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Many vector-borne diseases are affected by the seasonality of the environment. Yet, seasonality can act on distinct steps of the life cycle of the pathogen and it is often difficult to predict the influence of the periodic fluctuations of the environment on the basic reproduction ratio R of vector-borne pathogens. Here, we analyse a general vector-borne disease model and we account for periodic fluctuations of different components of the pathogen's life cycle. We develop a perturbation analysis framework to obtain useful approximations to evaluate the overall consequences of seasonality on the 0 of vector-borne pathogens. Here, we analyse a general vector-borne disease model and we account for periodic fluctuations of different components of the pathogen's life cycle. We develop a perturbation analysis framework to obtain useful approximations to evaluate the overall consequences of seasonality on the R 0 of the pathogen. This analysis reveals when seasonality is expected to increase or to decrease pathogen persistence. We show that seasonality in vector density or in the biting rate of the vector can have opposite effects on persistence and we provide a useful biological explanation for this result based on the covariance between key compartments of the epidemiological model. This framework could be readily extended to explore the influence of seasonality on other components of the life cycle of vector-borne pathogens.

What is the influence of periodic environmental fluctuations on life-history evolution? We present a general theoretical framework to understand and predict the long-term evolution of life-history traits under a broad range of ecological scenarios. Specifically, we investigate how periodic fluctuations affect selection when the population is also structured in distinct classes. This analysis yields time-varying selection gradients that clarify the influence of the fluctuations of the environment on the competitive ability of a specific life-history mutation. We use this framework to analyse the evolution of key life-history traits of pathogens. We examine three different epidemiological scenarios and we show how periodic fluctuations of the environment can affect the evolution of virulence and transmission as well as the preference for different hosts. These examples yield new and testable predictions on pathogen evolution, and illustrate how our approach can provide a better understanding of the evolutionary consequences of time-varying environmental fluctuations in a broad range of scenarios., (© 2022 The Authors. Evolution published by Wiley Periodicals LLC on behalf of The Society for the Study of Evolution.)

Many infectious diseases exhibit seasonal dynamics driven by periodic fluctuations of the environment. Predicting the risk of pathogen emergence at different points in time is key for the development of effective public health strategies. Here we study the impact of seasonality on the probability of emergence of directly transmitted pathogens under different epidemiological scenarios. We show that when the period of the fluctuation is large relative to the duration of the infection, the probability of emergence varies dramatically with the time at which the pathogen is introduced in the host population. In particular, we identify a new effect of seasonality (the winter is coming effect) where the probability of emergence is vanishingly small even though pathogen transmission is high. We use this theoretical framework to compare the impact of different preventive control strategies on the average probability of emergence. We show that, when pathogen eradication is not attainable, the optimal strategy is to act intensively in a narrow time interval. Interestingly, the optimal control strategy is not always the strategy minimizing R0, the basic reproduction ratio of the pathogen. This theoretical framework is extended to study the probability of emergence of vector borne diseases in seasonal environments and we show how it can be used to improve risk maps of Zika virus emergence., Competing Interests: The authors have declared that no competing interests exist.

The theory of life-history evolution provides a powerful framework to understand the evolutionary dynamics of pathogens. It assumes, however, that host populations are large and that one can neglect the effects of demographic stochasticity. Here, we expand the theory to account for the effects of finite population size on the evolution of pathogen virulence. We show that demographic stochasticity introduces additional evolutionary forces that can qualitatively affect the dynamics and the evolutionary outcome. We discuss the importance of the shape of the pathogen fitness landscape on the balance between mutation, selection and genetic drift. This analysis reconciles Adaptive Dynamics with population genetics in finite populations and provides a new theoretical toolbox to study life-history evolution in realistic ecological scenarios., (© 2018 The Authors.)

The transmission of many animal and plant diseases relies on the behavior of arthropod vectors. In particular, the specific preference for infected or uninfected hosts observed in many vector species is expected to affect the circulation of vector-borne diseases. Here I develop a theoretical framework to study the epidemiology and evolution of the manipulation of host choice behavior of vectors. I show that vector preference strategies have dramatic epidemiological consequences. I also explore the evolution of vector host choice under different scenarios regarding control of the vector behavior by the pathogen. This analysis yields multiple evolutionary outcomes and explains the diversity of host choice behaviors observed in a broad range of vector-borne diseases. In particular, this analysis helps us understand why several pathogens have evolved manipulation strategies that vary with the infectious status of their vector species while other pathogens seem unable to evolve such complex conditional strategies. I argue that contrasting the behavior of infected and uninfected vectors is key to revealing the mechanistic constraints acting on the evolution of the manipulation of vector behavior.

Most spatial models of host-parasite interactions either neglect the possibility of pathogen evolution or consider that this process is slow enough for epidemiological dynamics to reach an equilibrium on a fast timescale. Here, we propose a novel approach to jointly model the epidemiological and evolutionary dynamics of spatially structured host and pathogen populations. Starting from a multi-strain epidemiological model, we use a combination of spatial moment equations and quantitative genetics to analyse the dynamics of mean transmission and virulence in the population. A key insight of our approach is that, even in the absence of long-term evolutionary consequences, spatial structure can affect the short-term evolution of pathogens because of the build-up of spatial differentiation in mean virulence. We show that spatial differentiation is driven by a balance between epidemiological and genetic effects, and this quantity is related to the effect of kin competition discussed in previous studies of parasite evolution in spatially structured host populations. Our analysis can be used to understand and predict the transient evolutionary dynamics of pathogens and the emergence of spatial patterns of phenotypic variation., (© 2016 The Author(s).)

Mathematical models have been powerful tools in developing mechanistic understanding of infectious diseases. Furthermore, they have allowed detailed forecasting of epidemiological phenomena such as outbreak size, which is of considerable public-health relevance. The short generation time of pathogens and the strong selection they are subjected to (by host immunity, vaccines, chemotherapy, etc.) mean that evolution is also a key driver of infectious disease dynamics. Accurate forecasting of pathogen dynamics therefore calls for the integration of epidemiological and evolutionary processes, yet this integration remains relatively rare. We review previous attempts to model and predict infectious disease dynamics with or without evolution and discuss major challenges facing the development of the emerging science of epidemic forecasting., (Copyright © 2016. Published by Elsevier Ltd.)

Patterns of local adaptation are expected to emerge when selection is spatially heterogeneous and sufficiently strong relative to the action of other evolutionary forces. The observation of local adaptation thus provides important insight into evolutionary processes and the adaptive divergence of populations. The detection of local adaptation, however, suffers from several conceptual, statistical and methodological issues. Here, we provide practical recommendations regarding (1) the definition of local adaptation, (2) the analysis of transplant experiments and (3) the optimisation of the experimental design of local adaptation studies. Together, these recommendations provide a unified approach for measuring local adaptation and understanding the adaptive divergence of populations in a wide range of biological systems., (© 2013 John Wiley & Sons Ltd/CNRS.)

Parasites with complex life cycles are expected to manipulate the behaviour of their intermediate hosts (IHs), which increase their predation rate and facilitate the transmission to definitive hosts (DHs). This ability, however, is a double-edged sword when the parasite can also be transmitted vertically in the IH. In this situation, as the manipulation of the IH behaviour increases the IH death rate, it conflicts with vertical transmission, which requires healthy and reproducing IHs. The protozoan Toxoplasma gondii, a widespread pathogen, combines both trophic and vertical transmission strategies. Is parasite manipulation of host behaviour still adaptive in this situation? We model the evolution of the IH manipulation by T. gondii to study the conflict between these two routes of transmission under different epidemiological situations. Model outputs show that manipulation is particularly advantageous for virulent strains and in epidemic situations, and that different levels of manipulation may evolve depending on the sex of the IH and the transmission routes considered. These results may help to understand the variability of strain characteristics encountered for T. gondii and may extend to other trophically transmitted parasites.

Among the wide variety of resistance mechanisms to parasitism, the transgenerational transfer of immunity from mother to offspring has largely been overlooked and never included in evolutionary or coevolutionary studies of resistance mechanisms. Here we study the evolution and coevolution of various resistance mechanisms with a special focus on maternal transfer of immunity. In particular we show that maternal transfer of immunity is only expected to evolve when cross immunity is high and when the pathogens have an intermediate virulence. We also show that the outcome of the coevolution between various resistance mechanisms depends critically on the life span of the host. We predict that short-lived species should invest in avoidance strategies, whereas long-lived species should invest in acquired resistance mechanisms. These results may help understanding the diversity of resistance strategies that have evolved in vertebrate species. Our framework also provides a general basis for the study of the evolution of other transgenerational resistance mechanisms., (© 2012 The Author(s). Evolution© 2012 The Society for the Study of Evolution.)

The ability to migrate can evolve in response to various forces. In particular, when selection is heterogeneous in space but constant in time, local adaptation induces a fitness cost on immigrants and selects against migration. The evolutionary outcome, however, is less clear when selection also varies temporally. Here, we present a two-locus model analyzing the effects of spatial and temporal variability in selection on the evolution of migration. The first locus is under temporally varying selection (various periodic functions are considered, but a general nonparametric framework is used), and the second locus is a modifier controlling migration ability. First, we study the dynamics of local adaptation and derive the migration rate that maximizes local adaptation as a function of the speed and geometry of the fluctuations in the environment. Second, we derive an analytical expression for the evolutionarily stable migration rate. When there is no cost of migration, we show that higher migration rates are favored when selection changes fast. When migration is costly, however, the evolutionarily stable migration rate is maximal for an intermediate speed of the variation of selection. This model may help in understanding the evolution of migration in a broad range of scenarios and, in particular, in host-parasite systems, where selection is thought to vary quickly in both space and time.

Latent viruses generally defend their host cell against superinfection by nonlatent virulent mutants that could destroy the host cell. Superinfection inhibition thus seems to be a prerequisite for the maintenance of viral latency. Yet viral latency can break down when resistance to superinfection inhibition, known as ultravirulence, occurs. To understand the evolution of viral latency, we have developed a model that analyzes the epidemiology of latent infection in the face of ultravirulence. We show that latency can be maintained when superinfection inhibition and resistance against it coevolve in an arms race, which can result in large fluctuations in virulence. An example is the coevolution of the virulence and superinfection repressor protein of phage lambda (cI) and its binding target, the lambda oLoR operator. We show that this repressor/operator coevolution is the driving force for the evolution of superinfection immunity groups. Beyond latent phages, we predict analogous dynamics for any latent virus that uses a single repressor for the simultaneous control of virulence and superinfection.

Hamilton's rule provides a general description of the conditions for the evolution of altruism. But altruism can take different forms depending on which life-history trait is affected by the helping behavior (fecundity vs. survival helping). In particular, these different forms of helping may have very different demographic consequences, which may feed back on evolution. We examine the interplay between various forms of helping and demography in viscous populations with empty sites. A key component of our analysis is the local density of empty sites experienced by a focal individual, which provides a measure of habitat saturation. Habitat saturation is shown to have contrasting effects depending on (1) whether the physiological costs and benefits of helping affect fecundity, survival or both; and (2) whether the costs of helping are paid in a density-dependent or density-independent manner. For a given level of habitat saturation and with density-dependent reproduction, we find that the conditions for the evolution of helping should be more favorable in the survival altruism life cycle with a cost on fecundity, and more stringent in the fecundity altruism life cycle with a cost on survival. More generally, our analysis stresses the importance of taking into account the feedback between population demography, life history, and kin selection when investigating the selective pressures on altruism.

The mean fitness of a population, often equal to its growth rate, measures its level of adaptation to particular environmental conditions. A better understanding of the evolution of mean fitness could thus provide a natural link between evolution and demography. Yet, after the seminal work of Fisher and its renowned "fundamental theorem of natural selection," the dynamics of mean fitness has attracted little attention, and mostly from theoretical population geneticists. Here we analyze the dynamics of mean fitness in the context of host-parasite interactions. We illustrate the potential relevance of this analysis under different scenarios ranging from a simple situation in which a parasite evolves in a homogeneous host population to a more complex one with host-parasite coevolution. In each case, we contrast the effects of natural selection, recurrent mutations, and the change of the biotic environment, on the dynamics of adaptation. Decoupling these three components helps elucidate the interplay between evolutionary and ecological dynamics. In particular, it offers new perspectives on situations leading to evolutionary suicide. As mean fitness is an easily measurable quantity in microbial systems, this analysis provides new ways to track the dynamics of adaptation in experimental evolution and coevolution studies.

How can we optimize the use of drugs against parasites to limit the evolution of drug resistance? This question has been addressed by many theoretical studies focusing either on the mixing of various treatments, or their temporal alternation. Here we consider a different treatment strategy where the use of the drug may vary in space to prevent the rise of drug-resistance. We analyze epidemiological models where drug-resistant and drug-sensitive parasites compete in a one-dimensional spatially heterogeneous environment. Two different parasite life-cycles are considered: (i) direct transmission between hosts, and (ii) vector-borne transmission. In both cases we find a critical size of the treated area, under which the drug-resistant strain cannot persist. This critical size depends on the basic reproductive ratios of each strain in each environment, on the ranges of dispersal, and on the duration of an infection with drug-resistant parasites. We discuss optimal treatment strategies that limit disease prevalence and the evolution of drug-resistance.

We explore the potential consequences of vaccination on parasite epidemiology and evolution. Our model combines a microscopic (within-host dynamics) and a macroscopic (epidemiological dynamics) description of the interaction between the parasite and its host. This approach allows relevant epidemiological traits such as parasite transmission, parasite virulence, and host recovery to emerge from a mechanistic model of acute infection describing the interaction between the parasite and the host immune system. We model the effect of a vaccine as an activator of immunity enhancing the replication rate of lymphocytes, their initial density at infection's initiation, their efficacy to kill the parasite, or their activation delay after infection. We analyze the evolution of the replication rate of parasites and show that vaccination may promote the evolution of faster replicating and, consequently, more virulent strains. We also show that intermediate vaccination coverage may lead to the coexistence of two different parasite strategies (a low-virulence strain adapted to naive hosts, and a high-virulence strain, more generalist, adapted to both naive and vaccinated hosts). We discuss the consequences of various vaccination strategies under different epidemiological situations using several distinct measures to evaluate the cost induced by the parasite on individuals and entire host populations.

Superparasitism refers to the oviposition behavior of parasitoid females who lay their eggs in an already parasitized host. This often yields intense competition among larvae that are sharing the same host. Why would a female oviposit in such hostile habitat instead of looking for a better quality, unparasitized host? Here we present a continuous-time model of host-parasitoid interaction and discuss alternative scenarios. This model is first used to analyze the evolution of the superparasitism behavior of a solitary proovigenic parasitoid under both time and egg limitation. Then, following the recent discovery by Varaldi et al., we allow the parasitoid to be infected by a virus that alters the superparasitism behavior of its host to enhance its own horizontal transmission. The analysis of the coevolution of this manipulative behavior with the oviposition behavior of uninfected females clarifies and quantifies the conflict that emerges between the parasitoid and its virus. The model also yields new testable predictions. For example, we expect that uninfected parasitoids should superparasite less after coevolving with the manipulative virus. More generally, this model provides a theoretical framework for analyzing the evolution of the manipulation of parasitoid life-history traits by microparasites.

Antagonistic coevolution between hosts and parasites in spatially structured populations can result in local adaptation of parasites; that is, the greater infectivity of local parasites than foreign parasites on local hosts. Such parasite specialization on local hosts has implications for human health and agriculture. By contrast with classic single-species population-genetic models, theory indicates that parasite migration between subpopulations might increase parasite local adaptation, as long as migration does not completely homogenize populations. To test this hypothesis we developed a system-specific mathematical model and then coevolved replicate populations of the bacterium Pseudomonas fluorescens and a parasitic bacteriophage with parasite only, with host only or with no migration. Here we show that patterns of local adaptation have considerable temporal and spatial variation and that, in the absence of migration, parasites tend to be locally maladapted. However, in accord with our model, parasite migration results in parasite local adaptation, but host migration alone has no significant effect.

Multihost parasites can infect different types of hosts or even different host species. Epidemiological models have shown the importance of the diversity of potential hosts for understanding the dynamics of infectious disease (e.g., the importance of reservoirs), but the consequences of this diversity for virulence and transmission evolution remain largely overlooked. Here, I present a general theoretical framework for the study of life-history evolution of multihost parasites. This analysis highlights the importance of epidemiology (the relative quality and quantity of different types of infected hosts) and between-trait constraints (both within and between different hosts) to parasite evolution. I illustrate these effects in different transmission scenarios under the simplifying assumption that parasites can infect only two types of hosts. These simple but contrasted evolutionary scenarios yield new insights into virulence evolution and the evolution of transmission routes among different hosts. Because many of the pathogens that have large public-health and agricultural impacts have complex life cycles, an understanding of their evolutionary dynamics could hold substantial benefits for management.

Vaccines rarely provide full protection from disease. Nevertheless, partially effective (imperfect) vaccines may be used to protect both individuals and whole populations. We studied the potential impact of different types of imperfect vaccines on the evolution of pathogen virulence (induced host mortality) and the consequences for public health. Here we show that vaccines designed to reduce pathogen growth rate and/or toxicity diminish selection against virulent pathogens. The subsequent evolution leads to higher levels of intrinsic virulence and hence to more severe disease in unvaccinated individuals. This evolution can erode any population-wide benefits such that overall mortality rates are unaffected, or even increase, with the level of vaccination coverage. In contrast, infection-blocking vaccines induce no such effects, and can even select for lower virulence. These findings have policy implications for the development and use of vaccines that are not expected to provide full immunity, such as candidate vaccines for malaria.

We present a general model of the evolution of dispersal in a population with any distribution of dispersal distance. We use this model to analyse evolutionarily stable (ES) dispersal rates for the classical island model of dispersal and for three different stepping-stone models. Using general techniques to compute relatedness coefficients in the different dispersal models which we consider, we find that the distribution of dispersal distance may affect the ES dispersal rate when the cost of dispersal is low. In this case the ES dispersal rate increases with the number of demes that can be reached by one dispersal event. However, for increasing cost the ES dispersal rate converges to a value independent of the distribution of dispersal distance. These results are in contrast to previous analyses of similar models. The effects of the size (number of demes) and shape (ratio between the width and the length) of the population on the evolution of dispersal are also studied. We find that larger and more elongated populations lead generally to higher ES dispersal rates. However, both of these effects can only be observed for extreme parameter values (i.e. for very small and very elongated populations). The direct fitness method and the analytical techniques used here to compute relatedness coefficients provide an efficient way to analyse ES strategies in subdivided populations.