A mixture of mobility and meteorological data provides a high correlation with COVID-19 growth in an infection-naive population: a study for Spanish provinces.

Introduction: We use Spanish data from August 2020 to March 2021 as a natural experiment to analyze how a standardized measure of COVID-19 growth correlates with asymmetric meteorological and mobility situations in 48 Spanish provinces. The period of time is selected prior to vaccination so that the level of susceptibility was high, and during geographically asymmetric implementation of non-pharmacological interventions. Methods: We develop reliable aggregated mobility data from different public sources and also compute the average meteorological time series of temperature, dew point, and UV radiance in each Spanish province from satellite data. We perform a dimensionality reduction of the data using principal component analysis and investigate univariate and multivariate correlations of mobility and meteorological data with COVID-19 growth. Results: We find significant, but generally weak, univariate correlations for weekday aggregated mobility in some, but not all, provinces. On the other hand, principal component analysis shows that the different mobility time series can be properly reduced to three time series. A multivariate time-lagged canonical correlation analysis of the COVID-19 growth rate with these three time series reveals a highly significant correlation, with a median R-squared of 0.65. The univariate correlation between meteorological data and COVID-19 growth is generally not significant, but adding its two main principal components to the mobility multivariate analysis increases correlations significantly, reaching correlation coefficients between 0.6 and 0.98 in all provinces with a median R-squared of 0.85. This result is robust to different approaches in the reduction of dimensionality of the data series. Discussion: Our results suggest an important effect of mobility on COVID-19 cases growth rate. This effect is generally not observed for meteorological variables, although in some Spanish provinces it can become relevant. The correlation between mobility and growth rate is maximal at a time delay of 2-3 weeks, which agrees well with the expected 5?10 day delays between infection, development of symptoms, and the detection/report of the case.

Performance of outbreak management plans for emerging plant diseases: the case of almond leaf scorch caused by Xylella fastidiosa in mainland Spain.

Outbreak response to quarantine pathogens and pests in the European Union (EU) is regulated by the EU Plant Health Law, but the performance of outbreak management plans in terms of their effectiveness and efficiency has been quantified only to a limited extent. As a case study, the disease dynamics of almond leaf scorch, caused by Xylella fastidiosa (Xf), in the affected area of Alicante, Spain, were approximated using an individual-based spatial epidemiological model. The emergence of this outbreak was dated based on phylogenetic studies, and official surveys were used to delimit the current extent of the disease. Different survey strategies and disease control measures were compared to determine their effectiveness and efficiency for outbreak management in relation to a baseline scenario without interventions. One-step and two-step survey approaches were compared with different confidence levels, buffer zone sizes and eradication radii, including those set by the EU legislation for Xf. The effect of disease control interventions was also considered by decreasing the transmission rate in the buffer zone. All outbreak management plans reduced the number of infected trees (effectiveness) but large differences were observed in the number of susceptible trees not eradicated (efficiency). The two-step survey approach and high confidence level increased the efficiency, while also reducing the transmission rate. Only the outbreak management plans with the two-step survey approach removed infected trees completely, but they required greater survey efforts. Although control measures reduced disease spread, surveillance was the key factor in the effectiveness and efficiency of the outbreak management plans.

Bayesian hierarchical modelling of the geospatial distribution of fasciolosis in dairy cattle and the impact on production: Application to the main milk-producing region (Galicia) in Spain.

Fasciolosis caused by Fasciola hepatica is a common parasitic infection among cattle in many countries. Although infected adult cows rarely show overt clinical signs, milk production may be impaired. Thus, significant production losses may occur in dairy herds with a high prevalence of fasciolosis. In this study, Bayesian hierarchical modelling was used to estimate the geospatial distribution of dairy cattle fasciolosis and its impact on milk production. The study was conducted in Galicia, the main milk producing region in Spain and a geographically heterogeneous area. The aims were: 1) to model the geospatial distribution of fasciolosis in dairy herds in the study area, 2) to identify clusters of herds with a high prevalence of fasciolosis, and 3) to assess the effect of fasciolosis on milk yield and quality. A large number of dairy cattle farms (n = 4907), of which 1660 provided production records, were surveyed. Fasciola infection status was determined by applying the MM3-SERO ELISA test to bulk tank milk samples. A high probability of infection was predicted in several zones, particularly in the centre, northeast and southeast of Galicia. Conversely, the predicted probability was very low in some parts of the northwest of the region. Infections with high within-herd prevalence (> 25% lactating cows infected) predominated. High within-herd prevalence was associated with loss of milk production (-1.387 kg/cow/ day, on average). No association between Fasciola infection and either milk fat or protein content was observed. This study has generated the first maps of the spatial distribution of the probability of Fasciola infection in dairy cattle herds in Galicia. The maps presented here can be used for reference purposes, enabling the design of better targeted fasciolosis control programmes in the region. Use of Bayesian hierarchical statistical analysis enabled us to ascertain the uncertainty of the predictions and to account for the spatial autocorrelation in the data. It also enabled us to generate maps showing the residual spatial variation in milk production, a topic that may deserve more detailed study.

Modelling seabirds biodiversity through Bayesian Spatial Beta regression models: A proxy to inform marine protected areas in the Mediterranean Sea.

Seabirds are bioindicators of marine ecosystems health and one of the world's most endangered avian groups. The creation of marine protected areas plays an important role in the conservation of marine environment and its biodiversity. The distributions of top predators, as seabirds, have been commonly used for the management and creation of these figures of protection. The main objective of this study is to investigate seabirds biodiversity distribution in the Mediterranean Sea through the use of Bayesian spatial Beta regression models. We used an extensive historical database of at-sea locations of 19 different seabird species as well as geophysical, climatology variables and cumulative anthropogenic threats to model species biodiversity. We found negative associations between seabirds biodiversity and distance to the coast as well as concavity of the seabed, and positive with chlorophyll and slope. Further, a positive association was found between seabirds biodiversity and coastal impact. In this study we define as hot spot of seabird biodiversity those areas with a posterior predictive mean over 0.50. We found potential hot spots in the Mediterranean Sea which do not overlap with the existing MPASs and marine IBAs. Specifically, our hot spots areas do not overlap with the 52.04% and 16.87% of the current MPAs and marine IBAs, respectively. Overall, our study highlights the need for the extension of spatial prioritization of conservation areas to seabirds biodiversity, addressing the challenges of establishing transboundary governance.

A mesoscale analysis of relations between fish species richness and environmental and anthropogenic pressures in the Mediterranean Sea.

Although there is a great knowledge about individual anthropogenic threats to different fish species in the Mediterranean Sea, little is known about how these threats accumulate and interact to affect fish species richness in conjunction with environmental dynamics. This study assesses the role of these threats in the fish richness component and identifies the main areas where the interaction between fish species richness and threats is highest. Our results show that fish richness seems to be higher in saltier and colder areas where the chlorophyll-a and phosphate concentrations are lower. Among the anthropogenic threats analyzed, the costal impact and the fishing effort seems to be the more relevant ones. Overall areas with high fish richness are mainly located along the western and northern shores, with lower values in the south-eastern regions. Areas of potential high cumulative threats are widespread in both the western and eastern basins, with fewer areas located in the south-eastern region. By describing the spatial patterns of the fish richness and which drivers explain these patterns we can also identify which anthropogenic activities can be managed more effectively to maintain and restore marine fish biodiversity in the basin.

Green gentrification in European and North American cities.

Although urban greening is universally recognized as an essential part of sustainable and climate-responsive cities, a growing literature on green gentrification argues that new green infrastructure, and greenspace in particular, can contribute to gentrification, thus creating social and racial inequalities in access to the benefits of greenspace and further environmental and climate injustice. In response to limited quantitative evidence documenting the temporal relationship between new greenspaces and gentrification across entire cities, let alone across various international contexts, we employ a spatially weighted Bayesian model to test the green gentrification hypothesis across 28 cities in 9 countries in North America and Europe. Here we show a strong positive and relevant relationship for at least one decade between greening in the 1990s-2000s and gentrification that occurred between 2000-2016 in 17 of the 28 cities. Our results also determine whether greening plays a "lead", "integrated", or "subsidiary" role in explaining gentrification.

Modeling the Spatial Distribution of Xylella fastidiosa: A Nonstationary Approach with Dispersal Barriers.

Spatial species distribution models often assume isotropy and stationarity, implying that spatial dependence is direction-invariant and uniform throughout the study area. However, these assumptions are violated when dispersal barriers are present. Despite this, the issue of nonstationarity has been little explored in the context of plant health. The objective of this study was to evaluate the influence of barriers in the distribution of Xylella fastidiosa in the demarcated area in Alicante, Spain. Occurrence data from 2018 were analyzed through spatial Bayesian hierarchical models. The stationary model, illustrating a scenario without control interventions or geographical features, was compared with three nonstationary models: a model with mountains as physical barriers, and two models with a continuous and discontinuous perimeter barrier representing hypothetical control interventions. In the stationary model, the posterior mean of the spatial range, as the distance where two observations are uncorrelated, was 4,030 m 95% credible interval (2,907 to 5,564). This distance can be used to define the buffer zone in the demarcated area. The predicted probability of X. fastidiosa presence in the area outside the barrier was 0.46 with the stationary model, whereas it was reduced to 0.29 and 0.36 with the continuous and discontinuous barrier models, respectively. Differences between the discontinuous and continuous barrier models showed that breaks, where no control interventions were implemented, resulted in a higher predicted probability of X. fastidiosa presence in the areas with low sampling intensity. These results may help authorities prioritize the areas for surveillance and disease control.[Formula: see text] Copyright © 2022 The Author(s). This is an open access article distributed under the CC BY 4.0 International license.

Monitoring and Analysis of COVID-19 Pandemic: The Need for an Empirical Approach.

The current worldwide pandemic produced by coronavirus disease 2019 (COVID-19) has changed the paradigm of mathematical epidemiology due to the high number of unknowns of this new disease. Thus, the empirical approach has emerged as a robust tool to analyze the actual situation carried by the countries and also allows us to predict the incoming scenarios. In this paper, we propose three empirical indexes to estimate the state of the pandemic. These indexes quantify both the propagation and the number of estimated cases, allowing us to accurately determine the real risk of a country. We have calculated these indexes' evolution for several European countries. Risk diagrams are introduced as a tool to visualize the evolution of a country and evaluate its current risk as a function of the number of contagious individuals and the empiric reproduction number. Risk diagrams at the regional level are useful to observe heterogeneity on COVID-19 penetration and spreading in some countries, which is essential during deconfinement processes. During the pandemic, there have been significant differences seen in countries reporting case criterion and detection capacity. Therefore, we have introduced estimations about the real number of infectious cases that allows us to have a broader view and to better estimate the risk. These diagrams and indexes have been successfully used for the monitoring of European countries and regions during the COVID-19 pandemic.

Modeling Inoculum Availability of Plurivorosphaerella nawae in Persimmon Leaf Litter with Bayesian Beta Regression.

Circular leaf spot (CLS), caused by Plurivorosphaerella nawae, is a serious disease affecting persimmon (Diospyros kaki) that is characterized by necrotic lesions on leaves, defoliation, and fruit drop. Under Mediterranean conditions, P. nawae forms pseudothecia in the leaf litter in winter, and ascospores are released in spring, infecting susceptible leaves. Persimmon growers are advised to apply fungicides for CLS control during the period of inoculum availability, which was previously defined based on ascospore counts under the microscope. A model of inoculum availability of P. nawae was developed and evaluated as an alternative to ascospore counts. Leaf litter samples were collected weekly in L'Alcúdia (Spain) from 2010 to 2015. Leaves were soaked and placed in a wind tunnel, and the released ascospores of P. nawae were counted. Hierarchical Bayesian beta regression methods were used to model the dynamics of ascospore production in the leaf litter. The selected model included accumulated degree-days (ADDs) and ADDs taking into account the vapor pressure deficit (ADDvpd) as fixed effects and year as random effect. This model had a mean absolute error of 0.042 and a root mean square error of 0.062. The beta regression model was evaluated in four orchards from 2010 to 2015. Higher accuracy was obtained at the beginning and the end of the ascospore production period, which are the events of interest to schedule fungicide sprays for CLS control in Spain. This same modeling framework can be extended to other fungal plant pathogens whose inoculum dynamics are expressed as proportion data.[Formula: see text] Copyright © 2021 The Author(s). This is an open access article distributed under the CC BY 4.0 International license.

Spatial Bayesian Modeling Applied to the Surveys of Xylella fastidiosa in Alicante (Spain) and Apulia (Italy).

The plant-pathogenic bacterium Xylella fastidiosa was first reported in Europe in 2013, in the province of Lecce, Italy, where extensive areas were affected by the olive quick decline syndrome, caused by the subsp. pauca. In Alicante, Spain, almond leaf scorch, caused by X. fastidiosa subsp. multiplex, was detected in 2017. The effects of climatic and spatial factors on the geographic distribution of X. fastidiosa in these two infested regions in Europe were studied. The presence/absence data of X. fastidiosa in the official surveys were analyzed using Bayesian hierarchical models through the integrated nested Laplace approximation (INLA) methodology. Climatic covariates were obtained from the WorldClim v.2 database. A categorical variable was also included according to Purcell's minimum winter temperature thresholds for the risk of occurrence of Pierce's disease of grapevine, caused by X. fastidiosa subsp. fastidiosa. In Alicante, data were presented aggregated on a 1 km grid (lattice data), where the spatial effect was included in the model through a conditional autoregressive structure. In Lecce, data were observed at continuous locations occurring within a defined spatial domain (geostatistical data). Therefore, the spatial effect was included via the stochastic partial differential equation approach. In Alicante, the pathogen was detected in all four of Purcell's categories, illustrating the environmental plasticity of the subsp. multiplex. Here, none of the climatic covariates were retained in the selected model. Only two of Purcell's categories were represented in Lecce. The mean diurnal range (bio2) and the mean temperature of the wettest quarter (bio8) were retained in the selected model, with a negative relationship with the presence of the pathogen. However, this may be due to the heterogeneous sampling distribution having a confounding effect with the climatic covariates. In both regions, the spatial structure had a strong influence on the models, but not the climatic covariates. Therefore, pathogen distribution was largely defined by the spatial relationship between geographic locations. This substantial contribution of the spatial effect in the models might indicate that the current extent of X. fastidiosa in the study regions had arisen from a single focus or from several foci, which have been coalesced.

Ecological, genetic and evolutionary drivers of regional genetic differentiation in Arabidopsis thaliana.

BACKGROUND: Disentangling the drivers of genetic differentiation is one of the cornerstones in evolution. This is because genetic diversity, and the way in which it is partitioned within and among populations across space, is an important asset for the ability of populations to adapt and persist in changing environments. We tested three major hypotheses accounting for genetic differentiation-isolation-by-distance (IBD), isolation-by-environment (IBE) and isolation-by-resistance (IBR)-in the annual plant Arabidopsis thaliana across the Iberian Peninsula, the region with the largest genomic diversity. To that end, we sampled, genotyped with genome-wide SNPs, and analyzed 1772 individuals from 278 populations distributed across the Iberian Peninsula. RESULTS: IBD, and to a lesser extent IBE, were the most important drivers of genetic differentiation in A. thaliana. In other words, dispersal limitation, genetic drift, and to a lesser extent local adaptation to environmental gradients, accounted for the within- and among-population distribution of genetic diversity. Analyses applied to the four Iberian genetic clusters, which represent the joint outcome of the long demographic and adaptive history of the species in the region, showed similar results except for one cluster, in which IBR (a function of landscape heterogeneity) was the most important driver of genetic differentiation. Using spatial hierarchical Bayesian models, we found that precipitation seasonality and topsoil pH chiefly accounted for the geographic distribution of genetic diversity in Iberian A. thaliana. CONCLUSIONS: Overall, the interplay between the influence of precipitation seasonality on genetic diversity and the effect of restricted dispersal and genetic drift on genetic differentiation emerges as the major forces underlying the evolutionary trajectory of Iberian A. thaliana.

Two-variable nullcline analysis of ionic general equilibrium predicts calcium homeostasis in ventricular myocytes.

Ventricular contraction is roughly proportional to the amount of calcium released from the Sarcoplasmic Reticulum (SR) during systole. While it is rather straightforward to measure calcium levels and contractibility under different physiological conditions, the complexity of calcium handling during systole and diastole has made the prediction of its release at steady state impossible. Here we approach the problem analyzing the evolution of intracellular and extracellular calcium fluxes during a single beat which is away from homeostatic balance. Using an in-silico subcellular model of rabbit ventricular myocyte, we show that the high dimensional nonlinear problem of finding the steady state can be reduced to a two-variable general equilibrium condition where pre-systolic calcium level in the cytosol and in the SR must fulfill simultaneously two different equalities. This renders calcium homeostasis as a problem that can be studied in terms of its equilibrium structure, leading to precise predictions of steady state from single-beat measurements. We show how changes in ion channels modify the general equilibrium, as shocks would do in general equilibrium macroeconomic models. This allows us to predict when an enhanced entrance of calcium in the cell reduces its contractibility and explain why SERCA gene therapy, a change in calcium handling to treat heart failure, might fail to improve contraction even when it successfully increases SERCA expression.

A hierarchical Bayesian Beta regression approach to study the effects of geographical genetic structure and spatial autocorrelation on species distribution range shifts.

Global climate change (GCC) may be causing distribution range shifts in many organisms worldwide. Multiple efforts are currently focused on the development of models to better predict distribution range shifts due to GCC. We addressed this issue by including intraspecific genetic structure and spatial autocorrelation (SAC) of data in distribution range models. Both factors reflect the joint effect of ecoevolutionary processes on the geographical heterogeneity of populations. We used a collection of 301 georeferenced accessions of the annual plant Arabidopsis thaliana in its Iberian Peninsula range, where the species shows strong geographical genetic structure. We developed spatial and nonspatial hierarchical Bayesian models (HBMs) to depict current and future distribution ranges for the four genetic clusters detected. We also compared the performance of HBMs with Maxent (a presence-only model). Maxent and nonspatial HBMs presented some shortcomings, such as the loss of accessions with high genetic admixture in the case of Maxent and the presence of residual SAC for both. As spatial HBMs removed residual SAC, these models showed higher accuracy than nonspatial HBMs and handled the spatial effect on model outcomes. The ease of modelling and the consistency among model outputs for each genetic cluster was conditioned by the sparseness of the populations across the distribution range. Our HBMs enrich the toolbox of software available to evaluate GCC-induced distribution range shifts by considering both genetic heterogeneity and SAC, two inherent properties of any organism that should not be overlooked.

Accounting for preferential sampling in species distribution models.

Species distribution models (SDMs) are now being widely used in ecology for management and conservation purposes across terrestrial, freshwater, and marine realms. The increasing interest in SDMs has drawn the attention of ecologists to spatial models and, in particular, to geostatistical models, which are used to associate observations of species occurrence or abundance with environmental covariates in a finite number of locations in order to predict where (and how much of) a species is likely to be present in unsampled locations. Standard geostatistical methodology assumes that the choice of sampling locations is independent of the values of the variable of interest. However, in natural environments, due to practical limitations related to time and financial constraints, this theoretical assumption is often violated. In fact, data commonly derive from opportunistic sampling (e.g., whale or bird watching), in which observers tend to look for a specific species in areas where they expect to find it. These are examples of what is referred to as preferential sampling, which can lead to biased predictions of the distribution of the species. The aim of this study is to discuss a SDM that addresses this problem and that it is more computationally efficient than existing MCMC methods. From a statistical point of view, we interpret the data as a marked point pattern, where the sampling locations form a point pattern and the measurements taken in those locations (i.e., species abundance or occurrence) are the associated marks. Inference and prediction of species distribution is performed using a Bayesian approach, and integrated nested Laplace approximation (INLA) methodology and software are used for model fitting to minimize the computational burden. We show that abundance is highly overestimated at low abundance locations when preferential sampling effects not accounted for, in both a simulated example and a practical application using fishery data. This highlights that ecologists should be aware of the potential bias resulting from preferential sampling and account for it in a model when a survey is based on non-randomized and/or non-systematic sampling.

Reference genome assessment from a population scale perspective: an accurate profile of variability and noise.

MOTIVATION: Current plant and animal genomic studies are often based on newly assembled genomes that have not been properly consolidated. In this scenario, misassembled regions can easily lead to false-positive findings. Despite quality control scores are included within genotyping protocols, they are usually employed to evaluate individual sample quality rather than reference sequence reliability. We propose a statistical model that combines quality control scores across samples in order to detect incongruent patterns at every genomic region. Our model is inherently robust since common artifact signals are expected to be shared between independent samples over misassembled regions of the genome. RESULTS: The reliability of our protocol has been extensively tested through different experiments and organisms with accurate results, improving state-of-the-art methods. Our analysis demonstrates synergistic relations between quality control scores and allelic variability estimators, that improve the detection of misassembled regions, and is able to find strong artifact signals even within the human reference assembly. Furthermore, we demonstrated how our model can be trained to properly rank the confidence of a set of candidate variants obtained from new independent samples. AVAILABILITY AND IMPLEMENTATION: This tool is freely available at http://gitlab.com/carbonell/ces. CONTACT: jcarbonell.cipf@gmail.com or joaquin.dopazo@juntadeandalucia.es. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Spatio-Temporal Analysis of Suicide-Related Emergency Calls.

Considerable effort has been devoted to incorporate temporal trends in disease mapping. In this line, this work describes the importance of including the effect of the seasonality in a particular setting related with suicides. In particular, the number of suicide-related emergency calls is modeled by means of an autoregressive approach to spatio-temporal disease mapping that allows for incorporating the possible interaction between both temporal and spatial effects. Results show the importance of including seasonality effect, as there are differences between the number of suicide-related emergency calls between the four seasons of each year.

Modelling the presence of disease under spatial misalignment using Bayesian latent Gaussian models.

Modelling patterns of the spatial incidence of diseases using local environmental factors has been a growing problem in the last few years. Geostatistical models have become popular lately because they allow estimating and predicting the underlying disease risk and relating it with possible risk factors. Our approach to these models is based on the fact that the presence/absence of a disease can be expressed with a hierarchical Bayesian spatial model that incorporates the information provided by the geographical and environmental characteristics of the region of interest. Nevertheless, our main interest here is to tackle the misalignment problem arising when information about possible covariates are partially (or totally) different than those of the observed locations and those in which we want to predict. As a result, we present two different models depending on the fact that there is uncertainty on the covariates or not. In both cases, Bayesian inference on the parameters and prediction of presence/absence in new locations are made by considering the model as a latent Gaussian model, which allows the use of the integrated nested Laplace approximation. In particular, the spatial effect is implemented with the stochastic partial differential equation approach. The methodology is evaluated on the presence of the Fasciola hepatica in Galicia, a North-West region of Spain.

Bovine paramphistomosis in Galicia (Spain): prevalence, intensity, aetiology and geospatial distribution of the infection.

The present study explored various basic aspects of the epidemiology of paramphistomosis in Galicia, the main cattle producing region in Spain. In total, 589 cows from different farms located across the region were selected at random in the slaughterhouse for examination of the rumens and reticula for the presence of Paramphistomidae flukes. Paramphistomes were found in 111 of 589 necropsied cows (18.8%; 95% CI: 15.7-21.9%), with higher prevalences of infection in beef cows than in dairy cows (29.2% vs 13.9%). Although the number of flukes per animal was generally low (median=266 flukes), some cows harboured large parasite burdens (up to 11,895 flukes), which may have harmful effects on their health or productivity. Cows with higher parasite burdens also excreted greater numbers of fluke eggs in their faeces, which suggests that heavily parasitized mature cows play an important role in the transmission of paramphistomosis. This role may be particularly important in Galicia, where the roe deer, which is the only wild ruminant in the study area, was found not to be a reservoir for the infection. The use of morpho-anatomical and molecular techniques applied to a large number of fluke specimens provided reliable confirmation that Calicophoron daubneyi is the only species of the family Paramphistomidae that parasitizes cattle in Galicia. The environmental data from the farms of origin of the necropsied cows were used in Bayesian geostatistical models to predict the probability of infection by C. daubneyi throughout the region. The results revealed the role of environmental risk factors in explaining the geographical heterogeneity in the probability of infection in beef and dairy cattle. These explanatory factors were used to construct predictive maps showing the areas with the highest predicted risk of infection as well as the uncertainty associated with the predictions.

FluDetWeb: an interactive web-based system for the early detection of the onset of influenza epidemics.

BACKGROUND: The early identification of influenza outbreaks has became a priority in public health practice. A large variety of statistical algorithms for the automated monitoring of influenza surveillance have been proposed, but most of them require not only a lot of computational effort but also operation of sometimes not-so-friendly software. RESULTS: In this paper, we introduce FluDetWeb, an implementation of a prospective influenza surveillance methodology based on a client-server architecture with a thin (web-based) client application design. Users can introduce and edit their own data consisting of a series of weekly influenza incidence rates. The system returns the probability of being in an epidemic phase (via e-mail if desired). When the probability is greater than 0.5, it also returns the probability of an increase in the incidence rate during the following week. The system also provides two complementary graphs. This system has been implemented using statistical free-software (R and WinBUGS), a web server environment for Java code (Tomcat) and a software module created by us (Rdp) responsible for managing internal tasks; the software package MySQL has been used to construct the database management system. The implementation is available on-line from: http://www.geeitema.org/meviepi/fludetweb/. CONCLUSION: The ease of use of FluDetWeb and its on-line availability can make it a valuable tool for public health practitioners who want to obtain information about the probability that their system is in an epidemic phase. Moreover, the architecture described can also be useful for developers of systems based on computationally intensive methods.