How host heterogeneity governs tuberculosis reinfection?

Recurrent episodes of tuberculosis (TB) can be due to relapse of latent infection or exogenous reinfection, and discrimination is crucial for control planning. Molecular genotyping of Mycobacterium tuberculosis isolates offers concrete opportunities to measure the relative contribution of reinfection in recurrent disease. Here, a mathematical model of TB transmission is fitted to data from 14 molecular epidemiology studies, enabling the estimation of relevant epidemiological parameters. Meta-analysis reveals that rates of reinfection after successful treatment are higher than rates of new TB, raising an important question about the underlying mechanism. We formulate two alternative mechanisms within our model framework: (i) infection increases susceptibility to reinfection or (ii) infection affects individuals differentially, thereby recruiting high-risk individuals to the group at risk for reinfection. The second mechanism is better supported by the fittings to the data, suggesting that reinfection rates are inflated through a population phenomenon that occurs in the presence of heterogeneity in individual risk of infection. As a result, rates of reinfection are higher when measured at the population level even though they might be lower at the individual level. Finally, differential host recruitment is modulated by transmission intensity, being less pronounced when incidence is high.     

Implications of partial immunity on the prospects for tuberculosis control by post-exposure interventions

One-third of the world population (approximately 2 billion individuals) is currently infected with Mycobacterium tuberculosis, the vast majority harboring a latent infection. As the risk of reactivation is around 10% in a lifetime, it follows that 200 million of these will eventually develop active pulmonary disease. Only therapeutic or post-exposure interventions can tame this vast reservoir of infection. Treatment of latent infections can reduce the risk of reactivation, and there is accumulating evidence that combination with post-exposure vaccines can reduce the risk of reinfection. Here we develop mathematical models to explore the potential of these post-exposure interventions to control tuberculosis on a global scale. Intensive programs targeting recent infections appear generally effective, but the benefit is potentially greater in intermediate prevalence scenarios. Extending these strategies to longer-term persistent infections appears more beneficial where prevalence is low. Finally, we consider that susceptibility to reinfection is altered by therapy, and explore its epidemiological consequences. When we assume that therapy reduces susceptibility to subsequent reinfection, catastrophic dynamics are observed. Thus, a bipolar outcome is obtained, where either small or large reductions in prevalence levels result, depending on the rate of detection and treatment of latent infections. By contrast, increased susceptibility after therapy may induce an increase in disease prevalence and does not lead to catastrophic dynamics. These potential outcomes are silent unless a widespread intervention is implemented.     

A model for tuberculosis with exogenous reinfection

Following primary tuberculosis (TB) infection, only approximately 10% of individuals develop active T.B. Most people are assumed to mount an effective immune response to the initial infection that limits proliferation of the bacilli and leads to long-lasting partial immunity both to further infection and to reactivation of latent bacilli remaining from the original infection. Infected individuals may develop active TB as a consequence of exogenous reinfection, i.e., acquiring a new infection from another infectious individual. Our results in this paper suggest that exogenous reinfection has a drastic effect on the qualitative dynamics of TB. The incorporation of exogenous reinfection into our TB model allows the possibility of a subcritical bifurcation at the critical value of the basic reproductive number R(0)=1, and hence the existence of multiple endemic equilibria for R(0)<1 and the exogenous reinfection rate larger than a threshold. Our results suggest that reducing R(0) to be smaller than one may not be sufficient to eradicate the disease. An additional reduction in reinfection rate may be required. These results may also partially explain the recently observed resurgence of TB.     

Analysis of Complex Patterns of Human Exposure and Immunity to Schistosomiasis mansoni: The Influence of Age,Sex, Ethnicity and IgE

Background

Numerous factors may influence Schistosoma infection intensity and prevalence within endemic communities, including exposure-related factors such as local environment and behaviour, and factors relating to susceptibility to infection such as immunology and genetics. While animal studies performed in the laboratory can be tightly controlled, human populations are highly heterogeneous, varying according to demographic characteristics, genetic background and exposure to infection. The heterogeneous nature of human water contact behaviour in particular makes it difficult to distinguish between a lack of cercarial exposure and reduced susceptibility to infection as the cause for low levels of infection in the field.

Methods and Principal Findings

In this study we investigate risk factors for Schistosoma mansoni infection in a rural Ugandan fishing community receiving treatment as part of a multi-disciplinary longitudinal reinfection study. More specifically, we examine the influence that age, sex and ethnic background have on susceptibility to reinfection after anti-helminth drug treatment, but use individual estimates of cercarial exposure and multivariable methods in an attempt to remove noise created by environmental and behavioural heterogeneities. We then investigate whether schistosome-specific IgE immune responses could account for any remaining variations in susceptibility to reinfection. Our findings suggest that observed ethnic- and sex-related variations in S. mansoni reinfection were due to variations in cercarial exposure, as opposed to biological differences in susceptibility to infection. Age-related differences in reinfection were not explained by exposure, however, and appeared linked to the balance of IgE and IgG₄ to the tegumental antigen SmTAL1 (formerly Sm22.6), which itself was significantly related to resistance to reinfection.

Conclusions

This study highlights the benefit of taking a multidisciplinary approach in complex field settings; it allows the ecology of a population to be understood and thus more robust conclusions to be made.     

The importance of environmental spatial heterogeneity and host social structure in disease transmission

[First paragraph]The spatial structure of a host population determines the spatial probability distribution of interaction between individuals, and therefore influences the spatio-temporal dynamics of disease transmission within the host population (Keeling, 1999; Gudelj and White, 2004). Nigel Barlow recognised this and included non-linear transmission in his later models (Barlow, 1991), simulating the result of spatial heterogeneity of risk in susceptible hosts. These models produced behaviour that could not be found in models with homogeneously mixed host populations: more rapid disease dynamics and a greater robustness of disease to control measures. However, in this model there was no causal mechanism driving the initial spatial heterogeneity of risk in host individuals. Environmental heterogeneity is likely to be a key factor in determining the spatial distribution of host individuals (Cronin and Reeve, 2005). We attempted to explore how environmental heterogeneity may affect disease dynamics via its influence on the spatial distribution of host individuals. We developed a spatially explicit stochastic model that incorporated spatially variable host density distributions, primarily driven by environmental heterogeneity.     

The effects of females’ susceptibility on the coexistence of multiple pathogen strains of sexually transmitted diseases

 We study the dynamics of sexually transmitted pathogens in a heterosexually active population, where females are divided into two different groups based on their susceptibility to two distinct pathogenic strains. It is assumed that a host cannot be invaded simultaneously by both disease agents and that when symptoms appear – a function of the pathogen, strain, virulence, and an individual’s degree of susceptibility – then individuals are treated and/or recover. Heterogeneity in susceptibility to the acquisition of infection and/or in variability in the length of the infection period of the female subpopulations is incorporated. Pathogens’ coexistence is highly unlikely on homogeneously mixing female and male populations with no heterogeneity among individuals of either gender. Variability in susceptibility in the female subpopulation makes coexistence possible albeit under a complex set of circumstances that must include differences in contact/mixing rates between the groups of females and the male population as well as differences in the lengths of their average periods of infectiousness for the three groups. Received 25 July 1995; received in revised form 6 May 1996     

Vaccination based control of infections in SIRS models with reinfection: special reference to pertussis

The aim of this paper is to study the impact of introducing a partially protective vaccine on the dynamics of infection in SIRS models where primary and secondary infections are distinguished. We investigate whether a public health strategy based solely on vaccinating a proportion of newborns can lead to an effective control of the disease. In addition to carrying out the qualitative analysis, the findings are further explained by numerical simulations. The model exhibits backward bifurcation for certain values of the parameters. In these cases the standard basic reproduction number (obtained by inspection of the uninfected state) is not significant. The key threshold is the reinfection level which depends on the relative transmissibility (susceptibility) of secondary, with respect to primary, infected (susceptible) individuals and the relative loss of immunity of vaccinated, with respect to recovered, individuals. If one or all of these ratios decrease, then the threshold increases which increases the possibility to contain the infection by vaccination. The analysis shows further that symptomatic infections can be eliminated by vaccination solely.     

Explaining rapid reinfections in multiple-wave influenza outbreaks: Tristan da Cunha 1971 epidemic as a case study

Influenza usually spreads through the human population in multiple-wave outbreaks. Successive reinfection of individuals over a short time interval has been explicitly reported during past pandemics. However, the causes of rapid reinfection and the role of reinfection in driving multiple-wave outbreaks remain poorly understood. To investigate these issues, we focus on a two-wave influenza A/H3N2 epidemic that occurred on the remote island of Tristan da Cunha in 1971. Over 59 days, 273 (96%) of 284 islanders experienced at least one attack and 92 (32%) experienced two attacks. We formulate six mathematical models invoking a variety of antigenic and immunological reinfection mechanisms. Using a maximum-likelihood analysis to confront model predictions with the reported incidence time series, we demonstrate that only two mechanisms can be retained: some hosts with either a delayed or deficient humoral immune response to the primary influenza infection were reinfected by the same strain, thus initiating the second epidemic wave. Both mechanisms are supported by previous empirical studies and may arise from a combination of genetic and ecological causes. We advocate that a better understanding and account of heterogeneity in the human immune response are essential to analysis of multiple-wave influenza outbreaks and pandemic planning.     

SARS-CoV-2 antibodies protect against reinfection for at least 6 months in a multicentre seroepidemiological workplace cohort

Identifying the potential for SARS-CoV-2 reinfection is crucial for understanding possible long-term epidemic dynamics. We analysed longitudinal PCR and serological testing data from a prospective cohort of 4,411 United States employees in 4 states between April 2020 and February 2021. We conducted a multivariable logistic regression investigating the association between baseline serological status and subsequent PCR test result in order to calculate an odds ratio for reinfection. We estimated an odds ratio for reinfection ranging from 0.14 (95% CI: 0.019 to 0.63) to 0.28 (95% CI: 0.05 to 1.1), implying that the presence of SARS-CoV-2 antibodies at baseline is associated with around 72% to 86% reduced odds of a subsequent PCR positive test based on our point estimates. This suggests that primary infection with SARS-CoV-2 provides protection against reinfection in the majority of individuals, at least over a 6-month time period. We also highlight 2 major sources of bias and uncertainty to be considered when estimating the relative risk of reinfection, confounders and the choice of baseline time point, and show how to account for both in reinfection analysis.

Identifying the potential for SARS-CoV-2 reinfection is crucial for understanding possible long-term epidemic dynamics. Analysis of a seroepidemiological cohort suggests that primary infection with SARS-CoV-2 protects against reinfection in the majority of individuals, at least over a six month period.     

Natural and Experimental Helicobacter mustelae Reinfection Following Successful Antimicrobial Eradication in Ferrets

Background. Recrudescence or reinfection may occur after eradication of Helicobacter pylori in humans.
Materials and Methods. We used the ferret Helicobacter mustelae model to investigate the effect of prior infection and eradication on reinfection by experimental and natural routes. Two groups of ferrets with naturally acquired H. mustelae infection were treated with an eradication protocol using amoxicillin, metronidazole, and bismuth subsalicylate. The ferrets were monitored for recrudescence by repeated cultures of endoscopic gastric mucosal biopsies. The ferrets were challenged at 17 months (group I) and 6 months (group II) after eradication with a strain of H. mustelae having a distinctive restriction endonuclease analysis pattern. The eradication protocol was repeated to eliminate the infection produced by experimental challenge. The ferrets were then cohoused intermittently with naturally infected ferrets.
Results. The original H. mustelae infection was successfully eliminated by the eradication protocol. No recrudescence was observed in group I for 12 months nor for 3 months in group II after eradication. All ferrets became persistently reinfected with the challenge strain. The infection from the challenge strain was eradicated successfully. No ferrets in group I and all ferrets in group II became infected through cohousing.
Conclusions. These results suggest that though prior infection with H. mustelae may confer some protection against reinfection, such protection is not universal in all circumstances; that susceptibility to reinfection by contact with infected animals varies between individuals; and that age may be a factor in this individual variability. These results are applicable to studies of reinfection after eradication of H. pylori in humans.     

Analysis of Social and Genetic Factors Influencing Heterosexual Transmission of HIV within Serodiscordant Couples in the Henan Cohort

There is considerable variability between individuals in susceptibility to infection by human immunodeficiency virus (HIV). Many social, clinical and genetic factors are known to contribute to the likelihood of HIV transmission, but there is little consensus on the relative importance and potential interaction of these factors. Additionally, recent studies of several variants in chemokine receptors have identified alleles that may be predictive of HIV transmission and disease progression; however the strengths and directions of the associations of these genetic markers with HIV transmission have markedly varied between studies. To better identify factors that predict HIV transmission in a Chinese population, 180 cohabiting serodiscordant couples were enrolled for study by the Henan Center for Disease Prevention and Control, and transmission and progression of HIV infection were regularly measured. We found that anti-retroviral therapy, education level, and condom use were the most significant factors in determining likelihood of HIV transmission in this study. We also assessed ten variants in three genes (CXCL12, CCR2, and CCR5) that have been shown to influence HIV transmission. We found two tightly linked variants in CCR2 and CCR5, rs1799864 and rs1800024, have a significant positive association with transmission as recessive models (OR>10, P value=0.011). Mixed effects models showed that these genetic variants both retained significance when assessed with either treatment or condom use. These markers of transmission susceptibility may therefore serve to help stratify individuals by risk for HIV transmission.     

Persistence of Tb in possums: Insights from a spatially explicit, stochastic model

The majority of models for predicting the dynamics of bovine Tb in brushtail possums in New Zealand owe much to the pioneering work of the late Nigel Barlow. These non-spatial, deterministic models subsumed local disease dynamics by using a heterogeneous mixing term that assumed Tb was confined to a fraction of the population (in patches). However, the underlying mechanism(s) that could result in this heterogeneity of infection risk were obscure. We present a new individual-based, spatial, stochastic simulation model of Tb in possums that provides an explicit mechanism for simulating heterogeneous risk of infection based on a model of individual home range utilisation and disease susceptibility. The manipulation of parameters governing individual utilisation of space also means that processes such as non-linear contact structure can be handled naturally. We use the model to predict the persistence of Tb in possums under scenarios currently implemented for the management of bovine Tb in wildlife and determine conditions under which Tb might be predicted to persist despite control efforts.     

Host-pathogen interactions, insect outbreaks, and natural selection for disease resistance

The theory of insect population dynamics has shown that heterogeneity in natural-enemy attack rates is strongly stabilizing. We tested the usefulness of this theory for outbreaking insects, many of which are attacked by infectious pathogens. We measured heterogeneity among gypsy moth larvae in their risk of infection with a nucleopolyhedrovirus, which is effectively heterogeneity in the pathogen's attack rate. Our data show that heterogeneity in infection risk in this insect is so high that it leads to a stable equilibrium in the models, which is inconsistent with the outbreaks seen in North American gypsy moth populations. Our data further suggest that infection risk declines after epidemics, in turn suggesting that the model assumption of constant infection risk is incorrect. We therefore constructed an alternative model in which natural selection drives fluctuations in infection risk, leading to reductions after epidemics because of selection for resistance and increases after epidemics because of a cost of resistance. This model shows cycles even for high heterogeneity, and experiments confirm that infection risk is indeed heritable. The model is very general, and so we argue that natural selection for disease resistance may play a role in many insect outbreaks.     

Microbiome-immune interactions in tuberculosis

Tuberculosis (TB) remains an infectious disease of global significance and a leading cause of death in low- and middle-income countries. Significant effort has been directed towards understanding Mycobacterium tuberculosis genomics, virulence, and pathophysiology within the framework of Koch postulates. More recently, the advent of “-omics” approaches has broadened our appreciation of how “commensal” microbes have coevolved with their host and have a central role in shaping health and susceptibility to disease. It is now clear that there is a diverse repertoire of interactions between the microbiota and host immune responses that can either sustain or disrupt homeostasis. In the context of the global efforts to combatting TB, such findings and knowledge have raised important questions: Does microbiome composition indicate or determine susceptibility or resistance to M. tuberculosis infection? Is the development of active disease or latent infection upon M. tuberculosis exposure influenced by the microbiome? Does microbiome composition influence TB therapy outcome and risk of reinfection with M. tuberculosis? Can the microbiome be actively managed to reduce risk of M. tuberculosis infection or recurrence of TB? Here, we explore these questions with a particular focus on microbiome-immune interactions that may affect TB susceptibility, manifestation and progression, the long-term implications of anti-TB therapy, as well as the potential of the host microbiome as target for clinical manipulation.     

Risk Factors for Visceral Leishmaniasis and Asymptomatic Leishmania donovani Infection in India and Nepal

There is increasing interest in the role of asymptomatic infection in transmission of Visceral Leishmaniasis (VL). We studied the individual, household and environmental factors associated with asymptomatic Leishmania donovani infected individuals and VL. 7,538 individuals living in VL endemic villages in India and Nepal were divided into three mutually exclusive groups based on their VL history and Direct Agglutination Test (DAT) results in yearly serosurveys over a two-year period. The groups were (1) VL cases, (2) asymptomatically infected individuals (seroconverters) and (3) seronegative individuals. VL cases and seroconverters were compared to seronegative individuals in mixed logistic regression models. The risk of seroconversion and disease was significantly increased in individuals aged 14 to 24 years old and by the presence of other DAT-positive, asymptomatically infected individuals and VL cases in the house. The risk of seroconversion was higher in Indian than in Nepalese villages and it increased significantly with age, but not so for VL. This study demonstrates that, when risk factors for leishmanial infection and VL disease are evaluated in the same population, epidemiological determinants for asymptomatic infection and VL are largely similar.     

Development of resistance to reinfection by Clonorchis sinensis in rats

We investigated the induction of resistance to Clonorchis sinensis infection by prior infection in rat and hamster models. Animals were challenged with C. sinensis metacercariae, then treated with praziquantel and reinfected. Worm recovery rate in reinfected animals was used to estimate resistance to reinfection. The determined resistance rates to reinfection in rats and hamsters were 97.7% and 10.3%, respectively. In rats, cure from the primary infection of C. sinensis increased resistant to reinfection, and the greater the worm burden and the longer the duration of primary infection, the higher was the resistance rate. For primary infection doses of 10, 40 and 100 metacercariae per rat, the resistance rates were 87.4%, 93.8% and 98.4%, respectively. The resistance rates in rats after 2 or 8-week primary infection were 78.7% and 95.3%, respectively. All worms recovered from reinfected rats were immature. When cured rats were administered with methylprednisolone, resistance to reinfection became impaired. These findings indicate that rats develop a high degree of resistance to reinfection by C. sinensis after cure. The growths and maturations of reinfected worms were also impaired.     

Covariation between the physiological and behavioral components of pathogen transmission: host heterogeneity determines epidemic outcomes

Although heterogeneity in contact rate, physiology, and behavioral response to infection have all been empirically demonstrated in host–pathogen systems, little is known about how interactions between individual variation in behavior and physiology scale‐up to affect pathogen transmission at a population level. The objective of this study is to evaluate how covariation between the behavioral and physiological components of transmission might affect epidemic outcomes in host populations. We tested the consequences of contact rate covarying with susceptibility, infectiousness, and infection status using an individual‐based, dynamic network model where individuals initiate and terminate contacts with conspecifics based on their behavioral predispositions and their infection status. Our results suggest that both heterogeneity in physiology and subsequent covariation of physiology with contact rate could powerfully influence epidemic dynamics. Overall, we found that 1) individual variability in susceptibility and infectiousness can reduce the expected maximum prevalence and increase epidemic variability; 2) when contact rate and susceptibility or infectiousness negatively covary, it takes substantially longer for epidemics to spread throughout the population, and rates of epidemic spread remained suppressed even for highly transmissible pathogens; and 3) reductions in contact rate resulting from infection‐induced behavioral changes can prevent the pathogen from reaching most of the population. These effects were strongest for theoretical pathogens with lower transmissibility and for populations where the observed variation in contact rate was higher, suggesting that such heterogeneity may be most important for less infectious, more chronic diseases in wildlife. Understanding when and how variability in pathogen transmission should be modelled is a crucial next step for disease ecology.     

Mycobacterial ecology as a modulator of tuberculosis vaccine success

Natural infection with Mycobacterium tuberculosis, as well as cross-immune reactions with the constituent of standard vaccines, attenuated M. bovis, and other species of mycobacteria confer partial immunity to subsequent M. tuberculosis infection. It has been shown in the past that the immune response to mycobacteria found naturally in the environment reduces the benefit of vaccination as assessed by means of vaccine efficacy. In this paper we show that efficacy is a poor measure of the potential success of new anti-tuberculous vaccines due to its inability to account for the relative weight of reinfection in disease dynamics. We advocate instead the use of vaccine effectiveness when evaluating the impact of new control methods against infections that confer partial immunity. Through the study of a simple model that incorporates cross-reactive responses to environmental mycobacteria (EM) and reinfection, we show how the particulars of the relation between EM abundance and vaccine effectiveness depend on the degree of protection conferred respectively by natural infection, vaccination and EM. The relative importance of reinfection as a transmission mechanism comes up as the most important source of variability in vaccine effectiveness. Our results suggest that control efforts should be placed in reducing the importance of reinfection through diminishing transmission rates. Vaccines that overcome preexisting immunity to other mycobacteria will still have varying degrees of success depending on the underlying rate of TB transmission.     

Monkeys in the Middle: Parasite Transmission through the Social Network of a Wild Primate

In wildlife populations, group-living is thought to increase the probability of parasite transmission because contact rates increase at high host densities. Physical contact, such as social grooming, is an important component of group structure, but it can also increase the risk of exposure to infection for individuals because it provides a mechanism for transmission of potentially pathogenic organisms. Living in groups can also create variation in susceptibility to infection among individuals because circulating levels of immunosuppressive hormones like glucocorticoids often depend on an individual’s position within the group’s social structure. Yet, little is known about the relative roles of socially mediated exposure versus susceptibility in parasite transmission among free-living animal groups. To address this issue, we investigate the relationship between host dominance hierarchy and nematode parasite transmission among females in a wild group of Japanese macaques (Macaca fuscata yakui). We use social network analysis to describe each individual female’s position within the grooming network in relation to dominance rank and relative levels of infection. Our results suggest that the number of directly-transmitted parasite species infecting each female, and the relative amount of transmission stages that one of these species sheds in faeces, both increase with dominance rank. Female centrality within the network, which shows positive associations with dominance hierarchy, is also positively associated with infection by certain parasite species, suggesting that the measured rank-bias in transmission may reflect variation in exposure rather than susceptibility. This is supported by the lack of a clear relationship between rank and faecal cortisol, as an indicator of stress, in a subset of these females. Thus, socially mediated exposure appears to be important for direct transmission of nematode parasites, lending support to the idea that a classical fitness trade-off inherent to living in groups can exist.     

Parameterizing Spatial Models of Infectious Disease Transmission that Incorporate Infection Time Uncertainty Using Sampling-Based Likelihood Approximations

A class of discrete-time models of infectious disease spread, referred to as individual-level models (ILMs), are typically fitted in a Bayesian Markov chain Monte Carlo (MCMC) framework. These models quantify probabilistic outcomes regarding the risk of infection of susceptible individuals due to various susceptibility and transmissibility factors, including their spatial distance from infectious individuals. The infectious pressure from infected individuals exerted on susceptible individuals is intrinsic to these ILMs. Unfortunately, quantifying this infectious pressure for data sets containing many individuals can be computationally burdensome, leading to a time-consuming likelihood calculation and, thus, computationally prohibitive MCMC-based analysis. This problem worsens when using data augmentation to allow for uncertainty in infection times. In this paper, we develop sampling methods that can be used to calculate a fast, approximate likelihood when fitting such disease models. A simple random sampling approach is initially considered followed by various spatially-stratified schemes. We test and compare the performance of our methods with both simulated data and data from the 2001 foot-and-mouth disease (FMD) epidemic in the U.K. Our results indicate that substantial computation savings can be obtained—albeit, of course, with some information loss—suggesting that such techniques may be of use in the analysis of very large epidemic data sets.