health and leads to considerable economic losses worldwide. Progress towards minimizing both veterinary and financial impact of the disease will be made with targeted disease control policies. To move towards targeted control, specific targets and detailed control strategies must be defined. One approach for identifying targets is to use mathematical and simulation models quantified with accurate and fine-scale data to design and evaluate alternative control policies. Nevertheless, published models of FMDV vary in modelling techniques and resolution of data incorporated. In order to determine which models and data sources contain enough detail to represent realistic control policy alternatives, we performed a systematic literature review of all FMDV dynamical models that use host data, disease data or both data types. For the purpose of evaluating modelling methodology, we classified models by control strategy represented, resolution of models and data, and location modelled. We found that modelling methodology has been well developed to the point where multiple methods are available to represent detailed and contact-specific transmission and targeted control. However, detailed host and disease data needed to quantify these models are only available from a few outbreaks. To address existing challenges in data collection, novel data sources should be considered and integrated into models of FMDV transmission and control. We suggest modelling multiple endemic areas to advance local control and global control and better understand FMDV transmission dynamics. With incorporation of additional data, models can assist with both the design of targeted control and identification of transmission drivers across geographic boundaries.

Extracting information from S-curves of language change

It is well accepted that adoption of innovations are described by S-curves (slow start, accelerating period and slow end). In this paper, we analyse how much information on the dynamics of innovation spreading can be obtained from a quantitative description of S-curves. We focus on the adoption of linguistic innovations for which detailed databases of written texts from the last 200 years allow for an unprecedented statistical precision. Combining data analysis with simulations of simple models (e.g. the Bass dynamics on complex networks), we identify signatures of endogenous and exogenous factors in the S-curves of adoption. We propose a measure to quantify the strength of these factors and three different methods to estimate it from S-curves. We obtain cases in which the exogenous factors are dominant (in the adoption of German orthographic reforms and of one irregular verb) and cases in which endogenous factors are dominant (in the adoption of conventions for romanization of Russian names and in the regularization of most studied verbs). These results show that the shape of S-curve is not universal and contains information on the adoption mechanism.

Serotype-Specific Transmission and Waning Immunity of Endemic Foot-and-Mouth Disease Virus in Cameroon

Foot-and-mouth disease virus (FMDV) causes morbidity and mortality in a range of animals and threatens local economies by acting as a barrier to international trade. The outbreak in the United Kingdom in 2001 that cost billions to control highlighted the risk that the pathogen poses to agriculture. In response, several mathematical models have been developed to parameterize and predict both transmission dynamics and optimal disease control. However, a lack of understanding of the multi-strain etiology prevents characterization of multi-strain dynamics. Here, we use data from FMDV serology in an endemic setting to probe strain-specific transmission and immunodynamics. Five serotypes of FMDV affect cattle in the Far North Region of Cameroon. We fit both catalytic and reverse catalytic models to serological data to estimate the force of infection and the rate of waning immunity, and to detect periods of sustained transmission. For serotypes SAT2, SAT3, and type A, a model assuming life-long immunity fit better. For serotypes SAT1 and type O, the better-fit model suggests that immunity may wane over time. Our analysis further indicates that type O has the greatest force of infection and the longest duration of immunity. Estimates for the force of infection were time-varying and indicated that serotypes SAT1 and O displayed endemic dynamics, serotype A displayed epidemic dynamics, and SAT2 and SAT3 did not sustain local chains of transmission. Since these results were obtained from the same population at the same time, they highlight important differences in transmission specific to each serotype. They also show that immunity wanes at rates specific to each serotype, which influences patterns of local persistence. Overall, this work shows that viral serotypes can differ significantly in their epidemiological and immunological characteristics. Patterns and processes that drive transmission in endemic settings must consider complex viral dynamics for accurate representation and interpretation.

Estimating seroprevalence of human papillomavirus type 16 using a mixture model with smoothed age-dependent mixing proportions

BACKGROUND

The presence in serum of antibodies to viral antigens is generally considered a well-defined marker of past infection or vaccination. However, analyses of serological data that use a cut-off value to classify individuals as seropositive are prone to misclassification bias, in particular when studying infections with a weak serological response, such as the sexually transmitted human papillomavirus (HPV).

METHODS

We analyzed the serological concentrations of HPV type 16 (HPV16) antibodies in the general Dutch population in 2006-2007, before the introduction of mass vaccination against HPV. We used a 2-component mixture model to represent persons who were seronegative or seropositive for HPV16. Component densities were assumed to be log-normally distributed, with parameters possibly dependent on sex. The age-dependent mixing proportions were smoothed using penalized splines to obtain a flexible seroprevalence profile.

RESULTS

Our results suggest that HPV16 seropositivity is associated with higher antibody concentrations in women as compared with men. Seroprevalence shows an increase starting from adolescence in men and women alike, coinciding with the age of sexual debut. Seroprevalence stabilizes in men around age 40, whereas it has a decreasing trend from age 50 onwards in women. Analyses that rely on a cut-off value to classify persons as seropositive yield substantially different seroprevalence profiles, leading to a qualitatively different interpretation of HPV16 infection dynamics.

CONCLUSIONS

Our results provide a benchmark for examining the effect of HPV16 vaccination in future serological surveys. Our method may prove useful for estimating seroprevalence of other infections with a weak serological response.

Seroepidemiology of Toxoplasma in a coastal region of Haiti: multiplex bead assay detection of immunoglobulin G antibodies that recognize the SAG2A antigen

Toxoplasma gondii is a globally distributed parasitic protozoan that infects most warm-blooded animals. We incorporated a bead coupled with recombinant SAG2A protein into our Neglected Tropical Disease (NTD) multiplex bead assay (MBA) panel and used it to determine Toxoplasma infection rates in two studies in Haiti. In a longitudinal cohort study of children aged 0-11 years, the infection rate varied with age reaching a maximum of 0·131 infections/year in children aged 3 years [95% confidence interval (CI) 0·065-0·204]. The median time to seroconversion was estimated to be 9·7 years (95% CI 7·6-∞). In a cross-sectional, community-wide survey of residents of all ages, we determined an overall seroprevalence of 28·2%. The seroprevalence age curve from the cross-sectional study also suggested that the force of infection varied with age and peaked at 0·057 infections/year (95% CI 0·033-0·080) at age 2·6 years. Integration of the Toxoplasma MBA into NTD surveys may allow for better estimates of the potential burden of congenital toxoplasmosis in underserved regions.

The social contact hypothesis under the assumption of endemic equilibrium: Elucidating the transmission potential of VZV in Europe

The basic reproduction number R0 and the effective reproduction number R are pivotal parameters in infectious disease epidemiology, quantifying the transmission potential of an infection in a population. We estimate both parameters from 13 pre-vaccination serological data sets on varicella zoster virus (VZV) in 12 European countries and from population-based social contact surveys under the commonly made assumptions of endemic and demographic equilibrium. The fit to the serology is evaluated using the inferred effective reproduction number R as a model eligibility criterion combined with AIC as a model selection criterion. For only 2 out of 12 countries, the common choice of a constant proportionality factor is sufficient to provide a good fit to the seroprevalence data. For the other countries, an age-specific proportionality factor provides a better fit, assuming physical contacts lasting longer than 15 min are a good proxy for potential varicella transmission events. In all countries, primary infection with VZV most often occurs in early childhood, but there is substantial variation in transmission potential with R0 ranging from 2.8 in England and Wales to 7.6 in The Netherlands. Two non-parametric methods, the maximal information coefficient (MIC) and a random forest approach, are used to explain these differences in R0 in terms of relevant country-specific characteristics. Our results suggest an association with three general factors: inequality in wealth, infant vaccination coverage and child care attendance. This illustrates the need to consider fundamental differences between European countries when formulating and parameterizing infectious disease models.

Lexis diagram and illness-death model: simulating populations in chronic disease epidemiology

Chronic diseases impose a tremendous global health problem of the 21st century. Epidemiological and public health models help to gain insight into the distribution and burden of chronic diseases. Moreover, the models may help to plan appropriate interventions against risk factors. To provide accurate results, models often need to take into account three different time-scales: calendar time, age, and duration since the onset of the disease. Incidence and mortality often change with age and calendar time. In many diseases such as, for example, diabetes and dementia, the mortality of the diseased persons additionally depends on the duration of the disease. The aim of this work is to describe an algorithm and a flexible software framework for the simulation of populations moving in an illness-death model that describes the epidemiology of a chronic disease in the face of the different times-scales. We set up a discrete event simulation in continuous time involving competing risks using the freely available statistical software R. Relevant events are birth, the onset (or diagnosis) of the disease and death with or without the disease. The Lexis diagram keeps track of the different time-scales. Input data are birth rates, incidence and mortality rates, which can be given as numerical values on a grid. The algorithm manages the complex interplay between the rates and the different time-scales. As a result, for each subject in the simulated population, the algorithm provides the calendar time of birth, the age of onset of the disease (if the subject contracts the disease) and the age at death. By this means, the impact of interventions may be estimated and compared.

Time-varying, serotype-specific force of infection of dengue virus

Infectious disease models play a key role in public health planning. These models rely on accurate estimates of key transmission parameters such as the force of infection (FoI), which is the per-capita risk of a susceptible person being infected. The FoI captures the fundamental dynamics of transmission and is crucial for gauging control efforts, such as identifying vaccination targets. Dengue virus (DENV) is a mosquito-borne, multiserotype pathogen that currently infects ∼390 million people a year. Existing estimates of the DENV FoI are inaccurate because they rely on the unrealistic assumption that risk is constant over time. Dengue models are thus unreliable for designing vaccine deployment strategies. Here, we present to our knowledge the first time-varying (daily), serotype-specific estimates of DENV FoIs using a spline-based fitting procedure designed to examine a 12-y, longitudinal DENV serological dataset from Iquitos, Peru (11,703 individuals, 38,416 samples, and 22,301 serotype-specific DENV infections from 1999 to 2010). The yearly DENV FoI varied markedly across time and serotypes (0-0.33), as did daily basic reproductive numbers (0.49-4.72). During specific time periods, the FoI fluctuations correlated across serotypes, indicating that different DENV serotypes shared common transmission drivers. The marked variation in transmission intensity that we detected indicates that intervention targets based on one-time estimates of the FoI could underestimate the level of effort needed to prevent disease. Our description of dengue virus transmission dynamics is unprecedented in detail, providing a basis for understanding the persistence of this rapidly emerging pathogen and improving disease prevention programs.

Dynamics of the force of infection: insights from Echinococcus multilocularis infection in foxes

Characterizing the force of infection (FOI) is an essential part of planning cost effective control strategies for zoonotic diseases. Echinococcus multilocularis is the causative agent of alveolar echinococcosis in humans, a serious disease with a high fatality rate and an increasing global spread. Red foxes are high prevalence hosts of E. multilocularis. Through a mathematical modelling approach, using field data collected from in and around the city of Zurich, Switzerland, we find compelling evidence that the FOI is periodic with highly variable amplitude, and, while this amplitude is similar across habitat types, the mean FOI differs markedly between urban and periurban habitats suggesting a considerable risk differential. The FOI, during an annual cycle, ranges from (0.1,0.8) insults (95% CI) in urban habitat in the summer to (9.4, 9.7) (95% CI) in periurban (rural) habitat in winter. Such large temporal and spatial variations in FOI suggest that control strategies are optimal when tailored to local FOI dynamics.

Human alveolar echinococcosis (AE) is caused by the fox tapeworm E. multilocularis and has a high fatality rate if untreated. The frequency of the tapeworm in foxes can be reduced through the regular distribution of anthelmintic baits and thus decrease the risk of zoonotic transmission. Here, we estimate the force of infection to foxes using a mathematical model and data from necropsied foxes. The results suggest that the frequency of anthelmintic baiting of foxes can be optimised to local variations in transmission that depend upon season and type of fox habitat.

Revisiting Rayong: shifting seroprofiles of dengue in Thailand and their implications for transmission and control

Dengue virus has traditionally caused substantial morbidity and mortality among children less than 15 years of age in Southeast Asia. Over the last 2 decades, a significant increase in the mean age of cases has been reported, and a once pediatric disease now causes substantial burden among the adult population. An age-stratified serological study (n = 1,736) was conducted in 2010 among schoolchildren in the Mueang Rayong district of Thailand, where a similar study had been conducted in 1980/1981. Serotype-specific forces of infection (λ(t)) and basic reproductive numbers (R0) of dengue were estimated for the periods 1969-1980 and 1993-2010. Despite a significant increase in the age at exposure and a decrease in λ(t) from 0.038/year to 0.019/year, R0 changed only from 3.3 to 3.2. Significant heterogeneity was observed across subdistricts and schools, with R0 ranging between 1.7 and 6.8. These findings are consistent with the idea that the observed age shift might be a consequence of the demographic transition in Thailand. Changes in critical vaccination fractions, estimated by using R0, have not accompanied the increase in age at exposure. These results have implications for dengue control interventions because multiple countries in Southeast Asia are undergoing similar demographic transitions. It is likely that dengue will never again be a disease exclusively of children.

Epidemiology of and impact of insecticide spraying on Chagas disease in communities in the Bolivian Chaco

Background

Chagas disease control campaigns relying upon residual insecticide spraying have been successful in many Southern American countries. However, in some areas, rapid reinfestation and recrudescence of transmission have occurred.

Methodology/Principal Findings

We conducted a cross-sectional survey in the Bolivian Chaco to evaluate prevalence of and risk factors for T. cruzi infection 11 years after two rounds of blanket insecticide application. We used a cubic B-spline model to estimate change in force of infection over time based on age-specific seroprevalence data. Overall T. cruzi seroprevalence was 51.7%. The prevalence was 19.8% among children 2–15, 72.7% among those 15–30 and 97.1% among participants older than 30 years. Based on the model, the estimated annual force of infection was 4.3% over the two years before the first blanket spray in 2000 and fell to 0.4% for 2001–2002. The estimated annual force of infection for 2004–2005, the 2 year period following the second blanket spray, was 4.6%. However, the 95% bootstrap confidence intervals overlap for all of these estimates. In a multivariable model, only sleeping in a structure with cracks in the walls (aOR = 2.35; 95% CI = 1.15–4.78), age and village of residence were associated with infection.

Conclusions/Significance

As in other areas in the Chaco, we found an extremely high prevalence of Chagas disease. Despite evidence that blanket insecticide application in 2000 may have decreased the force of infection, active transmission is ongoing. Continued spraying vigilance, infestation surveillance, and systematic household improvements are necessary to disrupt and sustain interruption of infection transmission.

Despite significant gains in the reduction of the burden of Chagas disease in many South American countries, active transmission and significant burden remain in areas such as the Gran Chaco. High initial vector density, poor housing material, peridomestic infestation, insecticide resistance, and a lack of systematic insecticide spraying and vector surveillance have previously been incriminated for failure to interrupt and sustain interruption of transmission. We conducted a census, seroprevalence, and epidemiologic study in a rural area in the Bolivian Chaco. The prevalence of infection was almost 20% in children and over 80% in adults. We estimated the intensity of transmission over time based on infection prevalence by age. We found that after the first spray program, transmission appeared to fall transiently but then increased again quickly. Sleeping in a structure with cracks in the walls, age and village of residence were associated with increased likelihood of infection. These findings suggest that consistently repeated systematic spraying campaigns accompanied by housing improvements are necessary to interrupt and sustain interruption of vector-borne transmission.

An application of Bayesian growth mixture modelling to estimate infection incidences from repeated serological tests

Diagnoses of infectious diseases are often performed using antibody detection through enzyme-linked immunosorbent assay techniques. These data are usually dichotomized into positive and negative samples using a fixed cut-off and prevalences of infection are subsequently estimated assuming perfect correspondence between the dichotomized test results and infection status. In contrast to this approach, in this case study, we estimate the effect of distributing insecticide impregnated bednets to prevent Leishmania infection through mixture modelling of the original continuous data. We analyze the data from a cluster randomized intervention trial using a generalized latent variable model consisting of a longitudinal mixture model for the observed outcome and a Hidden Markov model for the underlying unobserved disease status to estimate the effect of an intervention. The response and structural models are jointly estimated in a Bayesian framework. This model has the advantage that it avoids the need to choose an arbitrary cut-off and allows for uncertainty in the infection status. In this paper, we describe the development of the model and selection of priors, the application to the motivating data, model checking and simulation results.

Deriving age-specific incidence from prevalence with an ordinary differential equation

This article describes new relationships between the age-specific incidence of, the prevalence of and mortality from a chronic disease. We express these relationships in terms of an ordinary differential equation and form the methodological basis for a novel approach to estimating incidences from age-specific prevalence data. We examine practical aspects of the relationships and a comparison with a known stochastic method in a simulation study. Finally, we apply the novel method to a data set of renal replacement therapy recorded from patients with chronic kidney failure in a region of Germany with approximately 310,000 inhabitants from 2002 to 2010.

Estimating the force of infection for HCV in injecting drug users using interval-censored data

Injecting drug users (IDUs) account for most new HCV infections. The objectives of this study were: to estimate the force of infection for hepatitis C virus in IDUs within the interval-censoring framework and to determine the impact of risk factors such as frequency of injection, drug injected, sharing of syringes and time of first injection on the time to HCV infection. We used data from the Amsterdam Cohort Study collected in The Netherlands and focused on those individuals who were HCV negative upon entry into the study. Based on the results, the force of infection was found to vary with time of first injection. The risk of infection was higher in the first 3 years of an IDU's career, implying estimates based on single cross-sectional studies could be biased. Frequency of injection and type of drug injected were found to be highly significant predictors, whereas sharing syringes was not.

Identifying the age cohort responsible for transmission in a natural outbreak of Bordetella bronchiseptica

Identifying the major routes of disease transmission and reservoirs of infection are needed to increase our understanding of disease dynamics and improve disease control. Despite this, transmission events are rarely observed directly. Here we had the unique opportunity to study natural transmission of Bordetella bronchiseptica--a directly transmitted respiratory pathogen with a wide mammalian host range, including sporadic infection of humans--within a commercial rabbitry to evaluate the relative effects of sex and age on the transmission dynamics therein. We did this by developing an a priori set of hypotheses outlining how natural B. bronchiseptica infections may be transmitted between rabbits. We discriminated between these hypotheses by using force-of-infection estimates coupled with random effects binomial regression analysis of B. bronchiseptica age-prevalence data from within our rabbit population. Force-of-infection analysis allowed us to quantify the apparent prevalence of B. bronchiseptica while correcting for age structure. To determine whether transmission is largely within social groups (in this case litter), or from an external group, we used random-effect binomial regression to evaluate the importance of social mixing in disease spread. Between these two approaches our results support young weanlings--as opposed to, for example, breeder or maternal cohorts--as the age cohort primarily responsible for B. bronchiseptica transmission. Thus age-prevalence data, which is relatively easy to gather in clinical or agricultural settings, can be used to evaluate contact patterns and infer the likely age-cohort responsible for transmission of directly transmitted infections. These insights shed light on the dynamics of disease spread and allow an assessment to be made of the best methods for effective long-term disease control.