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

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

Crayfish population size under different routes of pathogen transmission

We present an epidemiological model for the crayfish plague, a disease caused by an invasive oomycete Aphanomyces astaci, and its general susceptible freshwater crayfish host. The pathogen shows high virulence with resulting high mortality rates in freshwater crayfishes native to Europe, Asia, Australia, and South America. The crayfish plague occurrence shows complicated dynamics due to the several types of possible infection routes, which include cannibalism and necrophagy. We explore this complexity by addressing the roles of host cannibalism and the multiple routes of transmission through (1) environment, (2) contact, (3) cannibalism, and (4) scavenging of infected carcasses. We describe a compartment model having six classes of crayfish and a pool of crayfish plague spores from a single nonevolving strain. We show that environmental transmission is the decisive factor in the development of epidemics. Compared with a pathogen-free crayfish population, the presence of the pathogen with a low environmental transmission rate, regardless of the contact transmission rate, decreases the crayfish population size with a low risk of extinction. Conversely, a high transmission rate could drive both the crayfish and pathogen populations to extinction. High contact transmission rate with a low but nonzero environmental transmission rate can have mixed outcomes from extinction to large healthy population, depending on the initial values. Scavenging and cannibalism have a relevant role only when the environmental transmission rate is low, but scavenging can destabilize the system by transmitting the pathogen from a dead to a susceptible host. To the contrary, cannibalism stabilizes the dynamics by decreasing the proportion of infected population. Our model provides a simple tool for further analysis of complex host parasite dynamics and for the general understanding of crayfish disease dynamics in the wild.     

Disease evolution across a range of spatio-temporal scales

Traditional explorations of infectious disease evolution have considered the competition between two cross-reactive strains within the standard framework of disease models. Such techniques predict that diseases should evolve to be highly transmissible, benign to the host and possess a long infectious period: in general, diseases do not conform to this ideal. Here we consider a more holistic approach, suggesting that evolution is a trade-off between adaptive pressures at different scales: within host, between hosts and at the population level. We present a model combining within-host pathogen dynamics and transmission between individuals governed by an explicit contact network, where transmission dynamics between hosts are a function of the interaction between the pathogen and the hosts' immune system, though ultimately constrained by the contacts each infected host possesses. Our results show how each of the scales places constraints on the evolutionary behavior, and that complex dynamics may emerge due to the feedbacks between epidemiological and evolutionary dynamics. In particular, multiple stable states can occur with switching between them stochastically driven.     

Host community structure and the maintenance of pathogen diversity

Community structure has been widely identified as a feature of many real-world networks. It has been shown that the antigenic diversity of a pathogen population can be significantly affected by the contact network of its hosts; however, the effects of community structure have not yet been explored. Here, we examine the congruence between patterns of antigenic diversity in pathogen populations in neighbouring communities, using both a deterministic metapopulation model and individual-based formulations. We show that the spatial differentiation of the pathogen population can only be maintained at levels of coupling far lower than that necessary for the host populations to remain distinct. Therefore, identifiable community structure in host networks may not reflect differentiation of the processes occurring upon them and, conversely, a lack of genetic differentiation between pathogens from different host communities may not reflect strong mixing between them.     

Not all types of host contacts are equal when it comes to E. coli transmission

The specific processes that facilitate pathogen transmission are poorly understood, particularly for wild animal populations. A major impediment for investigating transmission pathways is the need for simultaneous information on host contacts and pathogen transfer. In this study, we used commensal Escherichia coli strains as a model system for gastrointestinal pathogens. We combined strain‐sharing information with detailed host contact data to investigate transmission routes in mountain brushtail possums. Despite E. coli being transmitted via the faecal‐oral route, we revealed that, strain‐sharing among possums was better explained by host contacts than spatial proximity. Furthermore, and unexpectedly, strain‐sharing was more strongly associated with the duration of brief nocturnal associations than day‐long den‐sharing. Thus, the most cryptic and difficult associations to measure were the most relevant connections for the transmission of this symbiont. We predict that future studies that employ similar approaches will reveal the importance of previously overlooked associations as key transmission pathways.     

Allocation to sexual versus nonsexual disease transmission

Many diseases have both sexual and nonsexual transmission routes, and closely related diseases often differ in their degree of sexual transmission. We investigate the evolution of transmission mode as a function of host social and mating structure using a model in which disease transmission is explicitly dependent on the numbers of sexual and nonsexual contacts (which are themselves a function of population density) and per-contact infection probabilities. Most generally, and in the absence of trade-offs between the degree of sexual transmission and effects on host fecundity and mortality, nonsexual transmission is favored above the social-sexual crossover point (the host density at which the number of nonsexual contacts exceeds the number of sexual contacts), while sexual transmission is favored below this point. When changes in allocation to the two transmission modes are accompanied by changes in mortality or fecundity, both mixed and pure transmission strategies can be favored. If invading genotypes differ substantially from resident genotypes, genetic polymorphism in transmission mode is possible. The evolutionary outcomes are predictable from a knowledge of the equilibrium population sizes in relation to the social-sexual crossover point. Our results also show that predictions about dynamic outcomes, based on rates of invasion for single pathogens into healthy populations, do not adequately describe the resulting disease prevalence nor predict the subsequent evolutionary dynamics; once invasion of a pathogen has occurred, the conditions for spread of a second pathogen are themselves altered. If the host is considered as a single resource, our results show that two pathogens may coexist on a single resource if they use that resource differentially and have differential feedbacks on resource abundance; such resource feedback effects may be present in other biological systems.     

Antigenic Diversity,Transmission Mechanisms,and the Evolution of Pathogens

Pathogens have evolved diverse strategies to maximize their transmission fitness. Here we investigate these strategies for directly transmitted pathogens using mathematical models of disease pathogenesis and transmission, modeling fitness as a function of within- and between-host pathogen dynamics. The within-host model includes realistic constraints on pathogen replication via resource depletion and cross-immunity between pathogen strains. We find three distinct types of infection emerge as maxima in the fitness landscape, each characterized by particular within-host dynamics, host population contact network structure, and transmission mode. These three infection types are associated with distinct non-overlapping ranges of levels of antigenic diversity, and well-defined patterns of within-host dynamics and between-host transmissibility. Fitness, quantified by the basic reproduction number, also falls within distinct ranges for each infection type. Every type is optimal for certain contact structures over a range of contact rates. Sexually transmitted infections and childhood diseases are identified as exemplar types for low and high contact rates, respectively. This work generates a plausible mechanistic hypothesis for the observed tradeoff between pathogen transmissibility and antigenic diversity, and shows how different classes of pathogens arise evolutionarily as fitness optima for different contact network structures and host contact rates.     

How the Dynamics and Structure of Sexual Contact Networks Shape Pathogen Phylogenies

The characteristics of the host contact network over which a pathogen is transmitted affect both epidemic spread and the projected effectiveness of control strategies. Given the importance of understanding these contact networks, it is unfortunate that they are very difficult to measure directly. This challenge has led to an interest in methods to infer information about host contact networks from pathogen phylogenies, because in shaping a pathogen''s opportunities for reproduction, contact networks also shape pathogen evolution. Host networks influence pathogen phylogenies both directly, through governing opportunities for evolution, and indirectly by changing the prevalence and incidence. Here, we aim to separate these two effects by comparing pathogen evolution on different host networks that share similar epidemic trajectories. This approach allows use to examine the direct effects of network structure on pathogen phylogenies, largely controlling for confounding differences arising from population dynamics. We find that networks with more heterogeneous degree distributions yield pathogen phylogenies with more variable cluster numbers, smaller mean cluster sizes, shorter mean branch lengths, and somewhat higher tree imbalance than networks with relatively homogeneous degree distributions. However, in particular for dynamic networks, we find that these direct effects are relatively modest. These findings suggest that the role of the epidemic trajectory, the dynamics of the network and the inherent variability of metrics such as cluster size must each be taken into account when trying to use pathogen phylogenies to understand characteristics about the underlying host contact network.     

Evaluating the importance of within- and between-host selection pressures on the evolution of chronic pathogens

Infectious pathogens compete and are subject to natural selection at multiple levels. For example, viral strains compete for access to host resources within an infected host and, at the same time, compete for access to susceptible hosts within the host population. Here we propose a novel approach to study the interplay between within- and between-host competition. This approach allows for a single host to be infected by and transmit two strains of the same pathogen. We do this by nesting a model for the host–pathogen dynamics within each infected host into an epidemiological model. The nesting of models allows the between-host infectivity and mortality rates suffered by infected hosts to be functions of the disease progression at the within-host level. We present a general method for computing the basic reproduction ratio of a pathogen in such a model. We then illustrate our method using a basic model for the within-host dynamics of viral infections, embedded within the simplest susceptible–infected (SI) epidemiological model. Within this nested framework, we show that the virion production rate at the level of the cell–virus interaction leads, via within-host competition, to the presence or absence of between-host level competitive exclusion. In particular, we find that in the absence of mutation the strain that maximizes between-host fitness can outcompete all other strains. In the presence of mutation we observe a complex invasion landscape showing the possibility of coexistence. Although we emphasize the application to human viral diseases, we expect this methodology to be applicable to be many host–parasite systems.     

A baseline model for the apparent competition between many host strains: the evolution of host resistance to microparasites.

The purpose of this article is to establish and analyse a baseline model for the apparent competition between many host strains attempting to avoid a uniform microparasitic population. The model is formulated and analysed using invasion criteria in the main text. The results are verified by more formal methods in the appendix. Cases in which the microparasite can invade are distinguished geometrically from those in which it cannot using threshold and strain composition conditions. A major result obtained when the pathogen persists is a competitive exclusion principle for host resistance. For non-lethal infections, the winning strain is that which affords the pathogen maximum threshold density; for possibly lethal infections, a somewhat generalized version of this criterion is presented and discussed. The tension is highlighted between these results and the baseline behaviour of many pathogen strains and a uniform host population-here the winning pathogen strain is that with minimum threshold density.     

Interactions between Social Structure,Demography, and Transmission Determine Disease Persistence in Primates

Catastrophic declines in African great ape populations due to disease outbreaks have been reported in recent years, yet we rarely hear of similar disease impacts for the more solitary Asian great apes, or for smaller primates. We used an age-structured model of different primate social systems to illustrate that interactions between social structure and demography create ‘dynamic constraints’ on the pathogens that can establish and persist in primate host species with different social systems. We showed that this varies by disease transmission mode. Sexually transmitted infections (STIs) require high rates of transmissibility to persist within a primate population. In particular, for a unimale social system, STIs require extremely high rates of transmissibility for persistence, and remain at extremely low prevalence in small primates, but this is less constrained in longer-lived, larger-bodied primates. In contrast, aerosol transmitted infections (ATIs) spread and persist at high prevalence in medium and large primates with moderate transmissibility;, establishment and persistence in small-bodied primates require higher relative rates of transmissibility. Intragroup contact structure – the social network - creates different constraints for different transmission modes, and our model underscores the importance of intragroup contacts on infection prior to intergroup movement in a structured population. When alpha males dominate sexual encounters, the resulting disease transmission dynamics differ from when social interactions are dominated by mother-infant grooming events, for example. This has important repercussions for pathogen spread across populations. Our framework reveals essential social and demographic characteristics of primates that predispose them to different disease risks that will be important for disease management and conservation planning for protected primate populations.     

Effects of heterogeneous and clustered contact patterns on infectious disease dynamics

The spread of infectious diseases fundamentally depends on the pattern of contacts between individuals. Although studies of contact networks have shown that heterogeneity in the number of contacts and the duration of contacts can have far-reaching epidemiological consequences, models often assume that contacts are chosen at random and thereby ignore the sociological, temporal and/or spatial clustering of contacts. Here we investigate the simultaneous effects of heterogeneous and clustered contact patterns on epidemic dynamics. To model population structure, we generalize the configuration model which has a tunable degree distribution (number of contacts per node) and level of clustering (number of three cliques). To model epidemic dynamics for this class of random graph, we derive a tractable, low-dimensional system of ordinary differential equations that accounts for the effects of network structure on the course of the epidemic. We find that the interaction between clustering and the degree distribution is complex. Clustering always slows an epidemic, but simultaneously increasing clustering and the variance of the degree distribution can increase final epidemic size. We also show that bond percolation-based approximations can be highly biased if one incorrectly assumes that infectious periods are homogeneous, and the magnitude of this bias increases with the amount of clustering in the network. We apply this approach to model the high clustering of contacts within households, using contact parameters estimated from survey data of social interactions, and we identify conditions under which network models that do not account for household structure will be biased.     

A baseline model for the co-evolution of hosts and pathogens

The basic reproduction ratio (R (0)) is the expected number of secondary cases per primary in a totally susceptible population. In a baseline model, faced with an individual host strain pathogen virulence evolves to maximise R (0) which yields monomorphism. The basic depression ratio (D (0)) is the amount by which the total population is decreased, per infected individual, due to the presence of infection. Again, in a baseline model, faced with an individual pathogen strain host resistance evolves to minimise D (0) which yields monomorphism. With this in mind we analyse the community dynamics of the interaction between R (0) and D (0) and show that multi-strain co-existence (polymorphism) is possible and we discuss the possibility of stable cycles occuring within the co-existence states. We show for co-existence, the number of host and pathogen strains present need to be identical in order to achieve stable equilibria. For polymorphic states we observe contingencies (outcome dependent on initial conditions) between both point equilibrium and sustained oscillations. Invasion criteria for host and pathogen strains are identified.     

A deterministic model for influenza infection with multiple strains and antigenic drift

We describe a multiple strain Susceptible Infected Recovered deterministic model for the spread of an influenza subtype within a population. The model incorporates appearance of new strains due to antigenic drift, and partial immunity to reinfection with related circulating strains. It also includes optional seasonal forcing of the transmission rate of the virus, which allows for comparison between temperate zones and the tropics. Our model is capable of reproducing observed qualitative patterns such as the overall annual outbreaks in the temperate region, a reduced magnitude and an increased frequency of outbreaks in the tropics, and the herald wave phenomenon. Our approach to modelling antigenic drift is novel and further modifications of this model may help improve the understanding of complex influenza dynamics.     

Evolution of acuteness in pathogen metapopulations: conflicts between “classical” and invasion-persistence trade-offs

Classical life-history theory predicts that acute, immunizing pathogens should maximize between-host transmission. When such pathogens induce violent epidemic outbreaks, however, a pathogen’s short-term advantage at invasion may come at the expense of its ability to persist in the population over the long term. Here, we seek to understand how the classical and invasion-persistence trade-offs interact to shape pathogen life-history evolution as a function of the size and structure of the host population. We develop an individual-based infection model at three distinct levels of organization: within an individual host, among hosts within a local population, and among local populations within a metapopulation. We find a continuum of evolutionarily stable pathogen strategies. At one end of the spectrum—in large well-mixed populations—pathogens evolve to greater acuteness to maximize between-host transmission: the classical trade-off theory applies in this regime. At the other end of the spectrum—when the host population is broken into many small patches—selection favors less acute pathogens, which persist longer within a patch and thereby achieve enhanced between-patch transmission: the invasion-persistence trade-off dominates in this regime. Between these extremes, we explore the effects of the size and structure of the host population in determining pathogen strategy. In general, pathogen strategies respond to evolutionary pressures arising at both scales.     

Evaluating the importance of within- and between-host selection pressures on the evolution of chronic pathogens

Infectious pathogens compete and are subject to natural selection at multiple levels. For example, viral strains compete for access to host resources within an infected host and, at the same time, compete for access to susceptible hosts within the host population. Here we propose a novel approach to study the interplay between within- and between-host competition. This approach allows for a single host to be infected by and transmit two strains of the same pathogen. We do this by nesting a model for the host-pathogen dynamics within each infected host into an epidemiological model. The nesting of models allows the between-host infectivity and mortality rates suffered by infected hosts to be functions of the disease progression at the within-host level. We present a general method for computing the basic reproduction ratio of a pathogen in such a model. We then illustrate our method using a basic model for the within-host dynamics of viral infections, embedded within the simplest susceptible-infected (SI) epidemiological model. Within this nested framework, we show that the virion production rate at the level of the cell-virus interaction leads, via within-host competition, to the presence or absence of between-host level competitive exclusion. In particular, we find that in the absence of mutation the strain that maximizes between-host fitness can outcompete all other strains. In the presence of mutation we observe a complex invasion landscape showing the possibility of coexistence. Although we emphasize the application to human viral diseases, we expect this methodology to be applicable to be many host-parasite systems.     

A general host–pathogen model with free–living infective stages and differing rates of uptake of the infective stages by infected and susceptible hosts

In addition to their lethal effects, pathogens can cause a number of other debilitating effects on infected hosts. A population dynamical model of the interaction between an invertebrate host and a pathogen is constructed to examine the importance of one such debilitating effect on the host population dynamics. Specifically the feeding rate and therefore the uptake of pathogen free-living infective particles by infected individuals is reduced as a consequence of the pathogen infection. The pathogen is more likely to regulate the host and the equilibrium population density of the host is reduced. Less intuitively there is also an increased chance of the pathogen causing cyclic population dynamics in the host. Received: December 8, 1997 / Accepted: March 23, 1999     

Coexistence and specialization of pathogen strains on contact networks

The coexistence of different pathogen strains has implications for pathogen variability and disease control and has been explained in a number of different ways. We use contact networks, which represent interactions between individuals through which infection could be transmitted, to investigate strain coexistence. For sexually transmitted diseases the structure of contact networks has received detailed study and has been shown to be a vital determinant of the epidemiological dynamics. By using analytical pairwise models and stochastic simulations, we demonstrate that network structure also has a profound influence on the interaction between pathogen strains. In particular, when the population is serially monogamous, fully cross-reactive strains can coexist, with different strains dominating in network regions with different characteristics. Furthermore, we observe specialization of different strains in different risk groups within the network, suggesting the existence of diverging evolutionary pressures.