Comparison of Frequentist and Bayesian Meta-Analysis Models for Assessing the Efficacy of Decision Support Systems in Reducing Fungal Disease Incidence

Diseases of fruit and foliage caused by fungi and oomycetes are generally controlled by the application of fungicides. The use of decision support systems (DSSs) may assist to optimize fungicide programs to enhance application on the basis of risk associated with disease outbreak. Case-by-case evaluations demonstrated the performance of DSSs for disease control, but an overall assessment of the efficacy of DSSs is lacking. A literature review was conducted to synthesize the results of 67 experiments assessing DSSs. Disease incidence data were obtained from published peer-reviewed field trials comparing untreated controls, calendar-based and DSS-based fungicide programs. Two meta-analysis generic models, a “fixed-effects” vs. a “random-effects” model within the framework of generalized linear models were evaluated to assess the efficacy of DSSs in reducing incidence. All models were fit using both frequentist and Bayesian estimation procedures and the results compared. Model including random effects showed better performance in terms of AIC or DIC and goodness of fit. In general, the frequentist and Bayesian approaches produced similar results. Odds ratio and incidence ratio values showed that calendar-based and DSS-based fungicide programs considerably reduced disease incidence compared to the untreated control. Moreover, calendar-based and DSS-based programs provided similar reductions in disease incidence, further supporting the efficacy of DSSs

Disease progression of peach powdery mildew in Catalonia, Spain – Towards a decision support system based on degree-days to initiate fungicide spray programs

The incidence of peach powdery mildew (PPM) on fruit was monitored in commercial peach orchards to i) describe the disease progress in relation to several environmental parameters and ii) establish an operating threshold to initiate a fungicide spray program based on accumulated degree-day (ADD) data. A beta-regression model for disease incidence showed a substantial contribution of the random effects orchard and year, whereas relevant fixed effects corresponded to ADD, wetness duration, and ADD considering vapor pressure deficit and rain. When beta-regression models were fitted for each orchard and year considering only ADD, disease onset was observed at 242 ± 13 ADD and symptoms did not develop further after 484 ± 42 ADD. An operating threshold to initiate fungicide applications was established at 220 ADD, coinciding with a PPM incidence in fruit around 0.05. A validation was further conducted by comparing PPM incidence in i) a standard, calendar-based program, ii) a program with applications initiated at 220 ADD, and iii) a nontreated control. A statistically relevant reduction in disease incidence in fruit was obtained with both fungicide programs, from 0.244 recorded in the control to 0.073 with the 220-ADD alert program, and 0.049 with the standard program. The 220-ADD alert program resulted in 33% reduction in fungicide applications

Spatial Modelling of Black Scabbardfish Fishery Off the Portuguese Coast

The Black Scabbardfish is a deep-water fish species that lives at depths greater than 700 m. In Portugal mainland, this is an important commercial resource which is exploited by longliners that operate at specific fishing grounds located off the coast. The monitoring of the population status mainly relies on the fishery data as no independent scientific surveys take place. The present work focus on modelling the spatial distribution of the BSF species relative biomass. Georeferenced data given by the location of the fishing hauls and the corresponding catches are available for a set of different vessels that belong to the longline fishing fleet. A classical geostatistical approach was applied to fit a variogram and evaluate the isotropy of the data. Then, different regression models with fixed, structured and unstructured random effects were fitted under a Bayesian framework, considering the Stochastic Partial Differential Equation (SPDE) methodology under the Integrated Nested Laplace Approximation (INLA), addressing some practical implementation issues. The models with spatial effects seemed to perform better, although some practical constraints related to the considered covariates hindered the choice.authorsversionpublishe

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

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

Muons in air showers at the Pierre Auger Observatory: Mean number in highly inclined events

We present the first hybrid measurement of the average muon number in air showers at ultrahigh energies, initiated by cosmic rays with zenith angles between 62° and 80°. The measurement is based on 174 hybrid events recorded simultaneously with the surface detector array and the fluorescence detector of the Pierre Auger Observatory. The muon number for each shower is derived by scaling a simulated reference profile of the lateral muon density distribution at the ground until it fits the data. A 10^19  eV shower with a zenith angle of 67°, which arrives at the surface detector array at an altitude of 1450 m above sea level, contains on average (2.68±0.04±0.48(sys))×10^7 muons with energies larger than 0.3 GeV. The logarithmic gain d ln N_μ/dlnE of muons with increasing energy between 4×1018  eV and 5×1019  eV is measured to be (1.029±0.024±0.030(sys)

Extracting relevant predictive variables for COVID-19 severity prognosis: An exhaustive comparison of feature selection techniques

With the COVID-19 pandemic having caused unprecedented numbers of infections and deaths, large research efforts have been undertaken to increase our understanding of the disease and the factors which determine diverse clinical evolutions. Here we focused on a fully data-driven exploration regarding which factors (clinical or otherwise) were most informative for SARS-CoV-2 pneumonia severity prediction via machine learning (ML). In particular, feature selection techniques (FS), designed to reduce the dimensionality of data, allowed us to characterize which of our variables were the most useful for ML prognosis. We conducted a multi-centre clinical study, enrolling n=1548 patients hospitalized due to SARS-CoV-2 pneumonia: where 792, 238, and 598 patients experienced low, medium and high-severity evolutions, respectively. Up to 106 patient-specific clinical variables were collected at admission, although 14 of them had to be discarded for containing ⩾60% missing values. Alongside 7 socioeconomic attributes and 32 exposures to air pollution (chronic and acute), these became d=148 features after variable encoding. We addressed this ordinal classification problem both as a ML classification and regression task. Two imputation techniques for missing data were explored, along with a total of 166 unique FS algorithm configurations: 46 filters, 100 wrappers and 20 embeddeds. Of these, 21 setups achieved satisfactory bootstrap stability (⩾0.70) with reasonable computation times: 16 filters, 2 wrappers, and 3 embeddeds. The subsets of features selected by each technique showed modest Jaccard similarities across them. However, they consistently pointed out the importance of certain explanatory variables. Namely: patient’s C-reactive protein (CRP), pneumonia severity index (PSI), respiratory rate (RR) and oxygen levels –saturation SpO2, quotients SpO2/RR and arterial SatO2/FiO2 –, the neutrophil-to-lymphocyte ratio (NLR) –to certain extent, also neutrophil and lymphocyte counts separately–, lactate dehydrogenase (LDH), and procalcitonin (PCT) levels in blood. A remarkable agreement has been found a posteriori between our strategy and independent clinical research works investigating risk factors for COVID-19 severity. Hence, these findings stress the suitability of this type of fully data-driven approaches for knowledge extraction, as a complementary to clinical perspectives

Cost-sensitive ordinal classification methods to predict SARS-CoV-2 pneumonia severity

Objective: To study the suitability of cost-sensitive ordinal artificial intelligence-machine learning (AI-ML) strategies in the prognosis of SARS-CoV-2 pneumonia severity. Materials & methods: Observational, retrospective, longitudinal, cohort study in 4 hospitals in Spain. Information regarding demographic and clinical status was supplemented by socioeconomic data and air pollution exposures. We proposed AI-ML algorithms for ordinal classification via ordinal decomposition and for cost-sensitive learning via resampling techniques. For performance-based model selection, we defined a custom score including per-class sensitivities and asymmetric misprognosis costs. 260 distinct AI-ML models were evaluated via 10 repetitions of 5×5 nested cross-validation with hyperparameter tuning. Model selection was followed by the calibration of predicted probabilities. Final overall performance was compared against five well-established clinical severity scores and against a ‘standard’ (non-cost sensitive, non-ordinal) AI-ML baseline. In our best model, we also evaluated its explainability with respect to each of the input variables. Results: The study enrolled $n$=1548 patients: 712 experienced low, 238 medium, and 598 high clinical severity. $d$=131 variables were collected, becoming $d′$=148 features after categorical encoding. Model selection resulted in our best-performing AI-ML pipeline having: a) no imputation of missing data, b) no feature selection (i.e. using the full set of $d′$ features), c) ‘Ordered Partitions’ ordinal decomposition, d) cost-based reimbalance, and e) a Histogram-based Gradient Boosting classifier. This best model (calibrated) obtained a median accuracy of 68.1% [67.3%, 68.8%] (95% confidence interval), a balanced accuracy of 57.0% [55.6%, 57.9%], and an overall area under the curve (AUC) 0.802 [0.795, 0.808]. In our dataset, it outperformed all five clinical severity scores and the ‘standard’ AI-ML baseline. Discussion & conclusion: We conducted an exhaustive exploration of AI-ML methods designed for both ordinal and cost-sensitive classification, motivated by a real-world application domain (clinical severity prognosis) in which these topics arise naturally. Our model with the best classification performance exploited successfully the ordering information of ground truth classes, coping with imbalance and asymmetric costs. However, these ordinal and cost-sensitive aspects are seldom explored in the literature

Impact of outdoor air pollution on severity and mortality in COVID-19 pneumonia

The relationship between exposure to air pollution and the severity of coronavirus disease 2019 (COVID-19) pneumonia and other outcomes is poorly understood. Beyond age and comorbidity, risk factors for adverse outcomes including death have been poorly studied. The main objective of our study was to examine the relationship between exposure to outdoor air pollution and the risk of death in patients with COVID-19 pneumonia using individual-level data. The secondary objective was to investigate the impact of air pollutants on gas exchange and systemic inflammation in this disease. This cohort study included 1548 patients hospitalised for COVID-19 pneumonia between February and May 2020 in one of four hospitals. Local agencies supplied daily data on environmental air pollutants ($PM_{10}$, $PM_{2.5}$, $O_3$, $NO_2$, $NO$ and $NO_X$) and meteorological conditions (temperature and humidity) in the year before hospital admission (from January 2019 to December 2019). Daily exposure to pollution and meteorological conditions by individual postcode of residence was estimated using geospatial Bayesian generalised additive models. The influence of air pollution on pneumonia severity was studied using generalised additive models which included: age, sex, Charlson comorbidity index, hospital, average income, air temperature and humidity, and exposure to each pollutant. Additionally, generalised additive models were generated for exploring the effect of air pollution on C-reactive protein (CRP) level and Sp$O_2$/Fi$O_2$ at admission. According to our results, both risk of COVID-19 death and CRP level increased significantly with median exposure to $PM_{10}$, $NO_2$, $NO$ and $NO_X$, while higher exposure to $NO_2$, $NO$ and $NO_X$ was associated with lower Sp$O_2$/Fi$O_2$ ratios. In conclusion, after controlling for socioeconomic, demographic and health-related variables, we found evidence of a significant positive relationship between air pollution and mortality in patients hospitalised for COVID-19 pneumonia. Additionally, inflammation (CRP) and gas exchange (Sp$O_2$/Fi$O_2$) in these patients were significantly related to exposure to air pollution