Identifying potential significant factors impacting zero-inflated proportion data.

Classical supervised methods like linear regression and decision trees are not completely adapted for identifying impacting factors on a response variable corresponding to zero-inflated proportion data (ZIPD) that are dependent, continuous and bounded. In this article we propose a within-block permutation-based methodology to identify factors (discrete or continuous) that are significantly correlated with ZIPD, we propose a performance indicator quantifying the percentage of correlation explained by the subset of significant factors, and we show how to predict the ranks of the response variables conditionally on the observation of these factors. The methodology is illustrated on simulated data and on two real data sets dealing with epidemiology. In the first data set, ZIPD correspond to probabilities of transmission of Influenza between horses. In the second data set, ZIPD correspond to probabilities that geographic entities (eg, states and countries) have the same COVID-19 mortality dynamics.

Forecasting Pathogen Dynamics with Bayesian Model-Averaging: Application to Xylella fastidiosa.

Forecasting invasive-pathogen dynamics is paramount to anticipate eradication and containment strategies. Such predictions can be obtained using a model grounded on partial differential equations (PDE; often exploited to model invasions) and fitted to surveillance data. This framework allows the construction of phenomenological but concise models relying on mechanistic hypotheses and real observations. However, it may lead to models with overly rigid behavior and possible data-model mismatches. Hence, to avoid drawing a forecast grounded on a single PDE-based model that would be prone to errors, we propose to apply Bayesian model averaging (BMA), which allows us to account for both parameter and model uncertainties. Thus, we propose a set of different competing PDE-based models for representing the pathogen dynamics, we use an adaptive multiple importance sampling algorithm (AMIS) to estimate parameters of each competing model from surveillance data in a mechanistic-statistical framework, we evaluate the posterior probabilities of models by comparing different approaches proposed in the literature, and we apply BMA to draw posterior distributions of parameters and a posterior forecast of the pathogen dynamics. This approach is applied to predict the extent of Xylella fastidiosa in South Corsica, France, a phytopathogenic bacterium detected in situ in Europe less than 10 years ago (Italy 2013, France 2015). Separating data into training and validation sets, we show that the BMA forecast outperforms competing forecast approaches.

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, Podosphaera plantaginis, on Plantago lanceolata in the Åland Islands, Finland. In each population, we tracked spatiotemporal disease progression throughout one epidemic season and coupled our survey of infection with intensive field sampling of the pathogen. We found that strain composition varied greatly among populations in the landscape. Within populations, strain composition was driven by the sequence of strain activity: early-active strains reached higher abundances, leading to consistent strain compositions over time. Co-occurring strains also varied in their contribution to the growth of the local epidemic, and these fitness inequalities were linked to epidemic dynamics: a higher proportion of hosts became infected in populations containing strains that were more similar in fitness. Epidemic trajectories in the populations were also linked to strain diversity and spatial dynamics: higher infection rates occurred in populations containing higher strain diversity, while spatially clustered epidemics experienced lower infection rates. Together, our results suggest that spatial and/or temporal variation in the strain composition, diversity, and fitness of pathogen populations are important factors generating variation in epidemiological trajectories among infected host populations.

Correction: Estimation of the dispersal distances of an aphid-borne virus in a patchy landscape.

[This corrects the article DOI: 10.1371/journal.pcbi.1006085.].

When the average hides the risk of Bt-corn pollen on non-target Lepidoptera: Application to Aglais io in Catalonia.

Field cultivation of Genetically Modified (GM) Bt-plants has a potential environmental risk toward non-target Lepidoptera (NTLs) larvae through the consumption of Bt-maize pollen. The Bt-maize Cry protein targeting Lepidoptera species detrimental to the crop is also expressed in pollen which is dispersed by wind and can thus reach habitats of NTLs. To better assess the current ecological risk of Bt-maize at landscape scales, we developed a spatially-explicit exposure-hazard model considering (i) the dynamics of pollen dispersal obtained by convolving GM plants emission with a dispersal kernel and (ii) a toxicokinetic-toxicodynamic (TKTD) model accounting for the impact of toxin ingestion on individual lethal effects. We simulated the model using real landscape observations in Catalonia (Spain): GM-maize locations, flowering dates, rainfall time series and larvae emergence date of the European peacock butterfly Aglais io. While in average, the additional mortality appears to be negligible, we show significant additional mortality at sub-population level, with for instance a mortality higher than 40% within the 10m for the 10% most Bt-sensitive individuals. Also, using Pareto optimality we capture the best trade-off between isolation distance and additional mortality: up to 50 m are required to significantly buffer Bt-pollen impact on NTLs survival at the individual level. Our study clears up the narrow line between diverging conclusions: those claiming no risk by only looking at the average regional effect of Bt on NTLs survival and those pointing out a significant threaten when considering the variability of individuals mortality.

Estimation of the dispersal distances of an aphid-borne virus in a patchy landscape.

Characterising the spatio-temporal dynamics of pathogens in natura is key to ensuring their efficient prevention and control. However, it is notoriously difficult to estimate dispersal parameters at scales that are relevant to real epidemics. Epidemiological surveys can provide informative data, but parameter estimation can be hampered when the timing of the epidemiological events is uncertain, and in the presence of interactions between disease spread, surveillance, and control. Further complications arise from imperfect detection of disease and from the huge number of data on individual hosts arising from landscape-level surveys. Here, we present a Bayesian framework that overcomes these barriers by integrating over associated uncertainties in a model explicitly combining the processes of disease dispersal, surveillance and control. Using a novel computationally efficient approach to account for patch geometry, we demonstrate that disease dispersal distances can be estimated accurately in a patchy (i.e. fragmented) landscape when disease control is ongoing. Applying this model to data for an aphid-borne virus (Plum pox virus) surveyed for 15 years in 605 orchards, we obtain the first estimate of the distribution of flight distances of infectious aphids at the landscape scale. About 50% of aphid flights terminate beyond 90 m, which implies that most infectious aphids leaving a tree land outside the bounds of a 1-ha orchard. Moreover, long-distance flights are not rare-10% of flights exceed 1 km. By their impact on our quantitative understanding of winged aphid dispersal, these results can inform the design of management strategies for plant viruses, which are mainly aphid-borne.

Identifying Lookouts for Epidemio-Surveillance: Application to the Emergence of Xylella fastidiosa in France.

Recent detections of Xylella fastidiosa in Corsica Island, France, has raised concerns on its possible spread to mainland France and the rest of the Mediterranean Basin. Early detection of infected plants is paramount to prevent the spread of the bacteria, but little is known about this pathosystem in European environments, hence standard surveillance strategies may be ineffective. We present a new methodological approach for the design of risk-based surveillance strategies, adapted to the emerging risk caused by X. fastidiosa. Our proposal is based on a combination of machine learning techniques and network analysis that aims at understanding the main abiotic drivers of the infection, produce risk maps and identify lookouts for the design of future surveillance plans. The identified drivers coincide with known results in laboratory studies about the correlation between environmental variables, such as water stress and temperature, and the presence of the bacterium in plants. Furthermore, the produced risk maps overlap nicely with detected foci of infection, while they also highlight other susceptible regions where X. fastidiosa has not been found yet. We conclude the paper presenting a list of recommended regions for a risk-based surveillance campaign based on the predicted spread and probability of early detection of the disease.

Analyzing the Influence of Landscape Aggregation on Disease Spread to Improve Management Strategies.

Epidemiological models are increasingly used to predict epidemics and improve management strategies. However, they rarely consider landscape characteristics although such characteristics can influence the epidemic dynamics and, thus, the effectiveness of disease management strategies. Here, we present a generic in silico approach which assesses the influence of landscape aggregation on the costs associated with an epidemic and on improved management strategies. We apply this approach to sharka, one of the most damaging diseases of Prunus trees, for which a management strategy is already applied in France. Epidemic simulations were carried out with a spatiotemporal stochastic model under various management strategies in landscapes differing in patch aggregation. Using sensitivity analyses, we highlight the impact of management parameters on the economic output of the model. We also show that the sensitivity analysis can be exploited to identify several strategies that are, according to the model, more profitable than the current French strategy. Some of these strategies are specific to a given aggregation level, which shows that management strategies should generally be tailored to each specific landscape. However, we also identified a strategy that is efficient for all levels of landscape aggregation. This one-size-fits-all strategy has important practical implications because of its simple applicability at a large scale.

Improving Management Strategies of Plant Diseases Using Sequential Sensitivity Analyses.

Improvement of management strategies of epidemics is often hampered by constraints on experiments at large spatiotemporal scales. A promising approach consists of modeling the biological epidemic process and human interventions, which both impact disease spread. However, few methods enable the simultaneous optimization of the numerous parameters of sophisticated control strategies. To do so, we propose a heuristic approach (i.e., a practical improvement method approximating an optimal solution) based on sequential sensitivity analyses. In addition, we use an economic improvement criterion based on the net present value, accounting for both the cost of the different control measures and the benefit generated by disease suppression. This work is motivated by sharka (caused by Plum pox virus), a vector-borne disease of prunus trees (especially apricot, peach, and plum), the management of which in orchards is mainly based on surveillance and tree removal. We identified the key parameters of a spatiotemporal model simulating sharka spread and control and approximated optimal values for these parameters. The results indicate that the current French management of sharka efficiently controls the disease, but it can be economically improved using alternative strategies that are identified and discussed. The general approach should help policy makers to design sustainable and cost-effective strategies for disease management.

Dating and localizing an invasion from post-introduction data and a coupled reaction-diffusion-absorption model.

Invasion of new territories by alien organisms is of primary concern for environmental and health agencies and has been a core topic in mathematical modeling, in particular in the intents of reconstructing the past dynamics of the alien organisms and predicting their future spatial extents. Partial differential equations offer a rich and flexible modeling framework that has been applied to a large number of invasions. In this article, we are specifically interested in dating and localizing the introduction that led to an invasion using mathematical modeling, post-introduction data and an adequate statistical inference procedure. We adopt a mechanistic-statistical approach grounded on a coupled reaction-diffusion-absorption model representing the dynamics of an organism in an heterogeneous domain with respect to growth. Initial conditions (including the date and site of the introduction) and model parameters related to diffusion, reproduction and mortality are jointly estimated in the Bayesian framework by using an adaptive importance sampling algorithm. This framework is applied to the invasion of Xylella fastidiosa, a phytopathogenic bacterium detected in South Corsica in 2015, France.

A Spatio-Temporal Exposure-Hazard Model for Assessing Biological Risk and Impact.

We developed a simulation model for quantifying the spatio-temporal distribution of contaminants (e.g., xenobiotics) and assessing the risk of exposed populations at the landscape level. The model is a spatio-temporal exposure-hazard model based on (i) tools of stochastic geometry (marked polygon and point processes) for structuring the landscape and describing the exposed individuals, (ii) a dispersal kernel describing the dissemination of contaminants from polygon sources, and (iii) an (eco)toxicological equation describing the toxicokinetics and dynamics of contaminants in affected individuals. The model was implemented in the briskaR package (biological risk assessment with R) of the R software. This article presents the model background, the use of the package in an illustrative example, namely, the effect of genetically modified maize pollen on nontarget Lepidoptera, and typical comparisons of landscape configurations that can be carried out with our model (different configurations lead to different mortality rates in the treated example). In real case studies, parameters and parametric functions encountered in the model will have to be precisely specified to obtain realistic measures of risk and impact and accurate comparisons of landscape configurations. Our modeling framework could be applied to study other risks related to agriculture, for instance, pathogen spread in crops or livestock, and could be adapted to cope with other hazards such as toxic emissions from industrial areas having health effects on surrounding populations. Moreover, the R package has the potential to help risk managers in running quantitative risk assessments and testing management strategies.

Inferring pathogen dynamics from temporal count data: the emergence of Xylella fastidiosa in France is probably not recent.

Unravelling the ecological structure of emerging plant pathogens persisting in multi-host systems is challenging. In such systems, observations are often heterogeneous with respect to time, space and host species, and may lead to biases of perception. The biased perception of pathogen ecology may be exacerbated by hidden fractions of the whole host population, which may act as infection reservoirs. We designed a mechanistic-statistical approach to help understand the ecology of emerging pathogens by filtering out some biases of perception. This approach, based on SIR (Susceptible-Infected-Removed) models and a Bayesian framework, disentangles epidemiological and observational processes underlying temporal counting data. We applied our approach to French surveillance data on Xylella fastidiosa, a multi-host pathogenic bacterium recently discovered in Corsica, France. A model selection led to two diverging scenarios: one scenario without a hidden compartment and an introduction around 2001, and the other with a hidden compartment and an introduction around 1985. Thus, Xylella fastidiosa was probably introduced into Corsica much earlier than its discovery, and its control could be arduous under the hidden compartment scenario. From a methodological perspective, our approach provides insights into the dynamics of emerging plant pathogens and, in particular, the potential existence of infection reservoirs.

Testing Differences Between Pathogen Compositions with Small Samples and Sparse Data.

The structure of pathogen populations is an important driver of epidemics affecting crops and natural plant communities. Comparing the composition of two pathogen populations consisting of assemblages of genotypes or phenotypes is a crucial, recurrent question encountered in many studies in plant disease epidemiology. Determining whether there is a significant difference between two sets of proportions is also a generic question for numerous biological fields. When samples are small and data are sparse, it is not straightforward to provide an accurate answer to this simple question because routine statistical tests may not be exactly calibrated. To tackle this issue, we built a computationally intensive testing procedure, the generalized Monte Carlo plug-in test with calibration test, which is implemented in an R package available at https://doi.org/10.5281/zenodo.635791 . A simulation study was carried out to assess the performance of the proposed methodology and to make a comparison with standard statistical tests. This study allows us to give advice on how to apply the proposed method, depending on the sample sizes. The proposed methodology was then applied to real datasets and the results of the analyses were discussed from an epidemiological perspective. The applications to real data sets deal with three topics in plant pathology: the reproduction of Magnaporthe oryzae, the spatial structure of Pseudomonas syringae, and the temporal recurrence of Puccinia triticina.

Linking winter conditions to regional disease dynamics in a wild plant-pathogen metapopulation.

Pathogens are considered to drive ecological and evolutionary dynamics of plant populations, but we lack data measuring the population-level consequences of infection in wild plant-pathogen interactions. Moreover, while it is often assumed that offseason environmental conditions drive seasonal declines in pathogen population size, little is known about how offseason environmental conditions impact the survival of pathogen resting stages, and how critical the offseason is for the next season's epidemic. The fungal pathogen Podosphaera plantaginis persists as a dynamic metapopulation in the large network of Plantago lanceolata host populations. Here, we analyze long-term data to measure the spatial synchrony of epidemics and consequences of infection for over 4000 host populations. Using a theoretical model, we study whether large-scale environmental change could synchronize disease occurrence across the metapopulation. During 2001-2013 exposure to freezing decreased, while pathogen extinction-colonization-persistence rates became more synchronized. Simulations of a theoretical model suggest that increasingly favorable winter conditions for pathogen survival could drive such synchronization. Our data also show that infection decreases host population growth. These results confirm that mild winter conditions increase pathogen overwintering success and thus increase disease prevalence across the metapopulation. Further, we conclude that the pathogen can drive host population growth in the Plantago-Podosphaera system.

Assessing the Mismatch Between Incubation and Latent Periods for Vector-Borne Diseases: The Case of Sharka.

The relative durations of the incubation period (the time between inoculation and symptom expression) and of the latent period (the time between inoculation and infectiousness of the host) are poorly documented for plant diseases. However, the extent of asynchrony between the ends of these two periods (i.e., their mismatch) can be a key determinant of the epidemic dynamics for many diseases and consequently it is of primary interest in the design of disease management strategies. In order to assess this mismatch, an experimental approach was developed and applied using sharka, a severe disease caused by Plum pox virus (PPV, genus Potyvirus, family Potyviridae) affecting trees belonging to the genus Prunus. Leaves of infected young peach trees were used individually as viral sources in aphid-mediated transmission tests carried out at different time points postinoculation in order to bracket symptom onset. By fitting a nonlinear logistic model to the obtained transmission rates, we demonstrated that the first symptoms appear on leaves 1 day before they rapidly become infectious. In addition, among symptomatic leaves, symptom intensity and transmission rate are positively correlated. These results strengthen the conclusion that, under our experimental conditions, incubation and latent periods of PPV infection are almost synchronous.

A Bayesian approach for inferring the dynamics of partially observed endemic infectious diseases from space-time-genetic data.

We describe a statistical framework for reconstructing the sequence of transmission events between observed cases of an endemic infectious disease using genetic, temporal and spatial information. Previous approaches to reconstructing transmission trees have assumed all infections in the study area originated from a single introduction and that a large fraction of cases were observed. There are as yet no approaches appropriate for endemic situations in which a disease is already well established in a host population and in which there may be multiple origins of infection, or that can enumerate unobserved infections missing from the sample. Our proposed framework addresses these shortcomings, enabling reconstruction of partially observed transmission trees and estimating the number of cases missing from the sample. Analyses of simulated datasets show the method to be accurate in identifying direct transmissions, while introductions and transmissions via one or more unsampled intermediate cases could be identified at high to moderate levels of case detection. When applied to partial genome sequences of rabies virus sampled from an endemic region of South Africa, our method reveals several distinct transmission cycles with little contact between them, and direct transmission over long distances suggesting significant anthropogenic influence in the movement of infected dogs.

Approximate Bayesian computation with functional statistics.

Functional statistics are commonly used to characterize spatial patterns in general and spatial genetic structures in population genetics in particular. Such functional statistics also enable the estimation of parameters of spatially explicit (and genetic) models. Recently, Approximate Bayesian Computation (ABC) has been proposed to estimate model parameters from functional statistics. However, applying ABC with functional statistics may be cumbersome because of the high dimension of the set of statistics and the dependences among them. To tackle this difficulty, we propose an ABC procedure which relies on an optimized weighted distance between observed and simulated functional statistics. We applied this procedure to a simple step model, a spatial point process characterized by its pair correlation function and a pollen dispersal model characterized by genetic differentiation as a function of distance. These applications showed how the optimized weighted distance improved estimation accuracy. In the discussion, we consider the application of the proposed ABC procedure to functional statistics characterizing non-spatial processes.

A Bayesian inference framework to reconstruct transmission trees using epidemiological and genetic data.

The accurate identification of the route of transmission taken by an infectious agent through a host population is critical to understanding its epidemiology and informing measures for its control. However, reconstruction of transmission routes during an epidemic is often an underdetermined problem: data about the location and timings of infections can be incomplete, inaccurate, and compatible with a large number of different transmission scenarios. For fast-evolving pathogens like RNA viruses, inference can be strengthened by using genetic data, nowadays easily and affordably generated. However, significant statistical challenges remain to be overcome in the full integration of these different data types if transmission trees are to be reliably estimated. We present here a framework leading to a bayesian inference scheme that combines genetic and epidemiological data, able to reconstruct most likely transmission patterns and infection dates. After testing our approach with simulated data, we apply the method to two UK epidemics of Foot-and-Mouth Disease Virus (FMDV): the 2007 outbreak, and a subset of the large 2001 epidemic. In the first case, we are able to confirm the role of a specific premise as the link between the two phases of the epidemics, while transmissions more densely clustered in space and time remain harder to resolve. When we consider data collected from the 2001 epidemic during a time of national emergency, our inference scheme robustly infers transmission chains, and uncovers the presence of undetected premises, thus providing a useful tool for epidemiological studies in real time. The generation of genetic data is becoming routine in epidemiological investigations, but the development of analytical tools maximizing the value of these data remains a priority. Our method, while applied here in the context of FMDV, is general and with slight modification can be used in any situation where both spatiotemporal and genetic data are available.

Flight behavior and pheromone changes associated to Nosema ceranae infection of honey bee workers (Apis mellifera) in field conditions.

Parasites are known to cause the loss of individuals in social insects. In honey bee colonies the disappearance of foragers is a common factor of the wide extended colony losses. The emergent parasite of the European honey bee Nosema ceranae has been found to reduce homing and orientation skills and alter metabolism of forager bees. N. ceranae-infected bees also show changes in Ethyl Oleate (EO) levels, which is so far the only primer pheromone identified in workers that is involved in foraging behavior. Thus, we hypothesized that N. ceranae (i) modifies flight activity of honey bees and (ii) induces EO changes that can alter foraging behavior of nestmates. We compared flight activity of infected bees and non-infected bees in small colonies using an electronic optic bee counter during 28 days. We measured EO levels by gas chromatography-mass spectrometry and spore-counts. Bee mortality was estimated at the end of the experiment. Infected bees showed precocious and a higher flight activity than healthy bees, which agreed with the more elevated EO titers of infected bees and reduced lifespan. Our results suggest that the higher EO levels of infected bees might delay the behavioral maturation of same age healthy bees, which might explain their lower level of activity. We propose that delayed behavioral maturation of healthy bees might be a protective response to infection, as healthy bees would be performing less risky tasks inside the hive, thus extending their lifespan. We also discuss the potential of increased flight activity of infected bees to reduce pathogen transmission inside the hive. Further research is needed to understand the consequences of host behavioral changes on pathogen transmission. This knowledge may contribute to enhance natural colony defense behaviors through beekeeping practices to reduce probability of colony losses.

Emigration of the plant pathogen Pseudomonas syringae from leaf litter contributes to its population dynamics in alpine snowpack.

The recently discovered ubiquity of the plant pathogen Pseudomonas syringae in headwaters and alpine ecosystems worldwide elicits new questions about the ecology of this bacterium and subsequent consequences for disease epidemiology. Because of the major contribution of snow to river run-off during crop growth, we evaluated the population dynamics of P.syringae in snowpack and the underlying leaf litter during two years in the Southern French Alps. High population densities of P.syringae were found on alpine grasses, and leaf litter was identified as the main source of populations of P.syringae in snowpack, contributing more than the populations arriving with the snowfall. The insulating properties of snow foster survival of P.syringae throughout the winter in the 10 cm layer of snow closest to the soil. Litter and snowpack harboured populations of P.syringae that were very diverse in terms of phenotypes and genotypes. Neither substrate nor sampling site had a marked effect on the structure of P.syringae populations, and snow and litter had genotypes in common with other non-agricultural habitats and with crops. These results contribute to the mounting evidence that a highly diverse P.syringae metapopulation is disseminated throughout drainage basins between cultivated and non-cultivated zones.