Transmission or within-host dynamics driving pulses of zoonotic viruses in reservoir-host populations

Progress in combatting zoonoses that emerge from wildlife is often constrained by limited knowledge of the biology of pathogens within reservoir hosts. We focus on the host–pathogen dynamics of four emerging viruses associated with bats: Hendra, Nipah, Ebola, and Marburg viruses. Spillover of bat infections to humans and domestic animals often coincides with pulses of viral excretion within bat populations, but the mechanisms driving such pulses are unclear. Three hypotheses dominate current research on these emerging bat infections. First, pulses of viral excretion could reflect seasonal epidemic cycles driven by natural variations in population densities and contact rates among hosts. If lifelong immunity follows recovery, viruses may disappear locally but persist globally through migration; in either case, new outbreaks occur once births replenish the susceptible pool. Second, epidemic cycles could be the result of waning immunity within bats, allowing local circulation of viruses through oscillating herd immunity. Third, pulses could be generated by episodic shedding from persistently infected bats through a combination of physiological and ecological factors. The three scenarios can yield similar patterns in epidemiological surveys, but strategies to predict or manage spillover risk resulting from each scenario will be different. We outline an agenda for research on viruses emerging from bats that would allow for differentiation among the scenarios and inform development of evidence-based interventions to limit threats to human and animal health. These concepts and methods are applicable to a wide range of pathogens that affect humans, domestic animals, and wildlife

Modelling the dynamics of an experimental host-pathogen microcosm within a hierarchical Bayesian framework.

The advantages of Bayesian statistical approaches, such as flexibility and the ability to acknowledge uncertainty in all parameters, have made them the prevailing method for analysing the spread of infectious diseases in human or animal populations. We introduce a Bayesian approach to experimental host-pathogen systems that shares these attractive features. Since uncertainty in all parameters is acknowledged, existing information can be accounted for through prior distributions, rather than through fixing some parameter values. The non-linear dynamics, multi-factorial design, multiple measurements of responses over time and sampling error that are typical features of experimental host-pathogen systems can also be naturally incorporated. We analyse the dynamics of the free-living protozoan Paramecium caudatum and its specialist bacterial parasite Holospora undulata. Our analysis provides strong evidence for a saturable infection function, and we were able to reproduce the two waves of infection apparent in the data by separating the initial inoculum from the parasites released after the first cycle of infection. In addition, the parameter estimates from the hierarchical model can be combined to infer variations in the parasite's basic reproductive ratio across experimental groups, enabling us to make predictions about the effect of resources and host genotype on the ability of the parasite to spread. Even though the high level of variability between replicates limited the resolution of the results, this Bayesian framework has strong potential to be used more widely in experimental ecology

Evolution and diversity of Rickettsia bacteria

Background: Rickettsia are intracellular symbionts of eukaryotes that are best known for infecting and causing serious diseases in humans and other mammals. All known vertebrate-associated Rickettsia are vectored by arthropods as part of their life-cycle, and many other Rickettsia are found exclusively in arthropods with no known secondary host. However, little is known about the biology of these latter strains. Here, we have identified 20 new strains of Rickettsia from arthropods, and constructed a multi-gene phylogeny of the entire genus which includes these new strains.Results: We show that Rickettsia are primarily arthropod-associated bacteria, and identify several novel groups within the genus. Rickettsia do not co-speciate with their hosts but host shifts most often occur between related arthropods. Rickettsia have evolved adaptations including transmission through vertebrates and killing males in some arthropod hosts. We uncovered one case of horizontal gene transfer among Rickettsia, where a strain is a chimera from two distantly related groups, but multi-gene analysis indicates that different parts of the genome tend to share the same phylogeny.Conclusion: Approximately 150 million years ago, Rickettsia split into two main clades, one of which primarily infects arthropods, and the other infects a diverse range of protists, other eukaryotes and arthropods. There was then a rapid radiation about 50 million years ago, which coincided with the evolution of life history adaptations in a few branches of the phylogeny. Even though Rickettsia are thought to be primarily transmitted vertically, host associations are short lived with frequent switching to new host lineages. Recombination throughout the genus is generally uncommon, although there is evidence of horizontal gene transfer. A better understanding of the evolution of Rickettsia will help in the future to elucidate the mechanisms of pathogenicity, transmission and virulence

Posterior mean deviance of the six models considered.

<p>(*) We were not able to fit the one-wave, <i>N</i>-saturating model.</p

Predicted dynamics of the force of infection for the two-wave, -saturating model.

<p>These were obtained for every population using the posterior median parameter values. Top row: low food, bottom row: high food. Panels from left to right: genotypes K4, K6, K8 and K9. Magenta: first wave (based on ), cyan: second wave (based on ).</p

Experimental data.

<p>(a) Time series of the number of paramecia in each of the 12 populations of each clone, classified by inoculum and food level treatments; note the logarithmic scale. (b) Time series of the mean proportions of paramecia in each of the three stages of infection (green: S, amber: C, brown: I) across all populations.</p

Symbols and summaries of prior information for variables used in this study.

<p>LF: low food, HF: high food. See Text S1 in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0069775#pone.0069775.s001" target="_blank">File S1</a> for an explanation of how prior distributions were obtained.</p

Compartmental models.

<p>(a) without distinction between the inoculum and newly-produced parasites, (b) with distinction.</p

Persistence of infection for the two-wave, -saturating model.

<p>(a) Posterior medians and 95%-credible intervals of for every experimental population; red: high food, blue: low food. (b) Equilibrium values of , and (obtained by running numerical simulations of the model for 5000 days) across a range of values of the virulence , shown here for genotype K8 in high food, replicate A. The vertical dashed line shows the position of the predicted threshold, , corresponding to .</p

Posterior 95%-credible intervals for clone K8, replicate A, in high food.

<p>The dots show experimental data and the lines show the predicted dynamics for the two-wave, -saturating model. In the central panel on the right-hand side, the red line shows the predicted dynamics of and the blue line the predicted dynamics of .</p