Discovering the Phylodynamics of RNA Viruses

The advent of extremely high throughput DNA sequencing ensures that genomic data from microbial organisms can be acquired in unprecedented quantities and with remarkable rapidity. Although this genomic revolution will affect all microbes alike, our focus here is on RNA viruses, as the rapidity of their evolution, which is observable over the time scale of human observation, allows phylodynamic inferences to be made with great precision. In the foreseeable future it is likely that complete genome sequencing will become the standard method of viral characterization, providing the highest possible resolution for phylogenetic studies. The rapidity with which genome sequence data were generated from the ongoing epidemic of swine-origin H1N1 influenza A virus [1] is testament to the power of this technology

Comparing Models for Early Warning Systems of Neglected Tropical Diseases

Early Warning Systems (EWS) are management tools to predict the occurrence of epidemics. They are based on the dependence of a given infectious disease on environmental variables. Although several neglected tropical diseases are sensitive to the effect of climate, our ability to predict their dynamics has been barely studied. In this paper, we use several models to determine if the relationship between cases and climatic variability is robust—that is, not simply an artifact of model choice. We propose that EWS should be based on results from several models that are to be compared in terms of their ability to predict future number of cases. We use a specific metric for this comparison known as the predictive R2, which measures the accuracy of the predictions. For example, an R2 of 1 indicates perfect accuracy for predictions that perfectly match observed cases. For cutaneous leishmaniasis, R2 values range from 72% to77%, well above predictions using mean seasonal values (64%). We emphasize that predictability should be evaluated with observations that have not been used to fit the model. Finally, we argue that EWS should incorporate climatic variables that are known to have a consistent relationship with the number of observed cases

Spatial Pattern Switching Enables Cyclic Evolution in Spatial Epidemics

Infectious diseases often spread as spatial epidemic outbreak waves. A number of model studies have shown that such spatial pattern formation can have important consequences for the evolution of pathogens. Here, we show that such spatial patterns can cause cyclic evolutionary dynamics in selection for the length of the infectious period. The necessary reversal in the direction of selection is enabled by a qualitative change in the spatial pattern from epidemic waves to irregular local outbreaks. The spatial patterns are an emergent property of the epidemic system, and they are robust against changes in specific model assumptions. Our results indicate that emergent spatial patterns can act as a rich source for complexity in pathogen evolution

Landscape structure affects the prevalence and distribution of a tick-borne zoonotic pathogen

Background Landscape structure can affect pathogen prevalence and persistence with consequences for human and animal health. Few studies have examined how reservoir host species traits may interact with landscape structure to alter pathogen communities and dynamics. Using a landscape of islands and mainland sites we investigated how natural landscape fragmentation affects the prevalence and persistence of the zoonotic tick-borne pathogen complex Borrelia burgdorferi(sensu lato), which causes Lyme borreliosis. We hypothesized that the prevalence of B. burgdorferi (s.l.) would be lower on islands compared to the mainland and B. afzelii, a small mammal specialist genospecies, would be more affected by isolation than bird-associated B. garinii and B. valaisiana and the generalist B. burgdorferi (sensu stricto). Methods Questing (host-seeking) nymphal I. Ricinus ticks (n = 6567) were collected from 12 island and 6 mainland sites in 2011, 2013 and 2015 and tested for B. burgdorferi(s.l.). Deer abundance was estimated using dung transects. Results The prevalence of B. burgdorferi (s.l.) was significantly higher on the mainland (2.5%, 47/1891) compared to island sites (0.9%, 44/4673) (P < 0.01). While all four genospecies of B. burgdorferi (s.l.) were detected on the mainland, bird-associated species B. garinii and B. valaisiana and the generalist genospecies B. burgdorferi(s.s.) predominated on islands. Conclusion We found that landscape structure influenced the prevalence of a zoonotic pathogen, with a lower prevalence detected among island sites compared to the mainland. This was mainly due to the significantly lower prevalence of small mammal-associated B. afzelii. Deer abundance was not related to pathogen prevalence, suggesting that the structure and dynamics of the reservoir host community underpins the observed prevalence patterns, with the higher mobility of bird hosts compared to small mammal hosts leading to a relative predominance of the bird-associated genospecies B. garinii and generalist genospecies B. burgdorferi (s.s.) on islands. In contrast, the lower prevalence of B. afzelii on islands may be due to small mammal populations there exhibiting lower densities, less immigration and stronger population fluctuations. This study suggests that landscape fragmentation can influence the prevalence of a zoonotic pathogen, dependent on the biology of the reservoir host

Genetic Assignment Methods for Gaining Insight into the Management of Infectious Disease by Understanding Pathogen, Vector, and Host Movement

For many pathogens with environmental stages, or those carried by vectors or intermediate hosts, disease transmission is strongly influenced by pathogen, host, and vector movements across complex landscapes, and thus quantitative measures of movement rate and direction can reveal new opportunities for disease management and intervention. Genetic assignment methods are a set of powerful statistical approaches useful for establishing population membership of individuals. Recent theoretical improvements allow these techniques to be used to cost-effectively estimate the magnitude and direction of key movements in infectious disease systems, revealing important ecological and environmental features that facilitate or limit transmission. Here, we review the theory, statistical framework, and molecular markers that underlie assignment methods, and we critically examine recent applications of assignment tests in infectious disease epidemiology. Research directions that capitalize on use of the techniques are discussed, focusing on key parameters needing study for improved understanding of patterns of disease

Forcing Versus Feedback: Epidemic Malaria and Monsoon Rains in Northwest India

Malaria epidemics in regions with seasonal windows of transmission can vary greatly in size from year to year. A central question has been whether these interannual cycles are driven by climate, are instead generated by the intrinsic dynamics of the disease, or result from the resonance of these two mechanisms. This corresponds to the more general inverse problem of identifying the respective roles of external forcings vs. internal feedbacks from time series for nonlinear and noisy systems. We propose here a quantitative approach to formally compare rival hypotheses on climate vs. disease dynamics, or external forcings vs. internal feedbacks, that combines dynamical models with recently developed, computational inference methods. The interannual patterns of epidemic malaria are investigated here for desert regions of northwest India, with extensive epidemiological records for Plasmodium falciparum malaria for the past two decades. We formulate a dynamical model of malaria transmission that explicitly incorporates rainfall, and we rely on recent advances on parameter estimation for nonlinear and stochastic dynamical systems based on sequential Monte Carlo methods. Results show a significant effect of rainfall in the inter-annual variability of epidemic malaria that involves a threshold in the disease response. The model exhibits high prediction skill for yearly cases in the malaria transmission season following the monsoonal rains. Consideration of a more complex model with clinical immunity demonstrates the robustness of the findings and suggests a role of infected individuals that lack clinical symptoms as a reservoir for transmission. Our results indicate that the nonlinear dynamics of the disease itself play a role at the seasonal, but not the interannual, time scales. They illustrate the feasibility of forecasting malaria epidemics in desert and semi-arid regions of India based on climate variability. This approach should be applicable to malaria in other locations, to other infectious diseases, and to other nonlinear systems under forcing

Identifying the Age Cohort Responsible for Transmission in a Natural Outbreak of Bordetella bronchiseptica

Identifying the major routes of disease transmission and reservoirs of infection are needed to increase our understanding of disease dynamics and improve disease control. Despite this, transmission events are rarely observed directly. Here we had the unique opportunity to study natural transmission of Bordetella bronchiseptica – a directly transmitted respiratory pathogen with a wide mammalian host range, including sporadic infection of humans – within a commercial rabbitry to evaluate the relative effects of sex and age on the transmission dynamics therein. We did this by developing an a priori set of hypotheses outlining how natural B. bronchiseptica infections may be transmitted between rabbits. We discriminated between these hypotheses by using force-of-infection estimates coupled with random effects binomial regression analysis of B. bronchiseptica age-prevalence data from within our rabbit population. Force-of-infection analysis allowed us to quantify the apparent prevalence of B. bronchiseptica while correcting for age structure. To determine whether transmission is largely within social groups (in this case litter), or from an external group, we used random-effect binomial regression to evaluate the importance of social mixing in disease spread. Between these two approaches our results support young weanlings – as opposed to, for example, breeder or maternal cohorts – as the age cohort primarily responsible for B. bronchiseptica transmission. Thus age-prevalence data, which is relatively easy to gather in clinical or agricultural settings, can be used to evaluate contact patterns and infer the likely age-cohort responsible for transmission of directly transmitted infections. These insights shed light on the dynamics of disease spread and allow an assessment to be made of the best methods for effective long-term disease control

Within-Host Dynamics of the Hepatitis C Virus Quasispecies Population in HIV-1/HCV Coinfected Patients

HIV/HCV coinfected individuals under highly active antiretroviral therapy (HAART) represent an interesting model for the investigation of the role played by the immune system in driving the evolution of the HCV quasispecies. We prospectively studied the intra-host evolution of the HCV heterogeneity in 8 coinfected subjects, selected from a cohort of 32 patients initiating HAART: 5 immunological responders (group A) and 3 immunological non-responders (group B), and in two HCV singly infected controls not assuming drugs (group C). For all these subjects at least two serial samples obtained at the first observation (before HAART) and more than 1 year later, underwent clonal sequence analysis of partial E1/E2 sequences, encompassing the whole HVR1. Evolutionary rates, dated phylogenies and population dynamics were co-estimated by using a Bayesian Markov Chain Monte Carlo approach, and site specific selection pressures were estimated by maximum likelihood-based methods. The intra-host evolutionary rates of HCV quasispecies was 10 times higher in subjects treated with HAART than in controls without immunodeficiency (1.9 and 2.3×10−3 sub/site/month in group A and B and 0.29×10−3 sub/site/month in group C individuals). The within-host Bayesian Skyline plot analysis showed an exponential growth of the quasispecies populations in immunological responders, coinciding with a peak in CD4 cell counts. On the contrary, quasispecies population remained constant in group B and in group C controls. A significant positive selection pressure was detected in a half of the patients under HAART and in none of the group C controls. Several sites under significant positive selection were described, mainly included in the HVR1. Our data indicate that different forces, in addition to the selection pressure, drive an exceptionally fast evolution of HCV during HAART immune restoration. We hypothesize that an important role is played by the enlargement of the viral replicative space

Glacial Refugia in Pathogens: European Genetic Structure of Anther Smut Pathogens on Silene latifolia and Silene dioica

Climate warming is predicted to increase the frequency of invasions by pathogens and to cause the large-scale redistribution of native host species, with dramatic consequences on the health of domesticated and wild populations of plants and animals. The study of historic range shifts in response to climate change, such as during interglacial cycles, can help in the prediction of the routes and dynamics of infectious diseases during the impending ecosystem changes. Here we studied the population structure in Europe of two Microbotryum species causing anther smut disease on the plants Silene latifolia and Silene dioica. Clustering analyses revealed the existence of genetically distinct groups for the pathogen on S. latifolia, providing a clear-cut example of European phylogeography reflecting recolonization from southern refugia after glaciation. The pathogen genetic structure was congruent with the genetic structure of its host species S. latifolia, suggesting dependence of the migration pathway of the anther smut fungus on its host. The fungus, however, appeared to have persisted in more numerous and smaller refugia than its host and to have experienced fewer events of large-scale dispersal. The anther smut pathogen on S. dioica also showed a strong phylogeographic structure that might be related to more northern glacial refugia. Differences in host ecology probably played a role in these differences in the pathogen population structure. Very high selfing rates were inferred in both fungal species, explaining the low levels of admixture between the genetic clusters. The systems studied here indicate that migration patterns caused by climate change can be expected to include pathogen invasions that follow the redistribution of their host species at continental scales, but also that the recolonization by pathogens is not simply a mirror of their hosts, even for obligate biotrophs, and that the ecology of hosts and pathogen mating systems likely affects recolonization patterns