The immunoepidemiology of nematode parasites of farmed animals: a mathematical approach

An explicit immunogenetic model of gastrointestinal nematode infection in sheep

Gastrointestinal nematodes are a global cause of disease and death in humans, wildlife and livestock. Livestock infection has historically been controlled with anthelmintic drugs, but the development of resistance means that alternative controls are needed. The most promising alternatives are vaccination, nutritional supplementation and selective breeding, all of which act by enhancing the immune response. Currently, control planning is hampered by reliance on the faecal egg count (FEC), which suffers from low accuracy and a nonlinear and indirect relationship with infection intensity and host immune responses. We address this gap by using extensive parasitological, immunological and genetic data on the sheep–Teladorsagia circumcincta interaction to create an immunologically explicit model of infection dynamics in a sheep flock that links host genetic variation with variation in the two key immune responses to predict the observed parasitological measures. Using our model, we show that the immune responses are highly heritable and by comparing selective breeding based on low FECs versus high plasma IgA responses, we show that the immune markers are a much improved measure of host resistance. In summary, we have created a model of host–parasite infections that explicitly captures the development of the adaptive immune response and show that by integrating genetic, immunological and parasitological understanding we can identify new immune-based markers for diagnosis and control.

A mathematical modelling framework for linked within-host and between-host dynamics for infections with free-living pathogens in the environment

In this study we develop a mathematical modelling framework for linking the within-host and between-host dynamics of infections with free-living pathogens in the environment. The resulting linked models are sometimes called immuno-epidemiological models. However, there is still no generalised framework for linking the within-host and between-host dynamics of infectious diseases. Furthermore, for infections with free-living pathogens in the environment, there is an additional stumbling block in that there is a gap in knowledge on how environmental factors (through water, air, soil, food, fomites, etc.) alter many aspects of such infections including susceptibility to infective dose, persistence of infection, pathogen shedding and severity of the disease. In this work, we link the two subsystems (within-host and between-host models) by identifying the within-host and between-host variables and parameters associated with the environmental dynamics of the pathogen and then design a feedback of the variables and parameters across the within-host and between-host models using human schistosomiasis as a case study. We study the mathematical properties of the linked model and show that the model is epidemiologically well-posed. Using results from the analysis of the endemic equilibrium expression, the disease reproductive number R0, and numerical simulations of the full model, we adequately account for the reciprocal influence of the linked within-host and between-host models. In particular, we illustrate that for human schistosomiasis, the outcome of infection at the individual level determines if, when and how much the individual host will further transmit the infectious agent into the environment, eventually affecting the spread of the infection in the host population. We expect the conceptual modelling framework developed here to be applicable to many infectious disease with free-living pathogens in the environment beyond the specific disease system of human schistosomiasis considered here.

Exploiting parallels between livestock and wildlife: Predicting the impact of climate change on gastrointestinal nematodes in ruminants

Global change, including climate, policy, land use and other associated environmental changes, is likely to have a major impact on parasitic disease in wildlife, altering the spatio-temporal patterns of transmission, with wide-ranging implications for wildlife, domestic animals, humans and ecosystem health. Predicting the potential impact of climate change on parasites infecting wildlife will become increasingly important in the management of species of conservation concern and control of disease at the wildlife-livestock and wildlife-human interface, but is confounded by incomplete knowledge of host-parasite interactions, logistical difficulties, small sample sizes and limited opportunities to manipulate the system. By exploiting parallels between livestock and wildlife, existing theoretical frameworks and research on livestock and their gastrointestinal nematodes can be adapted to wildlife systems. Similarities in the gastrointestinal nematodes and the life-histories of wild and domestic ruminants, coupled with a detailed knowledge of the ecology and life-cycle of the parasites, render the ruminant-GIN host-parasite system particularly amenable to a cross-disciplinary approach.

AN EPIDEMIC MODEL STRUCTURED BY HOST IMMUNITY

In this paper we introduce a physiologically structured SIR epidemic model where the individuals are distributed according to their immune status. An individual immune status is assumed to increase during the infectious period and remain unchanged after the recovery. Recovered individuals can become reinfected at a rate which is a decreasing function of their immune status. We find that the possibility of reinfection of recovered individuals results in subthreshold endemic equilibria. The differential immunity of the infectious individuals leads to multiple nontrivial equilibria in the superthreshold case. We present an example that has exactly three nontrivial equilibria. We also analyze the local stability of equilibria.

Immune regulation of a chronic bacteria infection and consequences for pathogen transmission

Background

The role of host immunity has been recognized as not only playing a fundamental role in the interaction between the host and pathogen but also in influencing host infectiousness and the ability to shed pathogens. Despite the interest in this area of study, and the development of theoretical work on the immuno-epidemiology of infections, little is known about the immunological processes that influence pathogen shedding patterns.

Results

We used the respiratory bacterium Bordetella bronchiseptica and its common natural host, the rabbit, to examine the intensity and duration of oro-nasal bacteria shedding in relation to changes in the level of serum antibodies, blood cells, cytokine expression and number of bacteria colonies in the respiratory tract. Findings show that infected rabbits shed B. bronchiseptica by contact up to 4.5 months post infection. Shedding was positively affected by number of bacteria in the nasal cavity (CFU/g) but negatively influenced by serum IgG, which also contributed to the initial reduction of bacteria in the nasal cavity. Three main patterns of shedding were identified: i- bacteria were shed intermittently (46% of individuals), ii- bacteria shedding fell with the progression of the infection (31%) and iii- individuals never shed bacteria despite being infected (23%). Differences in the initial number of bacteria shed between the first two groups were associated with differences in the level of serum antibodies and white blood cells. These results suggest that the immunological conditions at the early stage of the infection may play a role in modulating the long term dynamics of B. bronchiseptica shedding.

Conclusions

We propose that IgG influences the threshold of bacteria in the oro-nasal cavity which then affects the intensity and duration of individual shedding. In addition, we suggest that a threshold level of infection is required for shedding, below this value individuals never shed bacteria despite being infected. The mechanisms regulating these interactions are still obscure and more studies are needed to understand the persistence of bacteria in the upper respiratory tract and the processes controlling the intensity and duration of shedding.

Worms and germs: the population dynamic consequences of microparasite-macroparasite co-infection

Hosts are typically simultaneously co-infected by a variety of microparasites (e.g. viruses and bacteria) and macroparasites (e.g. parasitic helminths). However, the population dynamical consequences of such co-infections and the implications for the effectiveness of imposed control programmes have yet to be fully realised. Mathematical models may provide an important framework for exploring such issues and have proved invaluable in helping to understand the factors affecting the epidemiology of single parasitic infections. Here the first population dynamic model of microparasite-macroparasite co-infection is presented and used to explore how co-infection alters the predictions of the existing single-species models. It is shown that incorporating an additional parasite species into existing models can greatly stabilise them, due to the combined density-dependent impacts on the host population, but co-infection can also restrict the region of parameter space where each species could persist alone. Overall it is concluded that the dynamic feedback between host, microparasite and macroparasite means that it is difficult to appreciate the factors affecting parasite persistence and predict the effectiveness of control by just studying one component in isolation.

Stochastic and spatial dynamics of nematode parasites in farmed ruminants

Host-parasite systems provide powerful opportunities for the study of spatial and stochastic effects in ecology; this has been particularly so for directly transmitted microparasites. Here, we construct a fully stochastic model of the population dynamics of a macroparasite system: trichostrongylid gastrointestinal nematode parasites of farmed ruminants. The model subsumes two implicit spatial effects: the host population size (the spatial extent of the interaction between hosts) and spatial heterogeneity ('clumping') in the infection process. This enables us to investigate the roles of several different processes in generating aggregated parasite distributions. The necessity for female worms to find a mate in order to reproduce leads to an Allee effect, which interacts nonlinearly with the stochastic population dynamics and leads to the counter-intuitive result that, when rare, epidemics can be more likely and more severe in small host populations. Clumping in the infection process reduces the strength of this Allee effect, but can hamper the spread of an epidemic by making infection events too rare. Heterogeneity in the hosts' response to infection has to be included in the model to generate aggregation at the level observed empirically.

Spatial parasite transmission, drug resistance, and the spread of rare genes

The transmission of many parasitic worms involves aggregated movement between hosts of "packets" of infectious larvae. We use a generic metapopulation model to show that this aggregation naturally promotes the preferential spread of rare recessive genes, compared with the expectations of traditional nonspatial models. A more biologically realistic model also demonstrates that this effect could explain the rapid observed spread of recessive or weakly dominant drug-resistant genotypes in nematode parasites of sheep. This promotion of a recessive trait arises from a novel mechanism of inbreeding arising from the metapopulation dynamics of transmission.

Immunoepidemiology--bridging the gap between immunology and epidemiology

'Immunoepidemiology' combines individual- and population-oriented approaches to create new perspectives. It examines how inter-individual differences in immune responses affect the population dynamics of micro- and macro-parasites to produce the epidemiological patterns of infection observed in heterogeneous host populations. Here, I discuss how research has only just begun to tap the potential of this integrative discipline that incorporates immunology, parasitology, genetics, epidemiology, ecology, mathematical modelling and statistics.