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We develop and apply analytically tractable generative models of livestock movements at national scale. These go beyond current models through mechanistic modelling of heterogeneous trade partnership network dynamics and the trade events that occur on them. Linking resulting animal movements to disease transmission between farms yields analytical expressions for the basic reproduction number R 0 . We show how these novel modelling tools enable systems approaches to disease control, using R 0 to explore impacts of changes in trading practices on between-farm prevalence levels. Using the Scottish cattle trade network as a case study, we show our approach captures critical complexities of real-world trade networks at the national scale for a broad range of endemic diseases. Changes in trading patterns that minimize disruption to business by maintaining in-flow of animals for each individual farm reduce R 0 , with the largest reductions for diseases that are most challenging to eradicate. Incentivizing high-risk farms to adopt such changes exploits ‘scale-free’ properties of the system and is likely to be particularly effective in reducing national livestock disease burden and incursion risk. Encouragingly, gains made by such targeted modification of trade practices scale much more favourably than comparably targeted improvements to more commonly adopted farm-level biosecurity.

Variants of the susceptible-infected-removed (SIR) model of Kermack & McKendrick (1927) enjoy wide application in epidemiology, offering simple yet powerful inferential and predictive tools in the study of diverse infectious diseases across human, animal and plant populations. Direct transmission models (DTM) are a subset of these that treat the processes of disease transmission as comprising a series of discrete instantaneous events. Infections transmitted indirectly by persistent environmental pathogens, however, are examples where a DTM description might fail and are perhaps better described by models that comprise explicit environmental transmission routes, so-called environmental transmission models (ETM). In this paper we discuss the stochastic susceptible-exposed-infected-removed (SEIR) DTM and susceptible-exposed-infected-removed-pathogen (SEIR-P) ETM and we show that the former is the timescale separation limit of the latter, with ETM host-disease dynamics increasingly resembling those of a DTM when the pathogen’s characteristic timescale is shortened, relative to that of the host population. Using graphical posterior predictive checks (GPPC), we investigate the validity of the SEIR model when fitted to simulated SEIR-P host infection and removal times. Such analyses demonstrate how, in many cases, the SEIR model is robust to departure from direct transmission. Finally, we present a case study of white spot disease (WSD) in penaeid shrimp with rates of environmental transmission and pathogen decay (SEIR-P model parameters) estimated using published results of experiments. Using SEIR and SEIR-P simulations of a hypothetical WSD outbreak management scenario, we demonstrate how relative shortening of the pathogen timescale comes about in practice. With atttempts to remove diseased shrimp from the population every 24h, we see SEIR and SEIR-P model outputs closely conincide. However, when removals are 6-hourly, the two models’ mean outputs diverge, with distinct predictions of outbreak size and duration., Author summary Mathematical models of the spread and progression of communicable disease in populations are important tools in efforts to prevent and control outbreaks. A common class of disease models assume that infection is transmitted directly from infectious to susceptible individuals when they are in close proximity—so called direct transmission models. These are used widely and have proven invaluable as simplified descriptions of a wide array of infectious diseases in diverse populations. However, many pathogens spread through indirect, environmental routes of transmission, for example via contact with contaminated water sources in the case of cholera, or inhalation of infectious airborne droplets for respiratory infections, such as Covid-19. We show that direct transmission models work well for such pathogens with short environmental lifetimes and where hosts shed pathogens into the environment at high rates. This means that we do not require information about environmental pathogen levels to understand the behaviour of outbreaks caused by these pathogens. When shedding rates are also low, e.g., with macroparasitic infections, or when variable environmental factors play a role in transmissibility, then explicit modelling of both the pathogen and environmental transmission will provide a more accurate picture than a direct transmission approximation.

1. Wildlife disease surveillance is the first line of defence against infectious disease. Fluctuations in host populations and disease prevalence are a known feature of wildlife disease systems. However, the impact of such heterogeneities on the performance of surveillance is currently poorly understood. 2. We present the first systematic exploration of the effects of fluctuations prevalence and host population size on the efficacy of wildlife disease surveillance systems. In this study efficacy is measured in terms of ability to estimate long term prevalence and detect disease risk. 3. Our results suggest that for many wildlife disease systems fluctuations in population size and disease lead to bias in surveillance-based estimates of prevalence and over-confidence in assessments of both the precision of prevalence estimates and the power to detect disease. 4. Neglecting such ecological effects may lead to poorly designed surveillance and ultimately to incorrect assessments of the risks posed by disease in wildlife. This will be most problematic in systems where prevalence fluctuations are large and disease fade-outs occur. Such fluctuations are determined by the interaction of demography and disease dynamics and although particularly likely in highly fluctuating populations typical of fecund short lived hosts, can’t be ruled out in more stable populations of longer lived hosts. 5. Synthesis and Applications: Fluctuations in population size and disease prevalence should be considered in the design and implementation of wildlife disease surveillance and the framework presented here provides a template for conducting suitable power calculations. Ultimately understanding the impact of fluctuations in demographic and epidemiological processes will enable improvements to wildlife disease surveillance systems leading to better characterisation of, and protection against endemic, emerging and re-emerging disease threats.

Emerging infectious diseases are a substantial threat to native populations. The spread of disease through naive native populations will depend on both demographic and disease parameters, as well as on habitat suitability and connectivity. Using the potential spread of squirrelpox virus (SQPV) on the Isle of Arran as a case study, we develop mathematical models to examine the impact of an emerging disease on a population in a complex landscape of different habitat types. Furthermore, by considering a range of disease parameters, we infer more generally how complex landscapes interact with disease characteristics to determine the spread and persistence of disease. Specific findings indicate that a SQPV outbreak on Arran is likely to be short lived and localized to the point of introduction allowing recovery of red squirrels to pre-infection densities; this has important consequences for the conservation of red squirrels. More generally, we find that the extent of disease spread is dependent on the rare passage of infection through poor quality corridors connecting good quality habitats. Acute, highly transmissible infectious diseases are predicted to spread rapidly causing high mortality. Nonetheless, the disease typically fades out following local epidemics and is not supported in the long term. A chronic infectious disease is predicted to spread more slowly but can remain endemic in the population. This allows the disease to spread more extensively in the long term as it increases the chance of spread between poorly connected populations. Our results highlight how a detailed understanding of landscape connectivity is crucial when considering conservation strategies to protect native species from disease threats.

Culling wildlife to control disease can lead to both decreases and increases in disease levels, with apparently conflicting responses observed, even for the same wildlife–disease system. There is therefore a pressing need to understand how culling design and implementation influence culling's potential to achieve disease control. We address this gap in understanding using a spatial metapopulation model representing wildlife living in distinct groups with density-dependent dispersal and framed on the badger–bovine tuberculosis (bTB) system. We show that if population reduction is too low, or too few groups are targeted, a ‘perturbation effect’ is observed, whereby culling leads to increased movement and disease spread. We also demonstrate the importance of culling across appropriate time scales, with otherwise successful control strategies leading to increased disease if they are not implemented for long enough. These results potentially explain a number of observations of the dynamics of both successful and unsuccessful attempts to control TB in badgers including the Randomized Badger Culling Trial in the UK, and we highlight their policy implications. Additionally, for parametrizations reflecting a broad range of wildlife–disease systems, we characterize ‘Goldilocks zones’, where, for a restricted combination of culling intensity, coverage and duration, the disease can be reduced without driving hosts to extinction.

Infectious disease surveillance is key to limiting the consequences from infectious pathogens and maintaining animal and public health. Following the detection of a disease outbreak, a response in proportion to the severity of the outbreak is required. It is thus critical to obtain accurate information concerning the origin of the outbreak and its forward trajectory. However, there is often a lack of situational awareness that may lead to over- or under-reaction. There is a widening range of tests available for detecting pathogens, with typically different temporal characteristics, e.g. in terms of when peak test response occurs relative to time of exposure. We have developed a statistical framework that combines response level data from multiple diagnostic tests and is able to ‘hindcast’ (infer the historical trend of) an infectious disease epidemic. Assuming diagnostic test data from a cross-sectional sample of individuals infected with a pathogen during an outbreak, we use a Bayesian Markov Chain Monte Carlo (MCMC) approach to estimate time of exposure, and the overall epidemic trend in the population prior to the time of sampling. We evaluate the performance of this statistical framework on simulated data from epidemic trend curves and show that we can recover the parameter values of those trends. We also apply the framework to epidemic trend curves taken from two historical outbreaks: a bluetongue outbreak in cattle, and a whooping cough outbreak in humans. Together, these results show that hindcasting can estimate the time since infection for individuals and provide accurate estimates of epidemic trends, and can be used to distinguish whether an outbreak is increasing or past its peak. We conclude that if temporal characteristics of diagnostics are known, it is possible to recover epidemic trends of both human and animal pathogens from cross-sectional data collected at a single point in time., Author Summary We have developed a Bayesian approach that can estimate the historic trend of incidence from cross-sectional samples, without relying on ongoing surveillance. This could be used to evaluate changing disease trends, or to inform outbreak responses. We combine two or more diagnostic tests to estimate the time since infection for the individual, and the historic incidence trend in the population as a whole. We evaluate this procedure by applying it to simulated data from synthetic epidemics. Further, we evaluate its real-world applicability by applying it to two scenarios modelled after the UK 2007 bluetongue epidemic, and a small outbreak of whooping cough in Wisconsin, USA. We were able to recover the epidemic trends under a range of conditions using sample sizes of 30–100 individuals. In the scenarios modelled after real-world epidemics, the hindcasted epidemic curves would have provided valuable information about the distribution of infections. The described approach is generic, and applicable to a wide range of human, livestock and wildlife diseases. It can estimate trends in settings for which this is not possible using current methods, including for diseases or regions lacking in surveillance; recover the pattern of spread during the initial “silent” phase once an outbreak is detected; and can be used track emerging infections. Being able to estimate the past trends of diseases from single cross-sectional studies has far-reaching consequences for the design and practice of disease surveillance in all contexts.

SUMMARYMany of the most pervasive disease challenges to livestock are transmitted via oral contact with faeces (or by faecal–aerosol) and the current paper focuses on how disease risk may depend on: spatial heterogeneity, animal searching behaviour, different grazing systems and faecal deposition patterns including those representative of livestock and a range of wildlife. A spatially explicit agent-based model was developed to describe the impact of empirically observed foraging and avoidance behaviours on the risk of disease presented by investigative and grazing contact with both livestock and wildlife faeces. To highlight the role of spatial heterogeneity on disease risks an analogous deterministic model, which ignores spatial heterogeneity and searching behaviour, was compared with the spatially explicit agent-based model. The models were applied to assess disease risks in temperate grazing systems. The results suggest that spatial heterogeneity is crucial in defining the disease risks to which individuals are exposed even at relatively small scales. Interestingly, however, although sensitive to other aspects of behaviour such as faecal avoidance, it was observed that disease risk is insensitive to search distance for typical domestic livestock restricted to small field plots. In contrast disease risk is highly sensitive to distributions of faecal contamination, in that contacts with highly clumped distributions of wildlife contamination are rare in comparison to those with more dispersed contamination. Finally it is argued that the model is a suitable framework to study the relative inter- and intra-specific disease risks posed to livestock under different realistic management regimes.

SUMMARYReduction in wildlife populations is a common method for the control of livestock infections which have wildlife hosts, but its success is dependent on the characteristics of the infection itself, as well as on the spatial and social structure of the wildlife host. Paratuberculosis (Mycobacterium avium subsp. paratuberculosis; Map) is a widespread and difficult infection to control in livestock populations and also has possible links to Crohn's disease in humans. Rabbits have recently been identified as a key wildlife species in terms of paratuberculosis persistence in the environment and risk to the wider host community, including cattle. Here we use a spatially explicit stochastic simulation model of Map dynamics in rabbit populations to quantify the effects of rabbit population control on infection persistence. The model parameters were estimated from empirical studies of rabbit population dynamics and rabbit-to-rabbit routes of Map transmission. Three rabbit control strategies were compared: single unrepeated population reductions based on removing individual animals; single unrepeated population reductions based on removal of entire social groups; and repeated annual population reductions based on removing individual animals. Unrealistically high rabbit culls (>95% population reduction) are needed if infection is to be eradicated from local rabbit populations with a single one-off population reduction event, either of individuals or social groups. Repeated annual culls are more effective at reducing the prevalence of infection in rabbit populations and eradicating infection. However, annual population reductions of >40% are required over extended periods of time (many years). Thus, using an approach which is both highly conservative and parsimonious with respect to estimating lower bounds on the time to eradicate the infection, we find that Map is extremely persistent in rabbit populations and requires significant and prolonged effort to achieve control.

Summary 1 Paratuberculosis (Mycobacterium avium subspecies paratuberculosis; Map) is a widespread and difficult disease to control in livestock populations and also has possible links to Crohn's disease in humans. Rabbits (Oryctolagus cuniculus) have been identified recently as the key wildlife species in terms of paratuberculosis transmission to the wider host community. Here, we test the hypothesis that Map can persist in rabbit populations for extended periods of time in the absence of any external source of infection. 2 A spatially explicit stochastic simulation model of a generic host–disease interaction was developed to quantify the interplay between vertical and horizontal routes of transmission needed for the persistence of Map in rabbit populations and to test the hypothesis. The model was parameterized based on empirical studies on rabbit population dynamics and on rabbit-to-rabbit routes of Map transmission. 3 Predictions from the model suggest that any disease with susceptible–infected (SI) dynamics without disease-induced mortality can persist within a rabbit population in the absence of vertical transmission, providing the horizontal transmission coefficient, β, is greater than approximately 0·012. The inclusion of any vertical transmission reduces the value of β that is necessary for infection to persist. 4 Paratuberculosis persists in rabbit populations at all values of the horizontal and vertical transmission parameters in the range estimated from the field data and in many cases at all values within 95% confidence intervals around this range. The persistence of Map infection in rabbit populations in the absence of external sources of infection suggests that they may act as a reservoir of infection for sympatric livestock. 5 Synthesis and applications. Our findings, in combination with the ubiquitous distribution of rabbits in the United Kingdom and elsewhere, suggests that if Map becomes established in rabbit populations they are likely to provide widespread and persistent environmental distributions of infection and thus disease risk to livestock and potentially humans. Where local rabbit populations are infected they should be included in any future paratuberculosis control strategies. Because eradication of rabbits is often not a realistic option, control strategies should include reducing interspecific transmission risk from rabbits to livestock via the faecal–oral route.

Parasitic nematodes represent one of the most pervasive and significant challenges to grazing livestock, and their intensity and distribution are strongly influenced by climate. Parasite levels and species composition have already shifted under climate change, with nematode parasite intensity frequently low in newly colonized areas, but sudden large-scale outbreaks are becoming increasingly common. These outbreaks compromise both food security and animal welfare, yet there is a paucity of predictions on how climate change will influence livestock parasites. This study aims to assess how climate change can affect parasite risk. Using a process-based approach, we determine how changes in temperature-sensitive elements of outbreaks influence parasite dynamics, to explore the potential for climate change to influence livestock helminth infections. We show that changes in temperate-sensitive parameters can result in nonlinear responses in outbreak dynamics, leading to distinct ‘tipping-points’ in nematode parasite burdens. Through applying two mechanistic models, of varying complexity, our approach demonstrates that these nonlinear responses are robust to the inclusion of a number of realistic processes that are present in livestock systems. Our study demonstrates that small changes in climatic conditions around critical thresholds may result in dramatic changes in parasite burdens.

Parasitic helminths present one of the most pervasive challenges to grazing herbivores. Many macro-parasite transmission models focus on host physiological defence strategies, omitting more complex interactions between hosts and their environments. This work represents the first model that integrates both the behavioural and physiological elements of gastro-intestinal nematode transmission dynamics in a managed grazing system. A spatially explicit, individual-based, stochastic model is developed, that incorporates both the hosts' immunological responses to parasitism, and key grazing behaviours including faecal avoidance. The results demonstrate that grazing behaviour affects both the timing and intensity of parasite outbreaks, through generating spatial heterogeneity in parasite risk and nutritional resources, and changing the timing of exposure to the parasites' free-living stages. The influence of grazing behaviour varies with the host-parasite combination, dependent on the development times of different parasite species and variations in host immune response. Our outputs include the counterintuitive finding that under certain conditions perceived parasite avoidance behaviours (faecal avoidance) can increase parasite risk, for certain host-parasite combinations. Through incorporating the two-way interaction between infection dynamics and grazing behaviour, the potential benefits of parasite-induced anorexia are also demonstrated. Hosts with phenotypic plasticity in grazing behaviour, that make grazing decisions dependent on current parasite burden, can reduce infection with minimal loss of intake over the grazing season. This paper explores how both host behaviours and immunity influence macro-parasite transmission in a spatially and temporally heterogeneous environment. The magnitude and timing of parasite outbreaks is influenced by host immunity and behaviour, and the interactions between them; the incorporation of both regulatory processes is required to fully understand transmission dynamics. Understanding of both physiological and behavioural defence strategies will aid the development of novel approaches for control.

Background A common approach to the application of epidemiological models is to determine a single (point estimate) parameterisation using the information available in the literature. However, in many cases there is considerable uncertainty about parameter values, reflecting both the incomplete nature of current knowledge and natural variation, for example between farms. Furthermore model outcomes may be highly sensitive to different parameter values. Paratuberculosis is an infection for which many of the key parameter values are poorly understood and highly variable, and for such infections there is a need to develop and apply statistical techniques which make maximal use of available data. Results A technique based on Latin hypercube sampling combined with a novel reweighting method was developed which enables parameter uncertainty and variability to be incorporated into a model-based framework for estimation of prevalence. The method was evaluated by applying it to a simulation of paratuberculosis in dairy herds which combines a continuous time stochastic algorithm with model features such as within herd variability in disease development and shedding, which have not been previously explored in paratuberculosis models. Generated sample parameter combinations were assigned a weight, determined by quantifying the model’s resultant ability to reproduce prevalence data. Once these weights are generated the model can be used to evaluate other scenarios such as control options. To illustrate the utility of this approach these reweighted model outputs were used to compare standard test and cull control strategies both individually and in combination with simple husbandry practices that aim to reduce infection rates. Conclusions The technique developed has been shown to be applicable to a complex model incorporating realistic control options. For models where parameters are not well known or subject to significant variability, the reweighting scheme allowed estimated distributions of parameter values to be combined with additional sources of information, such as that available from prevalence distributions, resulting in outputs which implicitly handle variation and uncertainty. This methodology allows for more robust predictions from modelling approaches by allowing for parameter uncertainty and combining different sources of information, and is thus expected to be useful in application to a large number of disease systems.

Simple Summary Parasitic helminths represent one of the most pervasive challenges to livestock, and their intensity and distribution will be influenced by climate change. There is a need for long-term predictions to identify potential risks and highlight opportunities for control. We explore the approaches to modelling future helminth risk to livestock under climate change. One of the limitations to model creation is the lack of purpose driven data collection. We also conclude that models need to include a broad view of the livestock system to generate meaningful predictions. Abstract Climate change is a driving force for livestock parasite risk. This is especially true for helminths including the nematodes Haemonchus contortus, Teladorsagia circumcincta, Nematodirus battus, and the trematode Fasciola hepatica, since survival and development of free-living stages is chiefly affected by temperature and moisture. The paucity of long term predictions of helminth risk under climate change has driven us to explore optimal modelling approaches and identify current bottlenecks to generating meaningful predictions. We classify approaches as correlative or mechanistic, exploring their strengths and limitations. Climate is one aspect of a complex system and, at the farm level, husbandry has a dominant influence on helminth transmission. Continuing environmental change will necessitate the adoption of mitigation and adaptation strategies in husbandry. Long term predictive models need to have the architecture to incorporate these changes. Ultimately, an optimal modelling approach is likely to combine mechanistic processes and physiological thresholds with correlative bioclimatic modelling, incorporating changes in livestock husbandry and disease control. Irrespective of approach, the principal limitation to parasite predictions is the availability of active surveillance data and empirical data on physiological responses to climate variables. By combining improved empirical data and refined models with a broad view of the livestock system, robust projections of helminth risk can be developed.

The effects of social hierarchy on population dynamics and epidemiology are examined through a model which contains a number of fundamental features of hierarchical systems, but is simple enough to allow analytical insight. In order to allow for differences in birth rates, contact rates and movement rates among different sets of individuals the population is first divided into subgroups representing levels in the hierarchy. Movement, representing dominance challenges, is allowed between any two levels, giving a completely connected network. The model includes hierarchical effects by introducing a set of dominance parameters which affect birth rates in each social level and movement rates between social levels, dependent upon their rank. Although natural hierarchies vary greatly in form, the skewing of contact patterns, introduced here through non-uniform dominance parameters, has marked effects on the spread of disease. A simple homogeneous mixing differential equation model of a disease with SI dynamics in a population subject to simple birth and death process is presented and it is shown that the hierarchical model tends to this as certain parameter regions are approached. Outside of these parameter regions correlations within the system give rise to deviations from the simple theory. A Gaussian moment closure scheme is developed which extends the homogeneous model in order to take account of correlations arising from the hierarchical structure, and it is shown that the results are in reasonable agreement with simulations across a range of parameters. This approach helps to elucidate the origin of hierarchical effects and shows that it may be straightforward to relate the correlations in the model to measurable quantities which could be used to determine the importance of hierarchical corrections. Overall, hierarchical effects decrease the levels of disease present in a given population compared to a homogeneous unstructured model, but show higher levels of disease than structured models with no hierarchy. The separation between these three models is greatest when the rate of dominance challenges is low, reducing mixing, and when the disease prevalence is low. This suggests that these effects will often need to be considered in models being used to examine the impact of control strategies where the low disease prevalence behaviour of a model is critical.

Rabbits have been increasingly linked to the persistence of paratuberculosis (Johne's disease) in domestic ruminants in the United Kingdom. The aims of this study were to determine the routes of intraspecies transmission of Mycobacterium avium subspecies paratuberculosis (MAP) in rabbits and to estimate the probability of transmission via each route, in order to gain understanding of the dynamics of MAP in this host. Rabbits were sampled from two sites where MAP had previously been isolated from the livestock and rabbit populations. No pathology was noted in any animals, but the overall prevalence of MAP in rabbits was high at both sites studied, 39.7% and 23.0%, respectively. MAP was isolated from the testes, uterus, placenta, fetuses, and milk. This is the first time that the bacterium has been isolated from any of these tissues in a nonruminant wildlife species. These results suggest that transmission may occur vertically, pseudovertically, and horizontally. Vertical, i.e., transplacental, and/or pseudo-vertical, i.e., through the ingestion of contaminated milk and/or feces, transmission occurred in 14% of offspring entering the population at 1 month of age. As infection via these routes is only possible from infected adult females, this equates to a probability of infection via this route of 0.326. Probability of infection via horizontal transmission (including interspecies transmission) occurred at up to 0.037 per month. The presence of these routes of transmission within natural rabbit populations will contribute to the maintenance of MAP infections within such populations and, therefore, the environment.

Population reduction is often used as a control strategy when managing infectious diseases in wildlife populations in order to reduce host density below a critical threshold. However, population reduction can disrupt existing social and demographic structures leading to changes in observed host behaviour that may result in enhanced disease transmission. Such effects have been observed in several disease systems, notably badgers and bovine tuberculosis. Here we characterise the fundamental properties of disease systems for which such effects undermine the disease control benefits of population reduction.\ud \ud By quantifying the size of response to population reduction in terms of enhanced transmission within a generic non-spatial model, the properties of disease systems in which such effects reduce or even reverse the disease control benefits of population reduction are identified. If population reduction is not sufficiently severe, then enhanced transmission can lead to the counter intuitive perturbation effect, whereby disease levels increase or persist where they would otherwise die out. Perturbation effects are largest for systems with low levels of disease, e.g. low levels of endemicity or emerging disease.\ud \ud Analysis of a stochastic spatial meta-population model of demography and disease dynamics leads to qualitatively similar conclusions. Moreover, enhanced transmission itself is found to arise as an emergent property of density dependent dispersal in such systems. This spatial analysis also shows that, below some threshold, population reduction can rapidly increase the area affected by disease, potentially expanding risks to sympatric species.\ud \ud Our results suggest that the impact of population reduction on social and demographic structures is likely to undermine disease control in many systems, and in severe cases leads to the perturbation effect. Social and demographic mechanisms that enhance transmission following population reduction should therefore be routinely considered when designing control programmes.

Previous studies have shown that host genetic heterogeneity in the response to infectious challenge can affect the emergence risk and the severity of diseases transmitted through direct contact between individuals. However, there is substantial uncertainty about the degree and direction of influence owing to different definitions of genetic variation, most of which are not in line with the current understanding of the genetic architecture of disease traits. Also, the relevance of previous results for diseases transmitted through environmental sources is unclear. In this article a compartmental genetic–epidemiological model was developed to quantify the impact of host genetic diversity on epidemiological characteristics of diseases transmitted through a contaminated environment. The model was parameterized for footrot in sheep. Genetic variation was defined through continuous distributions with varying shape and degree of dispersion for different disease traits. The model predicts a strong impact of genetic heterogeneity on the disease risk and its progression and severity, as well as on observable host phenotypes, when dispersion in key epidemiological parameters is high. The impact of host variation depends on the disease trait for which variation occurs and on environmental conditions affecting pathogen survival. In particular, compared to homogeneous populations with the same average susceptibility, disease risk and severity are substantially higher in populations containing a large proportion of highly susceptible individuals, and the differences are strongest when environmental contamination is low. The implications of our results for the recording and analysis of disease data and for predicting response to selection are discussed.