Spatially structured eco-evolutionary dynamics in a host-pathogen interaction render isolated populations vulnerable to disease.

While the negative effects that pathogens have on their hosts are well-documented in humans and agricultural systems, direct evidence of pathogen-driven impacts in wild host populations is scarce and mixed. Here, to determine how the strength of pathogen-imposed selection depends on spatial structure, we analyze growth rates across approximately 4000 host populations of a perennial plant through time coupled with data on pathogen presence-absence. We find that infection decreases growth more in the isolated than well-connected host populations.

Strain Diversity and Spatial Distribution Are Linked to Epidemic Dynamics in Host Populations.

AbstractThe inherently variable nature of epidemics renders predictions of when and where infection is expected to occur challenging. Differences in pathogen strain composition, diversity, fitness, and spatial distribution are generally ignored in epidemiological modeling and are rarely studied in natural populations, yet they may be important drivers of epidemic trajectories. To examine how these factors are linked to epidemics in natural host populations, we collected epidemiological and genetic data from 15 populations of the powdery mildew fungus, , on in the Åland Islands, Finland.

Facilitative priority effects drive parasite assembly under coinfection.

Host individuals are often coinfected with diverse parasite assemblages, resulting in complex interactions among parasites within hosts. Within hosts, priority effects occur when the infection sequence alters the outcome of interactions among parasites. Yet, the role of host immunity in this process remains poorly understood.

The spread of a wild plant pathogen is driven by the road network.

Spatial analyses of pathogen occurrence in their natural surroundings entail unique opportunities for assessing in vivo drivers of disease epidemiology. Such studies are however confronted by the complexity of the landscape driving epidemic spread and disease persistence. Since relevant information on how the landscape influences epidemiological dynamics is rarely available, simple spatial models of spread are often used.

Variation and correlations between sexual, asexual and natural enemy resistance life-history traits in a natural plant pathogen population.

Background: Understanding the mechanisms by which diversity is maintained in pathogen populations is critical for epidemiological predictions. Life-history trade-offs have been proposed as a hypothesis for explaining long-term maintenance of variation in pathogen populations, yet the empirical evidence supporting trade-offs has remained mixed. This is in part due to the challenges of documenting successive pathogen life-history stages in many pathosystems.

Variable opportunities for outcrossing result in hotspots of novel genetic variation in a pathogen metapopulation.

Many pathogens possess the capacity for sex through outcrossing, despite being able to reproduce also asexually and/or via selfing. Given that sex is assumed to come at a cost, these mixed reproductive strategies typical of pathogens have remained puzzling. While the ecological and evolutionary benefits of outcrossing are theoretically well-supported, support for such benefits in pathogen populations are still scarce.

Antipsychotic medication and long-term mortality risk in patients with schizophrenia; a systematic review and meta-analysis.

Patients with schizophrenia have a higher mortality risk than patients suffering from any other psychiatric disorder. Previous research is inconclusive regarding the association of antipsychotic treatment with long-term mortality risk. To this aim, we systematically reviewed the literature and performed a meta-analysis on the relationship between long-term mortality and exposure to antipsychotic medication in patients with schizophrenia.

The impact of host metapopulation structure on the population genetics of colonizing bacteria.

Many key bacterial pathogens are frequently carried asymptomatically, and the emergence and spread of these opportunistic pathogens can be driven, or mitigated, via demographic changes within the host population. These inter-host transmission dynamics combine with basic evolutionary parameters such as rates of mutation and recombination, population size and selection, to shape the genetic diversity within bacterial populations. Whilst many studies have focused on how molecular processes underpin bacterial population structure, the impact of host migration and the connectivity of the local populations has received far less attention.

Climate induces seasonality in pneumococcal transmission.

Streptococcus pneumoniae is a significant human pathogen and a leading cause of infant mortality in developing countries. Considerable global variation in the pneumococcal carriage prevalence has been observed and the ecological factors contributing to it are not yet fully understood. We use data from a cohort of infants in Asia to study the effects of climatic conditions on both acquisition and clearance rates of the bacterium, finding significantly higher transmissibility during the cooler and drier months.

Two-phase importance sampling for inference about transmission trees.

There has been growing interest in the statistics community to develop methods for inferring transmission pathways of infectious pathogens from molecular sequence data. For many datasets, the computational challenge lies in the huge dimension of the missing data. Here, we introduce an importance sampling scheme in which the transmission trees and phylogenies of pathogens are both sampled from reasonable importance distributions, alleviating the inference.

Estimating the transmission dynamics of Streptococcus pneumoniae from strain prevalence data.

Streptococcus pneumoniae is a typical commensal bacterium causing severe diseases. Its prevalence is high among young children attending day care units, due to lower levels of acquired immunity and a high rate of infectious contacts between the attendees. Understanding the population dynamics of different strains of S.

Approximate Bayesian computation.

Approximate Bayesian computation (ABC) constitutes a class of computational methods rooted in Bayesian statistics. In all model-based statistical inference, the likelihood function is of central importance, since it expresses the probability of the observed data under a particular statistical model, and thus quantifies the support data lend to particular values of parameters and to choices among different models. For simple models, an analytical formula for the likelihood function can typically be derived.