How do pathogen evolution and host heterogeneity interact in disease emergence?

Analysis of the risk and pre-emptive control of viral outbreaks accounting for within-host dynamics: SARS-CoV-2 as a case study

In the era of living with COVID-19, the risk of localised SARS-CoV-2 outbreaks remains. Here, we develop a multiscale modelling framework for estimating the local outbreak risk for a viral disease (the probability that a major outbreak results from a single case introduced into the population), accounting for within-host viral dynamics. Compared to population-level models previously used to estimate outbreak risks, our approach enables more detailed analysis of how the risk can be mitigated through pre-emptive interventions such as antigen testing. Considering SARS-CoV-2 as a case study, we quantify the within-host dynamics using data from individuals with omicron variant infections. We demonstrate that regular antigen testing reduces, but may not eliminate, the outbreak risk, depending on characteristics of local transmission. In our baseline analysis, daily antigen testing reduces the outbreak risk by 45% compared to a scenario without antigen testing. Additionally, we show that accounting for heterogeneity in within-host dynamics between individuals affects outbreak risk estimates and assessments of the impact of antigen testing. Our results therefore highlight important factors to consider when using multiscale models to design pre-emptive interventions against SARS-CoV-2 and other viruses.

A practical guide to mathematical methods for estimating infectious disease outbreak risks

Mathematical models are increasingly used throughout infectious disease outbreaks to guide control measures. In this review article, we focus on the initial stages of an outbreak, when a pathogen has just been observed in a new location (e.g., a town, region or country). We provide a beginner's guide to two methods for estimating the risk that introduced cases lead to sustained local transmission (i.e., the probability of a major outbreak), as opposed to the outbreak fading out with only a small number of cases. We discuss how these simple methods can be extended for epidemiological models with any level of complexity, facilitating their wider use, and describe how estimates of the probability of a major outbreak can be used to guide pathogen surveillance and control strategies. We also give an overview of previous applications of these approaches. This guide is intended to help quantitative researchers develop their own epidemiological models and use them to estimate the risks associated with pathogens arriving in new host populations. The development of these models is crucial for future outbreak preparedness. This manuscript was submitted as part of a theme issue on "Modelling COVID-19 and Preparedness for Future Pandemics".

Ecological and evolutionary dynamics of multi-strain RNA viruses

Potential interactions among co-circulating viral strains in host populations are often overlooked in the study of virus transmission. However, these interactions probably shape transmission dynamics by influencing host immune responses or altering the relative fitness among co-circulating strains. In this Review, we describe multi-strain dynamics from ecological and evolutionary perspectives, outline scales in which multi-strain dynamics occur and summarize important immunological, phylogenetic and mathematical modelling approaches used to quantify interactions among strains. We also discuss how host-pathogen interactions influence the co-circulation of pathogens. Finally, we highlight outstanding questions and knowledge gaps in the current theory and study of ecological and evolutionary dynamics of multi-strain viruses.

The source of individual heterogeneity shapes infectious disease outbreaks

There is known heterogeneity between individuals in infectious disease transmission patterns. The source of this heterogeneity is thought to affect epidemiological dynamics but studies tend not to control for the overall heterogeneity in the number of secondary cases caused by an infection. To explore the role of individual variation in infection duration and transmission rate in parasite emergence and spread, while controlling for this potential bias, we simulate stochastic outbreaks with and without parasite evolution. As expected, heterogeneity in the number of secondary cases decreases the probability of outbreak emergence. Furthermore, for epidemics that do emerge, assuming more realistic infection duration distributions leads to faster outbreaks and higher epidemic peaks. When parasites require adaptive mutations to cause large epidemics, the impact of heterogeneity depends on the underlying evolutionary model. If emergence relies on within-host evolution, decreasing the infection duration variance decreases the probability of emergence. These results underline the importance of accounting for realistic distributions of transmission rates to anticipate the effect of individual heterogeneity on epidemiological dynamics.

Host genotype and genetic diversity shape the evolution of a novel bacterial infection

Pathogens continue to emerge from increased contact with novel host species. Whilst these hosts can represent distinct environments for pathogens, the impacts of host genetic background on how a pathogen evolves post-emergence are unclear. In a novel interaction, we experimentally evolved a pathogen (Staphylococcus aureus) in populations of wild nematodes (Caenorhabditis elegans) to test whether host genotype and genetic diversity affect pathogen evolution. After ten rounds of selection, we found that pathogen virulence evolved to vary across host genotypes, with differences in host metal ion acquisition detected as a possible driver of increased host exploitation. Diverse host populations selected for the highest levels of pathogen virulence, but infectivity was constrained, unlike in host monocultures. We hypothesise that population heterogeneity might pool together individuals that contribute disproportionately to the spread of infection or to enhanced virulence. The genomes of evolved populations were sequenced, and it was revealed that pathogens selected in distantly-related host genotypes diverged more than those in closely-related host genotypes. S. aureus nevertheless maintained a broad host range. Our study provides unique empirical insight into the evolutionary dynamics that could occur in other novel infections of wildlife and humans.

Evolutionary rescue in host-pathogen systems

Natural populations encounter a variety of threats that can increase their risk of extinction. Populations can avoid extinction through evolutionary rescue (ER), which occurs when an adaptive, genetic response to selection allows a population to recover from an environmental change that would otherwise cause extinction. While the traditional framework for ER was developed with abiotic risk factors in mind, ER may also occur in response to a biotic source of demographic change, such as the introduction of a novel pathogen. We first describe how ER in response to a pathogen differs from the traditional ER framework; density-dependent transmission, pathogen evolution, and pathogen extinction can change the strength of selection imposed by a pathogen and make host population persistence more likely. We also discuss several variables that affect traditional ER (abundance, genetic diversity, population connectivity, and community composition) that also directly affect disease risk resulting in diverse outcomes for ER in host-pathogen systems. Thus, generalizations developed in studies of traditional ER may not be relevant for ER in response to the introduction of a pathogen. Incorporating pathogens into the framework of ER will lead to a better understanding of how and when populations can avoid extinction in response to novel pathogens.

Predicting the Effectiveness of Endemic Infectious Disease Control Interventions: The Impact of Mass Action versus Network Model Structure

BACKGROUND

Analyses of the effectiveness of infectious disease control interventions often rely on dynamic transmission models to simulate intervention effects. We aim to understand how the choice of network or compartmental model can influence estimates of intervention effectiveness in the short and long term for an endemic disease with susceptible and infected states in which infection, once contracted, is lifelong.

METHODS

We consider 4 disease models with different permutations of socially connected network versus unstructured contact (mass-action mixing) model and heterogeneous versus homogeneous disease risk. The models have susceptible and infected populations calibrated to the same long-term equilibrium disease prevalence. We consider a simple intervention with varying levels of coverage and efficacy that reduces transmission probabilities. We measure the rate of prevalence decline over the first 365 d after the intervention, long-term equilibrium prevalence, and long-term effective reproduction ratio at equilibrium.

RESULTS

Prevalence declined up to 10% faster in homogeneous risk models than heterogeneous risk models. When the disease was not eradicated, the long-term equilibrium disease prevalence was higher in mass-action mixing models than in network models by 40% or more. This difference in long-term equilibrium prevalence between network versus mass-action mixing models was greater than that of heterogeneous versus homogeneous risk models (less than 30%); network models tended to have higher effective reproduction ratios than mass-action mixing models for given combinations of intervention coverage and efficacy.

CONCLUSIONS

For interventions with high efficacy and coverage, mass-action mixing models could provide a sufficient estimate of effectiveness, whereas for interventions with low efficacy and coverage, or interventions in which outcomes are measured over short time horizons, predictions from network and mass-action models diverge, highlighting the importance of sensitivity analyses on model structure.

HIGHLIGHTS

• We calibrate 4 models-socially connected network versus unstructured contact (mass-action mixing) model and heterogeneous versus homogeneous disease risk-to 10% preintervention disease prevalence.• We measure the short- and long-term intervention effectiveness of all models using the rate of prevalence decline, long-term equilibrium disease prevalence, and effective reproduction ratio.• Generally, in the short term, prevalence declined faster in the homogeneous risk models than in the heterogeneous risk models.• Generally, in the long term, equilibrium disease prevalence was higher in the mass-action mixing models than in the network models, and the effective reproduction ratio was higher in network models than in the mass-action mixing models.

Genetic, social and maternal contributions to Mycobacterium bovis infection status in European badgers (Meles meles)

Within host populations, individuals can vary in their susceptibility to infections and in the severity and progression of disease once infected. Though mediated through differences in behaviour, resistance or tolerance, variation in disease outcomes ultimately stems from genetic and environmental (including social) factors. Despite obvious implications for the evolutionary, ecological and epidemiological dynamics of disease traits, the relative importance of these factors has rarely been quantified in naturally infected wild animal hosts. Here, we use a long-term capture-mark-recapture study of group-living European badgers (Meles meles) to characterize genetic and environmental sources of variation in host infection status by Mycobacterium bovis, the causative agent of bovine tuberculosis (bTB). We find that genetic factors contribute to M. bovis infection status, whether measured over a lifetime or across repeated captures. In the latter case, the heritability (h² ) of infection status is close to zero in cubs and yearlings but increases in adulthood. Overall, environmental influences arising from a combination of social group membership (defined in time and space) and maternal effects appear to be more important than genetic factors. Thus, while genes do contribute to among-individual variation, they play a comparatively minor role, meaning that rapid evolution of host defences under parasite-mediated selection is unlikely (especially if selection is on young animals where h² is lowest). Conversely, our results lend further support to the view that social and early-life environments are important drivers of the dynamics of bTB infection in badger populations specifically, and of disease traits in wild hosts more generally.

SHERLOCK and DETECTR: CRISPR-Cas Systems as Potential Rapid Diagnostic Tools for Emerging Infectious Diseases

Infectious diseases are one of the most intimidating threats to human race, responsible for an immense burden of disabilities and deaths. Rapid diagnosis and treatment of infectious diseases offers a better understanding of their pathogenesis. According to the World Health Organization, the ideal approach for detecting foreign pathogens should be rapid, specific, sensitive, instrument-free, and cost-effective. Nucleic acid pathogen detection methods, typically PCR, have numerous limitations, such as highly sophisticated equipment requirements, reagents, and trained personnel relying on well-established laboratories, besides being time-consuming. Thus, there is a crucial need to develop novel nucleic acid detection tools that are rapid, specific, sensitive, and cost-effective, particularly ones that can be used for versatile point-of-care diagnostic applications. Two new methods exploit unpredicted in vitro properties of CRISPR-Cas effectors, turning activated nucleases into basic amplifiers of a specific nucleic acid binding event. These effectors can be attached to a diversity of reporters and utilized in tandem with isothermal amplification approaches to create sensitive identification in multiple deployable field formats. Although still in their beginning, SHERLOCK and DETECTR technologies are potential methods for rapid detection and identification of infectious diseases, with ultrasensitive tests that do not require complicated processing. This review describes SHERLOCK and DETECTR technologies and assesses their properties, functions, and prospective to become the ultimate diagnostic tools for diagnosing infectious diseases and curbing disease outbreaks.

Pathogen Dynamics across the Diversity of Aging

AbstractReproduction, mortality, and immune function often change with age but do not invariably deteriorate. Across the tree of life, there is extensive variation in age-specific performance and changes to key life-history traits. These changes occur on a spectrum from classic senescence, where performance declines with age, to juvenescence, where performance improves with age. Reproduction, mortality, and immune function are also important factors influencing the spread of infectious disease, yet there exists no comprehensive investigation into how the aging spectrum of these traits impacts epidemics. We used a model laboratory infection system to compile an aging profile of a single organism, including traits directly linked to pathogen susceptibility and those that should indirectly alter pathogen transmission by influencing demography. We then developed generalizable epidemiological models demonstrating that different patterns of aging produce dramatically different transmission landscapes: in many cases, aging can reduce the probability of epidemics, but it can also promote severity. This work provides context and tools for use across taxa by empiricists, demographers, and epidemiologists, advancing our ability to accurately predict factors contributing to epidemics or the potential repercussions of senescence manipulation.

Diversity in CRISPR-based immunity protects susceptible genotypes by restricting phage spread and evolution

Diversity in host resistance often associates with reduced pathogen spread. This may result from ecological and evolutionary processes, likely with feedback between them. Theory and experiments on bacteria-phage interactions have shown that genetic diversity of the bacterial adaptive immune system can limit phage evolution to overcome resistance. Using the CRISPR-Cas bacterial immune system and lytic phage, we engineered a host-pathogen system where each bacterial host genotype could be infected by only one phage genotype. With this model system, we explored how CRISPR diversity impacts the spread of phage when they can overcome a resistance allele, how immune diversity affects the evolution of the phage to increase its host range and if there was feedback between these processes. We show that increasing CRISPR diversity benefits susceptible bacteria via a dilution effect, which limits the spread of the phage. We suggest that this ecological effect impacts the evolution of novel phage genotypes, which then feeds back into phage population dynamics.

On the probability of strain invasion in endemic settings: Accounting for individual heterogeneity and control in multi-strain dynamics

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Endemic infection can insulate host populations from invasion by mutant variants.

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The timing of control implementation strongly influences its efficacy.

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Controls that exacerbate host heterogeneity outperform those that curtail it.

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Differential control can facilitate strain invasion and eventual replacement.

Pathogen evolution is an imminent threat to global health that has warranted, and duly received, considerable attention within the medical, microbiological and modelling communities. Outbreaks of new pathogens are often ignited by the emergence and transmission of mutant variants descended from wild-type strains circulating in the community. In this work we investigate the stochastic dynamics of the emergence of a novel disease strain, introduced into a population in which it must compete with an existing endemic strain. In analogy with past work on single-strain epidemic outbreaks, we apply a branching process approximation to calculate the probability that the new strain becomes established. As expected, a critical determinant of the survival prospects of any invading strain is the magnitude of its reproduction number relative to that of the background endemic strain. Whilst in most circumstances this ratio must exceed unity in order for invasion to be viable, we show that differential control scenarios can lead to less-fit novel strains invading populations hosting a fitter endemic one. This analysis and the accompanying findings will inform our understanding of the mechanisms that have led to past instances of successful strain invasion, and provide valuable lessons for thwarting future drug-resistant strain incursions.

Genetic resistance and specificity in sister taxa of Daphnia: insights from the range of host susceptibilities

BACKGROUND

Host genetic diversity can affect various aspects of host-parasite interactions, including individual-level effects on parasite infectivity, production of transmission stages and virulence, as well as population-level effects that reduce disease spread and prevalence, and buffer against widespread epidemics. However, a key aspect of this diversity, the genetic variation in host susceptibility, has often been neglected in interpreting empirical data and in theoretical studies. Daphnia similis naturally coexists with its competitor Daphnia magna and is more resistant to the endoparasitic microsporidium Hamiltosporidium tvaerminnensis, as suggested by a previous survey of waterbodies, which detected this parasite in D. magna, but not in D. similis. However, under laboratory conditions D. similis was sometimes found to be susceptible. We therefore asked if there is genetic variation for disease trait expression, and if the genetic variation in disease traits in D. similis is different from that of D. magna.

METHODS

We exposed ten clones of D. similis and ten clones of D. magna to three isolates of H. tvaerminnensis, and measured infection rates, parasite-induced host mortality and parasite spore production.

RESULTS

The two Daphnia species differ in the range and variation of their susceptibilities. The parasite produced on average two-fold more spores when growing in D. magna clones than in D. similis clones.

CONCLUSIONS

We confirm that D. similis is indeed much more resistant than D. magna and suggest that this could create a dilution effect in habitats where both species coexist.

Role of host genetic diversity for susceptibility-to-infection in the evolution of virulence of a plant virus

Predicting viral emergence is difficult due to the stochastic nature of the underlying processes and the many factors that govern pathogen evolution. Environmental factors affecting the host, the pathogen and the interaction between both are key in emergence. In particular, infectious disease dynamics are affected by spatiotemporal heterogeneity in their environments. A broad knowledge of these factors will allow better estimating where and when viral emergence is more likely to occur. Here, we investigate how the population structure for susceptibility-to-infection genes of the plant Arabidopsis thaliana shapes the evolution of Turnip mosaic virus (TuMV). For doing so we have evolved TuMV lineages in two radically different host population structures: (1) a metapopulation subdivided into six demes (subpopulations); each one being composed of individuals from only one of six possible A. thaliana ecotypes and (2) a well-mixed population constituted by equal number of plants from the same six A. thaliana ecotypes. These two populations were evolved for twelve serial passages. At the end of the experimental evolution, we found faster adaptation of TuMV to each ecotype in the metapopulation than in the well-mixed heterogeneous host populations. However, viruses evolved in well-mixed populations were more pathogenic and infectious than viruses evolved in the metapopulation. Furthermore, the viruses evolved in the demes showed stronger signatures of local specialization than viruses evolved in the well-mixed populations. These results illustrate how the genetic diversity of hosts in an experimental ecosystem favors the evolution of virulence of a pathogen.

The evolution of stage-specific virulence: Differential selection of parasites in juveniles

The impact of infectious disease is often very different in juveniles and adults, but theory has focused on the drivers of stage-dependent defense in hosts rather than the potential for stage-dependent virulence evolution in parasites. Stage structure has the potential to be important to the evolution of pathogens because it exposes parasites to heterogeneous environments in terms of both host characteristics and transmission pathways. We develop a stage-structured (juvenile-adult) epidemiological model and examine the evolutionary outcomes of stage-specific virulence under the classic assumption of a transmission-virulence trade-off. We show that selection on virulence against adults remains consistent with the classic theory. However, the evolution of juvenile virulence is sensitive to both demography and transmission pathway with higher virulence against juveniles being favored either when the transmission pathway is assortative (juveniles preferentially interact together) and the juvenile stage is long, or in contrast when the transmission pathway is disassortative and the juvenile stage is short. These results highlight the potentially profound effects of host stage structure on determining parasite virulence in nature. This new perspective may have broad implications for both understanding and managing disease severity.

Evolutionary emergence of infectious diseases in heterogeneous host populations

The emergence and re-emergence of pathogens remains a major public health concern. Unfortunately, when and where pathogens will (re-)emerge is notoriously difficult to predict, as the erratic nature of those events is reinforced by the stochastic nature of pathogen evolution during the early phase of an epidemic. For instance, mutations allowing pathogens to escape host resistance may boost pathogen spread and promote emergence. Yet, the ecological factors that govern such evolutionary emergence remain elusive because of the lack of ecological realism of current theoretical frameworks and the difficulty of experimentally testing their predictions. Here, we develop a theoretical model to explore the effects of the heterogeneity of the host population on the probability of pathogen emergence, with or without pathogen evolution. We show that evolutionary emergence and the spread of escape mutations in the pathogen population is more likely to occur when the host population contains an intermediate proportion of resistant hosts. We also show that the probability of pathogen emergence rapidly declines with the diversity of resistance in the host population. Experimental tests using lytic bacteriophages infecting their bacterial hosts containing Clustered Regularly Interspaced Short Palindromic Repeat and CRISPR-associated (CRISPR-Cas) immune defenses confirm these theoretical predictions. These results suggest effective strategies for cross-species spillover and for the management of emerging infectious diseases.

The probability that an epidemic will break out is highly dependent on the ability of the pathogen to acquire new adaptive mutations and to induce evolutionary emergence. Forecasting pathogen emergence thus requires a good understanding of the interplay between the epidemiology and evolution taking place at the onset of an outbreak. Here, we provide a comprehensive theoretical framework to analyze the impact of host population heterogeneity on the probability of pathogen evolutionary emergence. We use this model to predict the impact of the fraction of susceptible hosts, the inoculum size of the pathogen, and the diversity of host resistance on pathogen emergence. Our experiments using lytic bacteriophages and CRISPR-resistant bacteria support our theoretical predictions and demonstrate that manipulating the diversity of resistance alleles in a host population may be an effective way to limit the emergence of new pathogens.

Precise Estimates of Persistence Time for SIS Infections in Heterogeneous Populations

For a susceptible-infectious-susceptible infection model in a heterogeneous population, we derive simple and precise estimates of mean persistence time, from a quasi-stationary endemic state to extinction of infection. Heterogeneity may be in either individuals' levels of infectiousness or of susceptibility, as well as in individuals' infectious period distributions. Infectious periods are allowed to follow arbitrary non-negative distributions. We also obtain a new and accurate approximation to the quasi-stationary distribution of the process, as well as demonstrating the use of our estimates to investigate the effects of different forms of heterogeneity. Our model may alternatively be interpreted as describing an infection spreading through a heterogeneous directed network, under the annealed network approximation.

Evolutionary rescue in a host-pathogen system results in coexistence not clearance

The evolutionary rescue of host populations may prevent extinction from novel pathogens. However, the conditions that facilitate rapid evolution of hosts, in particular the population variation in host susceptibility, and the effects of host evolution in response to pathogens on population outcomes remain largely unknown. We constructed an individual-based model to determine the relationships between genetic variation in host susceptibility and population persistence in an amphibian-fungal pathogen (Batrachochytrium dendrobatidis) system. We found that host populations can rapidly evolve reduced susceptibility to a novel pathogen and that this rapid evolution led to a 71-fold increase in the likelihood of host-pathogen coexistence. However, the increased rates of coexistence came at a cost to host populations; fewer populations cleared infection, population sizes were depressed, and neutral genetic diversity was lost. Larger adult host population sizes and greater adaptive genetic variation prior to the onset of pathogen introduction led to substantially reduced rates of extinction, suggesting that populations with these characteristics should be prioritized for conservation when species are threatened by novel infectious diseases.

Dissecting the contributions of time and microbe density to variation in immune gene expression

Widespread differential expression of immunological genes is a hallmark of the response to infection in almost all surveyed taxa. However, several challenges remain in the attempt to connect differences in gene expression with functional outcomes like parasite killing and host survival. For example, temporal gene expression patterns are not always monotonic (unidirectional slope), yielding results that qualitatively depend on the time point selected for analysis. They may also be correlated to microbe density, confounding the strength of an immune response and resistance to parasites. In this study, we analyse these relationships in an mRNA-seq time series of Tribolium castaneum infected with Bacillus thuringiensis Our results suggest that many extracellular immunological components with known roles in immunity, like antimicrobial peptides and recognition proteins, are highly correlated to microbe load. On the other hand, intracellular components of immunological signalling pathways overwhelmingly show non-monotonic temporal patterns of gene expression, despite the underlying assumption of monotonicity in most ecological and comparative transcriptomics studies that rely on cross-sectional analyses. Our results raise a host of new questions, including to what extent variation in host resistance, infection tolerance and immunopathology can be explained by variation in the slope or sensitivity of these newly characterized patterns.

Assessing the Epidemic Potential of RNA and DNA Viruses

Many new and emerging RNA and DNA viruses are zoonotic or have zoonotic origins in an animal reservoir that is usually mammalian and sometimes avian. Not all zoonotic viruses are transmissible (directly or by an arthropod vector) between human hosts. Virus genome sequence data provide the best evidence of transmission. Of human transmissible virus, 37 species have so far been restricted to self-limiting outbreaks. These viruses are priorities for surveillance because relatively minor changes in their epidemiologies can potentially lead to major changes in the threat they pose to public health. On the basis of comparisons across all recognized human viruses, we consider the characteristics of these priority viruses and assess the likelihood that they will further emerge in human populations. We also assess the likelihood that a virus that can infect humans but is not capable of transmission (directly or by a vector) between human hosts can acquire that capability.

Host Responses to Pathogen Priming in a Natural Songbird Host

Hosts in free-living populations can experience substantial variation in the frequency and dose of pathogen exposure, which can alter disease progression and protection from future exposures. In the house finch-Mycoplasma gallisepticum (MG) system, the pathogen is primarily transmitted via bird feeders, and some birds may be exposed to frequent low doses of MG while foraging. Here we experimentally determined how low dose, repeated exposures of house finches to MG influence host responses and protection from secondary high-dose challenge. MG-naive house finches were given priming exposures that varied in dose and total number. After quantifying host responses to priming exposures, all birds were given a secondary high-dose challenge to assess immunological protection. Dose, but not the number of exposures, significantly predicted both infection and disease severity following priming exposure. Furthermore, individuals given higher priming doses showed stronger protection upon secondary, high-dose challenge. However, even single low-dose exposures to MG, a proxy for what some birds likely experience in the wild while feeding, provided significant protection against a high-dose challenge. Our results suggest that bird feeders, which serve as sources of infection in the wild, may in some cases act as "immunizers," with important consequences for disease dynamics.

The effects of heterogeneity on stochastic cycles in epidemics

Models of biological processes are often subject to different sources of noise. Developing an understanding of the combined effects of different types of uncertainty is an open challenge. In this paper, we study a variant of the susceptible-infective-recovered model of epidemic spread, which combines both agent-to-agent heterogeneity and intrinsic noise. We focus on epidemic cycles, driven by the stochasticity of infection and recovery events, and study in detail how heterogeneity in susceptibilities and propensities to pass on the disease affects these quasi-cycles. While the system can only be described by a large hierarchical set of equations in the transient regime, we derive a reduced closed set of equations for population-level quantities in the stationary regime. We analytically obtain the spectra of quasi-cycles in the linear-noise approximation. We find that the characteristic frequency of these cycles is typically determined by population averages of susceptibilities and infectivities, but that their amplitude depends on higher-order moments of the heterogeneity. We also investigate the synchronisation properties and phase lag between different groups of susceptible and infected individuals.

Host size influences the effects of four isolates of an amphibian chytrid fungus

Understanding factors that influence host–pathogen interactions is key to predicting outbreaks in natural systems experiencing environmental change. Many amphibian population declines have been attributed to an amphibian chytrid fungus, Batrachochytrium dendrobatidis (Bd). While this fungus is widespread, not all Bd‐positive populations have been associated with declines, which could be attributed to differences in pathogen virulence or host susceptibility. In a laboratory experiment, we examined the effects of Bd isolate origin, two from areas with Bd‐associated amphibian population declines (El Copé, Panama, and California, USA) and two from areas without Bd‐related population declines (Ohio and Maine, USA), on the terrestrial growth and survival of American toad (Anaxyrus americanus) metamorphs reared in larval environments with low or high intraspecific density. We predicted that (1) Bd isolates from areas experiencing declines would have greater negative effects than Bd isolates from areas without declines, and (2) across all isolates, growth and survival of smaller toads from high‐density larval conditions would be reduced by Bd exposure compared to larger toads from low‐density larval conditions. Our results showed that terrestrial survival was reduced for smaller toads exposed to Bd with variation in the response to different isolates, suggesting that smaller size increased susceptibility to Bd. Toads exposed to Bd gained less mass, which varied by isolate. Bd isolates from areas with population declines, however, did not have more negative effects than isolates from areas without recorded declines. Most strikingly, our study supports that host condition, measured by size, can be indicative of the negative effects of Bd exposure. Further, Bd isolates’ impact may vary in ways not predictable from place of origin or occurrence of disease‐related population declines. This research suggests that amphibian populations outside of areas experiencing Bd‐associated declines could be impacted by this pathogen and that the size of individuals could influence the magnitude of Bd's impact.

Modeling optimal treatment strategies in a heterogeneous mixing model

Background

Many mathematical models assume random or homogeneous mixing for various infectious diseases. Homogeneous mixing can be generalized to mathematical models with multi-patches or age structure by incorporating contact matrices to capture the dynamics of the heterogeneously mixing populations. Contact or mixing patterns are difficult to measure in many infectious diseases including influenza. Mixing patterns are considered to be one of the critical factors for infectious disease modeling.

Methods

A two-group influenza model is considered to evaluate the impact of heterogeneous mixing on the influenza transmission dynamics. Heterogeneous mixing between two groups with two different activity levels includes proportionate mixing, preferred mixing and like-with-like mixing. Furthermore, the optimal control problem is formulated in this two-group influenza model to identify the group-specific optimal treatment strategies at a minimal cost. We investigate group-specific optimal treatment strategies under various mixing scenarios.

Results

The characteristics of the two-group influenza dynamics have been investigated in terms of the basic reproduction number and the final epidemic size under various mixing scenarios. As the mixing patterns become proportionate mixing, the basic reproduction number becomes smaller; however, the final epidemic size becomes larger. This is due to the fact that the number of infected people increases only slightly in the higher activity level group, while the number of infected people increases more significantly in the lower activity level group. Our results indicate that more intensive treatment of both groups at the early stage is the most effective treatment regardless of the mixing scenario. However, proportionate mixing requires more treated cases for all combinations of different group activity levels and group population sizes.

Conclusions

Mixing patterns can play a critical role in the effectiveness of optimal treatments. As the mixing becomes more like-with-like mixing, treating the higher activity group in the population is almost as effective as treating the entire populations since it reduces the number of disease cases effectively but only requires similar treatments. The gain becomes more pronounced as the basic reproduction number increases. This can be a critical issue which must be considered for future pandemic influenza interventions, especially when there are limited resources available.

Population viscosity suppresses disease emergence by preserving local herd immunity

Animal reservoirs for infectious diseases pose ongoing risks to human populations. In this theory of zoonoses, the introduction event that starts an epidemic is assumed to be independent of all preceding events. However, introductions are often concentrated in communities that bridge the ecological interfaces between reservoirs and the general population. In this paper, we explore how the risks of disease emergence are altered by the aggregation of introduction events within bridge communities. In viscous bridge communities, repeated introductions can elevate the local prevalence of immunity. This local herd immunity can form a barrier reducing the opportunities for disease emergence. In some situations, reducing exposure rates counterintuitively increases the emergence hazards because of off-setting reductions in local immunity. Increases in population mixing can also increase emergence hazards, even when average contact rates are conserved. Our theory of bridge communities may help guide prevention and explain historical emergence events, where disruption of stable economic, political or demographic processes reduced population viscosity at ecological interfaces.

Host Genotype and Coinfection Modify the Relationship of within and between Host Transmission

Variation in individual-level disease transmission is well documented, but the underlying causes of this variation are challenging to disentangle in natural epidemics. In general, within-host replication is critical in determining the extent to which infected hosts shed transmission propagules, but which factors cause variation in this relationship are poorly understood. Here, using a plant host, Plantago lanceolata, and the powdery mildew fungus Podosphaera plantaginis, we quantify how the distinct stages of within-host spread (autoinfection), spore release, and successful transmission to new hosts (alloinfection) are influenced by host genotype, pathogen genotype, and the coinfection status of the host. We find that within-host spread alone fails to predict transmission rates, as this relationship is modified by genetic variation in hosts and pathogens. Their contributions change throughout the course of the epidemic. Host genotype and coinfection had particularly pronounced effects on the dynamics of spore release from infected hosts. Confidently predicting disease spread from local levels of individual transmission, therefore, requires a more nuanced understanding of genotype-specific infection outcomes. This knowledge is key to better understanding the drivers of epidemiological dynamics and the resulting evolutionary trajectories of infectious disease.

Evolution and emergence of infectious diseases in theoretical and real-world networks

One of the most important advancements in theoretical epidemiology has been the development of methods that account for realistic host population structure. The central finding is that heterogeneity in contact networks, such as the presence of 'superspreaders', accelerates infectious disease spread in real epidemics. Disease control is also complicated by the continuous evolution of pathogens in response to changing environments and medical interventions. It remains unclear, however, how population structure influences these adaptive processes. Here we examine the evolution of infectious disease in empirical and theoretical networks. We show that the heterogeneity in contact structure, which facilitates the spread of a single disease, surprisingly renders a resident strain more resilient to invasion by new variants. Our results suggest that many host contact structures suppress invasion of new strains and may slow disease adaptation. These findings are important to the natural history of disease evolution and the spread of drug-resistant strains.

A marginal benefit approach for vaccinating influenza "superspreaders"

BACKGROUND

There is widespread recognition that interventions targeting "superspreaders" are more effective at containing epidemics than strategies aimed at the broader

POPULATION

However, little attention has been devoted to determining optimal levels of coverage for targeted vaccination strategies, given the nonlinear relationship between program scale and the costs and benefits of identifying and successfully administering vaccination to potential superspreaders.

METHODS

We developed a framework for such an assessment derived from a transmission model of seasonal influenza parameterized to emulate typical seasonal influenza epidemics in the US. We used this framework to estimate how the marginal benefit of expanded targeted vaccination changes with the proportion of the target population already vaccinated.

RESULTS

The benefit of targeting additional superspreaders varies considerably as a function of both the baseline vaccination coverage and proximity to the herd immunity threshold. The general form of the marginal benefit function starts low, particularly for severe epidemics, increases monotonically until its peak at the point of herd immunity, and then plummets rapidly. We present a simplified transmission model, primarily designed to convey qualitative insight rather than quantitative precision. With appropriate contact data, future work could address more complex population structures, such as age structure and assortative mixing patterns. Our illustrative example highlights the general economic and epidemiological findings of our method but does not address intervention design, policy, and resource allocation issues related to practical implementation of this particular scenario.

CONCLUSIONS

Our approach offers a means of estimating willingness to pay for search costs associated with targeted vaccination of superspreaders, which can inform policies regarding whether a targeted intervention should be implemented and, if so, up to what levels.

MHC class II diversity of koala (Phascolarctos cinereus) populations across their range

Major histocompatibility complex class II (MHCII) genes code for proteins that bind and present antigenic peptides and trigger the adaptive immune response. We present a broad geographical study of MHCII DA β1 (DAB) and DB β1 (DBB) variants of the koala (Phascolarctos cinereus; n=191) from 12 populations across eastern Australia, with a total of 13 DAB and 7 DBB variants found. We identified greater MHCII variation and, possibly, additional gene copies in koala populations in the north (Queensland and New South Wales) relative to the south (Victoria), confirmed by STRUCTURE analyses and genetic differentiation using analysis of molecular variance. The higher MHCII diversity in the north relative to south could potentially be attributed to (i) significant founder effect in Victorian populations linked to historical translocation of bottlenecked koala populations and (ii) increased pathogen-driven balancing selection and/or local genetic drift in the north. Low MHCII genetic diversity in koalas from the south could reduce their potential response to disease, although the three DAB variants found in the south had substantial sequence divergence between variants. This study assessing MHCII diversity in the koala with historical translocations in some populations contributes to understanding the effects of population translocations on functional genetic diversity.

Evolution and emergence of plant viruses

Viruses are common agents of plant infectious diseases. During last decades, worldwide agriculture production has been compromised by a series of epidemics caused by new viruses that spilled over from reservoir species or by new variants of classic viruses that show new pathogenic and epidemiological properties. Virus emergence has been generally associated with ecological change or with intensive agronomical practices. However, the complete picture is much more complex since the viral populations constantly evolve and adapt to their new hosts and vectors. This chapter puts emergence of plant viruses into the framework of evolutionary ecology, genetics, and epidemiology. We will stress that viral emergence begins with the stochastic transmission of preexisting genetic variants from the reservoir to the new host, whose fate depends on their fitness on each hosts, followed by adaptation to new hosts or vectors, and finalizes with an efficient epidemiological spread.

Epidemiological feedbacks affect evolutionary emergence of pathogens

The evolutionary emergence of new pathogens via mutation poses a considerable risk to human and animal populations. Most previous studies have investigated cases where a potentially pandemic strain emerges though mutation from an initial maladapted strain (i.e., its basic reproductive ratio R0 < 1). However, an alternative (and arguably more likely) cause of novel pathogen emergence is where a "weakly adapted" strain (with R0 ≈ 1) mutates into a strongly adapted strain (with R0 ≫ 1). In this case, a proportion of the host susceptible population is removed as the first strain spreads, but the impact this feedback has on emergence of mutated strains has yet to be quantified. We produce a model of pathogen emergence that takes into account changes in the susceptible population over time and find that the ongoing depletion of susceptible individuals by the first strain has a drastic effect on the emergence probability of the mutated strain, above that assumed by just scaling the reproductive ratio. Finally, we apply our model to the documented emergence of Chikungunya virus on La Réunion Island and demonstrate that the emergence probability of the mutated strain was reduced approximately 10-fold, compared to models assuming that susceptible depletion would not affect outbreak probability. These results highlight the importance of taking population feedbacks into account when predicting disease emergence.

Introducing the outbreak threshold in epidemiology

When a pathogen is rare in a host population, there is a chance that it will die out because of stochastic effects instead of causing a major epidemic. Yet no criteria exist to determine when the pathogen increases to a risky level, from which it has a large chance of dying out, to when a major outbreak is almost certain. We introduce such an outbreak threshold (T₀), and find that for large and homogeneous host populations, in which the pathogen has a reproductive ratio R₀, on the order of 1/Log(R₀) infected individuals are needed to prevent stochastic fade-out during the early stages of an epidemic. We also show how this threshold scales with higher heterogeneity and R₀ in the host population. These results have implications for controlling emerging and re-emerging pathogens.

What limits the evolutionary emergence of pathogens?

The ability of a pathogen to cause an epidemic when introduced in a new host population often relies on its ability to adapt to this new environment. Here, we give a brief overview of recent theoretical and empirical studies of such evolutionary emergence of pathogens. We discuss the effects of several ecological and genetic factors that may affect the likelihood of emergence: migration, life history of the infectious agent, host heterogeneity, and the rate and effects of mutations. We contrast different modelling approaches and indicate how details in the way we model each step of a life cycle can have important consequences on the predicted probability of evolutionary emergence. These different theoretical perspectives yield important insights into optimal surveillance and intervention strategies, which should aim for a reduction in the emergence (and re-emergence) of infectious diseases.

Multiple scales of selection influence the evolutionary emergence of novel pathogens

When pathogens encounter a novel environment, such as a new host species or treatment with an antimicrobial drug, their fitness may be reduced so that adaptation is necessary to avoid extinction. Evolutionary emergence is the process by which new pathogen strains arise in response to such selective pressures. Theoretical studies over the last decade have clarified some determinants of emergence risk, but have neglected the influence of fitness on evolutionary rates and have not accounted for the multiple scales at which pathogens must compete successfully. We present a cross-scale theory for evolutionary emergence, which embeds a mechanistic model of within-host selection into a stochastic model for emergence at the population scale. We explore how fitness landscapes at within-host and between-host scales can interact to influence the probability that a pathogen lineage will emerge successfully. Results show that positive correlations between fitnesses across scales can greatly facilitate emergence, while cross-scale conflicts in selection can lead to evolutionary dead ends. The local genotype space of the initial strain of a pathogen can have disproportionate influence on emergence probability. Our cross-scale model represents a step towards integrating laboratory experiments with field surveillance data to create a rational framework to assess emergence risk.

Bias, accuracy, and impact of indirect genetic effects in infectious diseases

Selection for improved host response to infectious disease offers a desirable alternative to chemical treatment but has proven difficult in practice, due to low heritability estimates of disease traits. Disease data from field studies is often binary, indicating whether an individual has become infected or not following exposure to an infectious disease. Numerous studies have shown that from this data one can infer genetic variation in individuals’ underlying susceptibility. In a previous study, we showed that with an indirect genetic effect (IGE) model it is possible to capture some genetic variation in infectivity, if present, as well as in susceptibility. Infectivity is the propensity of transmitting infection upon contact with a susceptible individual. It is an important factor determining the severity of an epidemic. However, there are severe shortcomings with the Standard IGE models as they do not accommodate the dynamic nature of disease data. Here we adjust the Standard IGE model to (1) make expression of infectivity dependent on the individuals’ disease status (Case Model) and (2) to include timing of infection (Case-ordered Model). The models are evaluated by comparing impact of selection, bias, and accuracy of each model using simulated binary disease data. These were generated for populations with known variation in susceptibility and infectivity thus allowing comparisons between estimated and true breeding values. Overall the Case Model provided better estimates for host genetic susceptibility and infectivity compared to the Standard Model in terms of bias, impact, and accuracy. Furthermore, these estimates were strongly influenced by epidemiological characteristics. However, surprisingly, the Case-Ordered model performed considerably worse than the Standard and the Case Models, pointing toward limitations in incorporating disease dynamics into conventional variance component estimation methodology and software used in animal breeding.

Does genetic diversity limit disease spread in natural host populations?

It is a commonly held view that genetically homogenous host populations are more vulnerable to infection than genetically diverse populations. The underlying idea, known as the 'monoculture effect,' is well documented in agricultural studies. Low genetic diversity in the wild can result from bottlenecks (that is, founder effects), biparental inbreeding or self-fertilization, any of which might increase the risk of epidemics. Host genetic diversity could buffer populations against epidemics in nature, but it is not clear how much diversity is required to prevent disease spread. Recent theoretical and empirical studies, particularly in Daphnia populations, have helped to establish that genetic diversity can reduce parasite transmission. Here, we review the present theoretical work and empirical evidence, and we suggest a new focus on finding 'diversity thresholds.'

The effect of population heterogeneities upon spread of infection

It has often been observed that population heterogeneities can lead to outbreaks of infection being less frequent and less severe than homogeneous population models would suggest. We address this issue by comparing a model incorporating various forms of heterogeneity with a homogenised model matched according to the value of the basic reproduction number [Formula: see text]. We mainly focus upon heterogeneity in individuals' infectivity and susceptibility, though with some allowance also for heterogeneous patterns of mixing. The measures of infectious spread we consider are (i) the probability of a major outbreak; (ii) the mean outbreak size; (iii) the mean endemic prevalence level; and (iv) the persistence time. For each measure, we establish conditions under which heterogeneity leads to a reduction in infectious spread. We also demonstrate that if such conditions are not satisfied, the reverse may occur. As well as comparison with a homogeneous population, we investigate comparisons between two heterogeneous populations of differing degrees of heterogeneity. All of our results are derived under the assumption that the susceptible population is sufficiently large.

Indirect genetic effects and the spread of infectious disease: are we capturing the full heritable variation underlying disease prevalence?

Reducing disease prevalence through selection for host resistance offers a desirable alternative to chemical treatment. Selection for host resistance has proven difficult, however, due to low heritability estimates. These low estimates may be caused by a failure to capture all the relevant genetic variance in disease resistance, as genetic analysis currently is not taylored to estimate genetic variation in infectivity. Host infectivity is the propensity of transmitting infection upon contact with a susceptible individual, and can be regarded as an indirect effect to disease status. It may be caused by a combination of physiological and behavioural traits. Though genetic variation in infectivity is difficult to measure directly, Indirect Genetic Effect (IGE) models, also referred to as associative effects or social interaction models, allow the estimation of this variance from more readily available binary disease data (infected/non-infected). We therefore generated binary disease data from simulated populations with known amounts of variation in susceptibility and infectivity to test the adequacy of traditional and IGE models. Our results show that a conventional model fails to capture the genetic variation in infectivity inherent in populations with simulated infectivity. An IGE model, on the other hand, does capture some of the variation in infectivity. Comparison with expected genetic variance suggests that there is scope for further methodological improvement, and that potential responses to selection may be greater than values presented here. Nonetheless, selection using an index of estimated direct and indirect breeding values was shown to have a greater genetic selection differential and reduced future disease risk than traditional selection for resistance only. These findings suggest that if genetic variation in infectivity substantially contributes to disease transmission, then breeding designs which explicitly incorporate IGEs might help reduce disease prevalence.

High host density favors greater virulence: a model of parasite-host dynamics based on multi-type branching processes

We use a multitype continuous time Markov branching process model to describe the dynamics of the spread of parasites of two types that can mutate into each other in a common host population. While most mathematical models for the virulence of infectious diseases focus on the interplay between the dynamics of host populations and the optimal characteristics for the success of the pathogen, our model focuses on how pathogen characteristics may change at the start of an epidemic, before the density of susceptible hosts decline. We envisage animal husbandry situations where hosts are at very high density and epidemics are curtailed before host densities are much reduced. The use of three pathogen characteristics: lethality, transmissibility and mutability allows us to investigate the interplay of these in relation to host density. We provide some numerical illustrations and discuss the effects of the size of the enclosure containing the host population on the encounter rate in our model that plays the key role in determining what pathogen type will eventually prevail. We also present a multistage extension of the model to situations where there are several populations and parasites can be transmitted from one of them to another. We conclude that animal husbandry situations with high stock densities will lead to very rapid increases in virulence, where virulent strains are either more transmissible or favoured by mutation. Further the process is affected by the nature of the farm enclosures.

Implications of host genetic variation on the risk and prevalence of infectious diseases transmitted through the environment

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.

Notions of synergy for combinations of interventions against infectious diseases in heterogeneously mixing populations

In public health programmes interventions are frequently combined with hoped for 'synergies'[22]. However, there is not yet a precise definition for synergy between interventions that captures the idea that there is added benefit at the population-level in using them together. To explore the synergy between interventions in the context of endemic disease, we consider a general model of infection spread in a heterogeneously mixing population. We consider interventions which may alter individuals' infectiousness, susceptibility, profile of infectiousness through time and survival while infected. Allowing general patterns of overlap and targeting in those receiving the interventions, we show how to compute changes to epidemiological indices such as R(0), and introduce a simple technique for calculating equilibrium prevalences and incidences via an iterated map. We argue for a particular definition of synergy and investigate its behaviour, both analytically and numerically, concluding that it is easiest to achieve synergy between interventions which perform poorly in isolation; implementation strategies that minimize the overlap of different interventions in the population tend to achieve more synergy; and that in populations with heterogeneous risk, interventions that are redundant when universally targeted can regain substantial synergy when applied in a targeted manner.

Insights into the evolution and emergence of a novel infectious disease

Many zoonotic, novel infectious diseases in humans appear as sporadic infections with spatially and temporally restricted outbreaks, as seen with influenza A(H5N1). Adaptation is often a key factor for successfully establishing sustained human-to-human transmission. Here we use simple mathematical models to describe different adaptation scenarios with particular reference to spatial heterogeneity within the human population. We present analytical expressions for the probability of emergence per introduction, as well as the waiting time to a successful emergence event. Furthermore, we derive general analytical results for the statistical properties of emergence events, including the probability distribution of outbreak sizes. We compare our analytical results with a stochastic model, which has previously been studied computationally. Our results suggest that, for typical connection strengths between communities, spatial heterogeneity has only a weak effect on outbreak size distributions, and on the risk of emergence per introduction. For example, if or larger, any village connected to a large city by just ten commuters a day is, effectively, just a part of the city when considering the chances of emergence and the outbreak size distribution. We present empirical data on commuting patterns and show that the vast majority of communities for which such data are available are at least this well interconnected. For plausible parameter ranges, the effects of spatial heterogeneity are likely to be dominated by the evolutionary biology of host adaptation. We conclude by discussing implications for surveillance and control of emerging infections.

Emerging infections are a continuing global public health issue, the most recent example being last year's ‘Swine flu’ influenza pandemic. However, for many zoonotic pathogens, some adaptation is required to cross the species barrier from an animal reservoir into humans and cause sustained transmission. Previous work has explored the relationship between the evolutionary biology of an adapting pathogen, and the epidemiology of cases that may arise before such a pathogen becomes pandemic-capable. Here, we extend this work to incorporate what is often an important host ecological feature, the spatial distribution of the host population. Many zoonoses occur away from large population centres. For example, HIV is thought to have entered the human population through bushmeat hunters in the sparsely populated jungles of Central Africa. We ask: when a pathogen is evolving to adapt for human transmission, under what circumstances does the spatial structure underlying the human population become important? We approach this question using mathematical models to explore regimes of connectedness between communities. Our results suggest that most communities are sufficiently interconnected to show no effect on the emergence process. We finish by discussing the implications of these findings for public health.

The effect of host genetic diversity on disease spread

Host genetic diversity is thought to reduce the likelihood that disease will spread in natural populations. In this study, I present an epidemiological model for the intrinsic rate of spread (R0) for an infectious disease. The results show that the average value for R0 (R0) is inversely related to the number of host genotypes in the population (G), assuming that each host genotype is susceptible to a different parasite genotype. Specifically, for large host populations, R0 is equal to B/G, where B is the number of infectious propagules produced by each infection that contact a different host. The results also suggest that virulent, single-strain infections, which initially spread in genetically diverse host populations, would quickly die out when the parasite depresses the frequency of susceptible hosts below 1/B. These results are consistent with empirical studies showing that genetically diverse host populations suffer less from pathogens and parasites.

Experimental evidence that source genetic variation drives pathogen emergence

A pathogen can readily mutate to infect new host types, but this does not guarantee successful establishment in the new habitat. What factors, then, dictate emergence success? One possibility is that the pathogen population cannot sustain itself on the new host type (i.e. host is a sink), but migration from a source population allows adaptive sustainability and eventual emergence by delivering beneficial mutations sampled from the source's standing genetic variation. This idea is relevant regardless of whether the sink host is truly novel (host shift) or whether the sink is an existing or related, similar host population thriving under conditions unfavourable to pathogen persistence (range expansion). We predicted that sink adaptation should occur faster under range expansion than during a host shift owing to the effects of source genetic variation on pathogen adaptability in the sink. Under range expansion, source migration should benefit emergence in the sink because selection acting on source and sink populations is likely to be congruent. By contrast, during host shifts, source migration is likely to disrupt emergence in the sink owing to uncorrelated selection or performance tradeoffs across host types. We tested this hypothesis by evolving bacteriophage populations on novel host bacteria under sink conditions, while manipulating emergence via host shift versus range expansion. Controls examined sink adaptation when unevolved founding genotypes served as migrants. As predicted, adaptability was fastest under range expansion, and controls did not adapt. Large, similar and similarly timed increases in fitness were observed in the host-shift populations, despite declines in mean fitness of immigrants through time. These results suggest that source populations are the origin of mutations that drive adaptive emergence at the edge of a pathogen's ecological or geographical range.

Risk factors for the evolutionary emergence of pathogens

Recent outbreaks of novel infectious diseases (e.g. SARS, influenza H1N1) have highlighted the threat of cross-species pathogen transmission. When first introduced to a population, a pathogen is often poorly adapted to its new host and must evolve in order to escape extinction. Theoretical arguments and empirical studies have suggested various factors to explain why some pathogens emerge and others do not, including host contact structure, pathogen adaptive pathways and mutation rates. Using a multi-type branching process, we model the spread of an introduced pathogen evolving through several strains. Extending previous models, we use a network-based approach to separate host contact patterns from pathogen transmissibility. We also allow for arbitrary adaptive pathways. These generalizations lead to novel predictions regarding the impact of hypothesized risk factors. Pathogen fitness depends on the host population in which it circulates, and the ‘riskiest’ contact distribution and adaptive pathway depend on initial transmissibility. Emergence probability is sensitive to mutation probabilities and number of adaptive steps required, with the possibility of large adaptive steps (e.g. simultaneous point mutations or recombination) having a dramatic effect. In most situations, increasing overall mutation probability increases the risk of emergence; however, notable exceptions arise when deleterious mutations are available.

Pathogen dose infectivity curves as a method to analyze the distribution of host susceptibility: a quantitative assessment of maternal effects after food stress and pathogen exposure

Stress conditions have been found to change the susceptibility of hosts or their offspring to infection. The usual method of testing at just one parasite dose level does not allow conclusions on the distribution of susceptibility. To better understand the epidemiology and evolution of host-parasite systems, however, knowledge about the distribution of host susceptibility, the parameters that characterize it, and how it changes in response to environmental conditions is required. We investigated transgenerational effects of different stress factors by exposing Daphnia magna to standard conditions, to low food levels, or to a high dose of the bacterial pathogen Pasteuria ramosa and then measuring the susceptibility of the offspring to different spore doses of the parasite. For the analysis we used a mathematical model that predicts the fraction of infected hosts at different parasite doses, allowing us to estimate the mean and variance of host susceptibility. We find that low food levels reduce both the mean and the variance of offspring susceptibility. Parasite exposure, on the other hand, widens the offspring's susceptibility distribution without affecting its mean. Our analysis uncovered previously unknown transgenerational effects on the distribution of susceptibilities. The finding of an alteration in the variance of susceptibility to infection has implications for host and parasite dynamics and can contribute to our understanding of the stability of host-parasite interactions.

Bacteriophages as model organisms for virus emergence research

Viruses fully emerge by gaining the ability to sustainably infect new host populations. When the hosts are humans, emerging viruses can present major public health issues, as exemplified by the AIDS pandemic. Therefore, heuristic approaches to identify nascent diseases before they become pandemic would be valuable. Unfortunately, the current patient-based and epidemiological approaches are ill-suited in this regard because they are largely responsive and not predictive. Alternative approaches based on virus evolutionary ecology might have greater potential to predict virus emergence. However, given the difficulties encountered when studying metazoan viruses in this context, the development of new model systems is greatly desirable. Here, I highlight studies that show that bacteriophages are appropriate model organisms for virus emergence research because of the ease in which important population parameters can be manipulated. Ideally this research will permit identifying major factors determining the persistence or extinction of emerging viruses. If such viruses could be recognized in advance, patient-based and epidemiological strategies could be better mobilized to deal with them.