A simple explanation for the low impact of border control as a countermeasure to the spread of an infectious disease

A simple model for the effect of border control or travel restrictions is proposed. It can be used to predict the corresponding results in quite complex disease spread models and has the advantage of providing easy qualitative understanding of the effects of this kind of intervention.     

Markov chain approach to analyze the dynamics of pathogen fecal shedding--example of Listeria monocytogenes shedding in a herd of dairy cattle

Fecal shedding is an important mechanism of spreading of a number of human and animal pathogens. Understanding of the dynamics of pathogen fecal shedding is critical to be able to control or prevent the spread of diseases caused by these pathogens. The objective of this study was to develop a model for analysis of the dynamics of pathogen fecal shedding. Fecal shedding of Listeria monocytogenes in dairy cattle was used as a model system. A Markov chain model (MCM) with two states, shedding and non-shedding, has been developed for overall L. monocytogenes fecal shedding (all L. monocytogenes subtypes) and fecal shedding of three L. monocytogenes subtypes (ribotypes 1058A, 1039E and 1042B) using data from one study farm. The matrices of conditional probabilities of transition between shedding and non-shedding states for different sets of covariates have been estimated by application of logistic regression. The covariate-specific matrices of conditional probabilities, describing the presence of different risk factors, were used to estimate (i) the stationary prevalence of dairy cows that shed any L. monocytogenes subtype or ribotypes 1058A, 1039E, and 1042B, (ii) the duration of overall and subtype specific fecal shedding, and (iii) the duration of periods without shedding. A non-homogeneous MCM was constructed to study how the prevalence of fecal shedders changes over time. The model was validated with data from the study farm and published literature. The results of our modeling work indicated that (i) the prevalence of L. monocytogenes fecal shedders varies over time and can be higher than 90%, (ii) L. monocytogenes subtypes exhibit different dynamics of fecal shedding, (iii) the dynamics of L. monocytogenes fecal shedding are highly associated with contamination of silage (fermented feed) and cows' exposure to stress, and (iv) the developed approach can be readily used to study the dynamics of fecal shedding in other pathogen-host-environment systems.     

Host mating system and the spread of a disease-resistant allele in a population

The model presented here modifies a susceptible-infected (SI) host–pathogen model to determine the influence of mating system on the outcome of a host–pathogen interaction. Both deterministic and stochastic (individual-based) versions of the model were used. This model considers the potential consequences of varying mating systems on the rate of spread of both the pathogen and resistance alleles within the population. We assumed that a single allele for disease resistance was sufficient to confer complete resistance in an individual, and that both homozygote and heterozygote resistant individuals had the same mean birth and death rates. When disease invaded a population with only an initial small fraction of resistant genes, inbreeding (selfing) tended to increase the probability that the disease would soon be eliminated from a small population rather than become endemic, while outcrossing greatly increased the probability that the population would become extinct due to the disease.     

Multivariate Markov process models for the transmission of methicillin-resistant Staphylococcus aureus in a hospital ward

Summary .   Methicillin-resistant Staphylococcus Aureus (MRSA) is a pathogen that continues to be of major concern in hospitals. We develop models and computational schemes based on observed weekly incidence data to estimate MRSA transmission parameters. We extend the deterministic model of McBryde, Pettitt, and McElwain (2007, Journal of Theoretical Biology 245, 470–481) involving an underlying population of MRSA colonized patients and health-care workers that describes, among other processes, transmission between uncolonized patients and colonized health-care workers and vice versa. We develop new bivariate and trivariate Markov models to include incidence so that estimated transmission rates can be based directly on new colonizations rather than indirectly on prevalence. Imperfect sensitivity of pathogen detection is modeled using a hidden Markov process. The advantages of our approach include (i) a discrete valued assumption for the number of colonized health-care workers, (ii) two transmission parameters can be incorporated into the likelihood, (iii) the likelihood depends on the number of new cases to improve precision of inference, (iv) individual patient records are not required, and (v) the possibility of imperfect detection of colonization is incorporated. We compare our approach with that used by McBryde et al. (2007) based on an approximation that eliminates the health-care workers from the model, uses Markov chain Monte Carlo and individual patient data. We apply these models to MRSA colonization data collected in a small intensive care unit at the Princess Alexandra Hospital, Brisbane, Australia.     

Integrating species distribution models and interacting particle systems to predict the spread of an invasive alien plant

Aim We demonstrate how to integrate two widely used tools for modelling the spread of invasive plants, and compare the performance of the combined model with that of its individual components using the recent range dynamics of the invasive annual weed Ambrosia artemisiifolia L. Location Austria. Methods Species distribution models, which deliver habitat‐based information on potential distributions, and interacting particle systems, which simulate spatio‐temporal range dynamics as dependent on neighbourhood configurations, were combined into a common framework. We then used the combined model to simulate the invasion of A. artemisiifolia in Austria between 1990 and 2005. For comparison, simulations were also performed with models that accounted only for habitat suitability or neighbourhood configurations. The fit of the three models to the data was assessed by likelihood ratio tests, and simulated invasion patterns were evaluated against observed ones in terms of predictive discrimination ability (area under the receiver operating characteristic curve, AUC) and spatial autocorrelation (Moran’s I). Results The combined model fitted the data significantly better than the single‐component alternatives. Simulations relying solely on parameterized spread kernels performed worst in terms of both AUC and spatial pattern formation. Simulations based only on habitat information correctly predicted infestation of susceptible areas but reproduced the autocorrelated patterns of A. artemisiifolia expansion less adequately than did the integrated model. Main conclusions Our integrated modelling approach offers a flexible tool for forecasts of spatio‐temporal invasion patterns from landscape to regional scales. As a further advantage, scenarios of environmental change can be incorporated consistently by appropriately updating habitat suitability layers. Given the susceptibility of many alien plants, including A. artemisiifolia, to both land use and climate changes, taking such scenarios into account will increasingly become relevant for the design of proactive management strategies.     

On the reproductive value and the spectrum of a population projection matrix with implications for dynamic population models

The eigenvalues of a population projection matrix-except for the Lotka coefficient-are uniquely determined by the reproductive values and the survival. This relation (proposed earlier, but not really well known in western literature) follows from another useful relation between fertility, reproductive values, survival, and Lotka’s coefficient. These results are applied to provide demographic interpretations to the intrinsically dynamic and metastable population models by Schoen and co-workers.     

Stochastic models for the spread of HIV in a mobile heterosexual population

An important factor in the dynamic transmission of HIV is the mobility of the population. We formulate various stochastic models for the spread of HIV in a heterosexual mobile population, under the assumptions of constant and varying population sizes. We also derive deterministic and diffusion analogues for these models, using a convenient rescaling technique, and analyze their stability conditions and equilibrium behavior. We illustrate the dynamic behavior of the models and their approximations via a range of numerical experiments.     

Improving plant functional groups for dynamic models of biodiversity: at the crossroads between functional and community ecology

The pace of on‐going climate change calls for reliable plant biodiversity scenarios. Traditional dynamic vegetation models use plant functional types that are summarized to such an extent that they become meaningless for biodiversity scenarios. Hybrid dynamic vegetation models of intermediate complexity (hybrid‐DVMs) have recently been developed to address this issue. These models, at the crossroads between phenomenological and process‐based models, are able to involve an intermediate number of well‐chosen plant functional groups (PFGs). The challenge is to build meaningful PFGs that are representative of plant biodiversity, and consistent with the parameters and processes of hybrid‐DVMs. Here, we propose and test a framework based on few selected traits to define a limited number of PFGs, which are both representative of the diversity (functional and taxonomic) of the flora in the Ecrins National Park, and adapted to hybrid‐DVMs. This new classification scheme, together with recent advances in vegetation modeling, constitutes a step forward for mechanistic biodiversity modeling.     

Exploration of the transition state for tertiary structure formation between an RNA helix and a large structured RNA

Docking of the P1 duplex into the pre-folded core of the Tetrahymena group I ribozyme exemplifies the formation of tertiary interactions in the context of a complex, structured RNA. We have applied Phi-analysis to P1 docking, which compares the effects of modifications on the rate constant for docking (k(dock)) with the effects on the docking equilibrium (K(dock)). To accomplish this we used a single molecule fluorescence resonance energy transfer assay that allows direct determination of the rate constants for formation of thermodynamically favorable, as well as unfavorable, states. Modification of the eight groups of the P1 duplex that make tertiary interactions with the core and changes in solution conditions decrease K(dock) up to 500-fold, whereas k(dock) changes by 相似文献   

Population genetic structure of a common host predicts the spread of white‐nose syndrome,an emerging infectious disease in bats

Landscape complexity influences patterns of animal dispersal, which in turn may affect both gene flow and the spread of pathogens. White‐nose syndrome (WNS) is an introduced fungal disease that has spread rapidly throughout eastern North America, causing massive mortality in bat populations. We tested for a relationship between the population genetic structure of the most common host, the little brown myotis (Myotis lucifugus), and the geographic spread of WNS to date by evaluating logistic regression models of WNS risk among hibernating colonies in eastern North America. We hypothesized that risk of WNS to susceptible host colonies should increase with both geographic proximity and genetic similarity, reflecting historical connectivity, to infected colonies. Consistent with this hypothesis, inclusion of genetic distance between infected and susceptible colonies significantly improved models of disease spread, capturing heterogeneity in the spatial expansion of WNS despite low levels of genetic differentiation among eastern populations. Expanding our genetic analysis to the continental range of little brown myotis reveals strongly contrasting patterns of population structure between eastern and western North America. Genetic structure increases markedly moving westward into the northern Great Plains, beyond the current distribution of WNS. In western North America, genetic differentiation of geographically proximate populations often exceeds levels observed across the entire eastern region, suggesting infrequent and/or locally restricted dispersal, and thus relatively limited opportunities for pathogen introduction in western North America. Taken together, our analyses suggest a possibly slower future rate of spread of the WNS pathogen, at least as mediated by little brown myotis.     

A framework for estimating the fixation time of an advantageous allele in stepping-stone models

Determining how population subdivision increases the fixation time of an advantageous allele is an important problem in evolutionary genetics as this influences many processes. Here, I lay out a framework for calculating the fixation time of a positively selected allele in a subdivided population, as a function of the number of demes present, the migration rate between them and the manner in which they are connected. Using this framework, it becomes clear that a beneficial allele's fixation time is significantly reduced through migration continuously introducing copies of the allele into a newly colonized subpopulation, increasing its frequency within these demes. The effect that migration has on allele frequency needs to be explicitly taken into account to produce a realistic estimate of fixation time. This behaviour is most prominent when demes are arranged on a two-dimensional torus, in comparison with populations where demes are arranged in a circle. This is because each subpopulation is connected to several neighbours over a torus, so that there are multiple paths that an allele can take in order to fix. As a consequence, some demes experience a greater influx and efflux of migrants than others. Analytical results are found to be very accurate when compared to stochastic simulations, and are generally robust if there are a large number of demes, or if the allele is weakly selected for.     

Point pattern analysis as a tool for assessing disease spread and population features in remaining sanctuaries of the critically endangered bivalve Pinna nobilis

An emergent disease has relegated populations of the Mediterranean pen shell, Pinna nobilis L. critically endangered to sanctuaries featuring salinities outside the 36.5 to 39 range. Point pattern analysis was used in three areas of the Alfacs Bay (Ebro Delta) still hosting pen shells to assess the possible undergoing of disease spread by comparing the spatial distribution of live individuals vs. empty shells across spatial scales. We also evaluated the importance of other ecological aspects of conservation relevance such as the size distribution of individuals, and the possible association to seagrass habitats. The population assessment showed no recent mortality and a clear dominance of large adults among empty shells (97.3%) pointing to no disease spread during the study period. At the low spatial scale Nearest Neighbor (NN) analyses evidenced significant clustering (NN Ratios of 0.4–0.8), but in one of the zones NN distances were closer in empty shells than in live individuals, suggesting a former localized outbreak. At the larger spatial scale, MDSCA confirmed clustering patterns up to distances of 115 to 190 m, with higher aggregation of empty shells at the same study zone. The bay also featured low juvenile availability (3.2%), which risks the continuity of the population. No evidence for habitat or conspecific selection could be observed from abundance patterns and variation in NN across study regions. Our research provides a tool for assessing population condition in paralic environments, where salinity conditions tend to slow down disease spread, thus allowing a time gap for undertaking conservation decisions.