Modelling the monthly abundance of Culicoides biting midges in nine European countries using Random Forests machine learning.

BACKGROUND: Culicoides biting midges transmit viruses resulting in disease in ruminants and equids such as bluetongue, Schmallenberg disease and African horse sickness. In the past decades, these diseases have led to important economic losses for farmers in Europe. Vector abundance is a key factor in determining the risk of vector-borne disease spread and it is, therefore, important to predict the abundance of Culicoides species involved in the transmission of these pathogens. The objectives of this study were to model and map the monthly abundances of Culicoides in Europe. METHODS: We obtained entomological data from 904 farms in nine European countries (Spain, France, Germany, Switzerland, Austria, Poland, Denmark, Sweden and Norway) from 2007 to 2013. Using environmental and climatic predictors from satellite imagery and the machine learning technique Random Forests, we predicted the monthly average abundance at a 1 km2 resolution. We used independent test sets for validation and to assess model performance. RESULTS: The predictive power of the resulting models varied according to month and the Culicoides species/ensembles predicted. Model performance was lower for winter months. Performance was higher for the Obsoletus ensemble, followed by the Pulicaris ensemble, while the model for Culicoides imicola showed a poor performance. Distribution and abundance patterns corresponded well with the known distributions in Europe. The Random Forests model approach was able to distinguish differences in abundance between countries but was not able to predict vector abundance at individual farm level. CONCLUSIONS: The models and maps presented here represent an initial attempt to capture large scale geographical and temporal variations in Culicoides abundance. The models are a first step towards producing abundance inputs for R0 modelling of Culicoides-borne infections at a continental scale.

Surveillance of animal diseases through implementation of a Bayesian spatio-temporal model: A simulation example with neurological syndromes in horses and West Nile Virus.

A potentially sensitive way to detect disease outbreaks is syndromic surveillance, i.e. monitoring the number of syndromes reported in the population of interest, comparing it to the baseline rate, and drawing conclusions about outbreaks using statistical methods. A decision maker may use the results to take disease control actions or to initiate enhanced epidemiological investigations. In addition to the total count of syndromes there are often additional pieces of information to consider when assessing the probability of an outbreak. This includes clustering of syndromes in space and time as well as historical data on the occurrence of syndromes, seasonality of the disease, etc. In this paper, we show how Bayesian theory for syndromic surveillance applies to the occurrence of neurological syndromes in horses in France. Neurological syndromes in horses may be connected e.g. to West Nile Virus (WNV), a zoonotic disease of growing concern for public health in Europe. A Bayesian method for spatio-temporal cluster detection of syndromes and for determining the probability of an outbreak is presented. It is shown how surveillance can be performed simultaneously for a specific class of diseases (WNV or diseases similar to WNV in terms of the information available to the system) and a non-specific class of diseases (not similar to WNV in terms of the information available to the system). We also discuss some new extensions to the spatio-temporal models and the computational algorithms involved. It is shown step-by-step how data from historical WNV outbreaks and surveillance data for neurological syndromes can be used for model construction. The model is implemented using a Gibbs sampling procedure, and its sensitivity and specificity is evaluated. Finally, it is illustrated how predictive modelling of syndromes can be useful for decision making in animal health surveillance.

Monthly variation in the probability of presence of adult Culicoides populations in nine European countries and the implications for targeted surveillance.

BACKGROUND: Biting midges of the genus Culicoides (Diptera: Ceratopogonidae) are small hematophagous insects responsible for the transmission of bluetongue virus, Schmallenberg virus and African horse sickness virus to wild and domestic ruminants and equids. Outbreaks of these viruses have caused economic damage within the European Union. The spatio-temporal distribution of biting midges is a key factor in identifying areas with the potential for disease spread. The aim of this study was to identify and map areas of neglectable adult activity for each month in an average year. Average monthly risk maps can be used as a tool when allocating resources for surveillance and control programs within Europe. METHODS: We modelled the occurrence of C. imicola and the Obsoletus and Pulicaris ensembles using existing entomological surveillance data from Spain, France, Germany, Switzerland, Austria, Denmark, Sweden, Norway and Poland. The monthly probability of each vector species and ensembles being present in Europe based on climatic and environmental input variables was estimated with the machine learning technique Random Forest. Subsequently, the monthly probability was classified into three classes: Absence, Presence and Uncertain status. These three classes are useful for mapping areas of no risk, areas of high-risk targeted for animal movement restrictions, and areas with an uncertain status that need active entomological surveillance to determine whether or not vectors are present. RESULTS: The distribution of Culicoides species ensembles were in agreement with their previously reported distribution in Europe. The Random Forest models were very accurate in predicting the probability of presence for C. imicola (mean AUC = 0.95), less accurate for the Obsoletus ensemble (mean AUC = 0.84), while the lowest accuracy was found for the Pulicaris ensemble (mean AUC = 0.71). The most important environmental variables in the models were related to temperature and precipitation for all three groups. CONCLUSIONS: The duration periods with low or null adult activity can be derived from the associated monthly distribution maps, and it was also possible to identify and map areas with uncertain predictions. In the absence of ongoing vector surveillance, these maps can be used by veterinary authorities to classify areas as likely vector-free or as likely risk areas from southern Spain to northern Sweden with acceptable precision. The maps can also focus costly entomological surveillance to seasons and areas where the predictions and vector-free status remain uncertain.

Detection probability models for bacteria, and how to obtain them from heterogeneous spiking data. An application to Bacillus anthracis.

Efficient and correct evaluation of sampling results with respect to hypotheses about the concentration or distribution of bacteria generally requires knowledge about the performance of the detection method. To assess the sensitivity of the detection method an experiment is usually performed where the target matrix is spiked (i.e. artificially contaminated) with different concentrations of the bacteria, followed by analyses of the samples using the pre-enrichment method and the analytical detection method of interest. For safety reasons or because of economic or time limits it is not always possible to perform exactly such an experiment, with the desired number of samples. In this paper, we show how heterogeneous data from diverse sources may be combined within a single model to obtain not only estimates of detection probabilities, but also, crucially, uncertainty estimates. We indicate how such results can then be used to obtain optimal conclusions about presence of bacteria, and illustrate how strongly the sampling results speak in favour of or against contamination. In our example, we consider the case when B. cereus is used as surrogate for B. anthracis, for safety reasons. The statistical modelling of the detection probabilities and of the growth characteristics of the bacteria types is based on data from four experiments where different matrices of food were spiked with B. anthracis or B. cereus and analysed using plate counts and qPCR. We show how flexible and complex Bayesian models, together with inference tools such as OpenBUGS, can be used to merge information about detection probability curves. Two different modelling approaches, differing in whether the pre-enrichment step and the PCR detection step are modelled separately or together, are applied. The relative importance on the detection curves for various existing data sets are evaluated and illustrated.

Using Bayes' rule to define the value of evidence from syndromic surveillance.

In this work we propose the adoption of a statistical framework used in the evaluation of forensic evidence as a tool for evaluating and presenting circumstantial "evidence" of a disease outbreak from syndromic surveillance. The basic idea is to exploit the predicted distributions of reported cases to calculate the ratio of the likelihood of observing n cases given an ongoing outbreak over the likelihood of observing n cases given no outbreak. The likelihood ratio defines the Value of Evidence (V). Using Bayes' rule, the prior odds for an ongoing outbreak are multiplied by V to obtain the posterior odds. This approach was applied to time series on the number of horses showing clinical respiratory symptoms or neurological symptoms. The separation between prior beliefs about the probability of an outbreak and the strength of evidence from syndromic surveillance offers a transparent reasoning process suitable for supporting decision makers. The value of evidence can be translated into a verbal statement, as often done in forensics or used for the production of risk maps. Furthermore, a Bayesian approach offers seamless integration of data from syndromic surveillance with results from predictive modeling and with information from other sources such as disease introduction risk assessments.

Organic acids for control of Salmonella in different feed materials.

BACKGROUND: Salmonella control in animal feed is important in order to protect animal and public health. Organic acids is one of the control measures used for treatment of Salmonella contaminated feed or feed ingredients. In the present study, the efficacy of formic acid (FA) and different blends of FA, propionic acid (PA) and sodium formate (SF) was investigated. Four Salmonella strains isolated from feed were assayed for their acid tolerance. Also, the effect of lower temperatures (5°C and 15°C) compared to room temperature was investigated in rape seed and soybean meal. RESULTS: The efficacy of acid treatments varied significantly between different feed materials. The strongest reduction was seen in pelleted and compound mash feed (2.5 log10 reduction) followed by rapeseed meal (1 log10 reduction) after 5 days exposure. However, in soybean meal the acid effects were limited (less than 0.5 log10 reduction) even after several weeks' exposure. In all experiments the survival curves showed a concave shape, with a fast initial death phase followed by reduction at a slower rate during the remaining time of the experiment.No difference in Salmonella reduction was observed between FA and a blend of FA and PA, whereas a commercial blend of FA and SF (Amasil) was slightly more efficacious (0.5-1 log10 reduction) than a blend of FA and PA (Luprocid) in compound mash feed. The Salmonella Infantis strain was found to be the most acid tolerant strain followed by, S. Putten, S. Senftenberg and S. Typhimurium. The tolerance of the S. Infantis strain compared with the S. Typhimurium strain was statistically significant (p<0.05). The lethal effect of FA on the S. Typhimurium strain and the S. Infantis strain was lower at 5°C and 15°C compared to room temperatures. CONCLUSIONS: Acid treatment of Salmonella in feed is a matter of reducing the number of viable bacterial cells rather than eliminating the organism. Recommendations on the use of acids for controlling Salmonella in feed should take into account the relative efficacy of acid treatment in different feed materials, the variation in acid tolerance between different Salmonella strains, and the treatment temperature.

Monte Carlo feature selection and rule-based models to predict Alzheimer's disease in mild cognitive impairment.

The objective of the present study was to evaluate a Monte Carlo feature selection (MCFS) and rough set Rosetta pipeline for generating rule-based models as a tool for comprehensive risk estimates for future Alzheimer's disease (AD) in individual patients with mild cognitive impairment (MCI). Risk estimates were generated on the basis of age, gender, Mini-Mental State Examination scores, apolipoprotein E (APOE) genotype and the cerebrospinal fluid (CSF) biomarkers total tau (T-tau), phospho-tau(181) (P-tau) and the 42 amino acid form of amyloid ß (Aß42) in two sets of longitudinally followed MCI patients (n = 217 in total). The predictive model was created in Rosetta, evaluated with the standard tenfold cross-validation approach and tested on an external set. Features were ranked and selected by the MCFS algorithm. Using the combined pipeline of MCFS and Rosetta, it was possible to predict AD among patients with MCI with an area under the receiver operating characteristics curve of 0.92. Risk estimates were produced for the individual patients and showed good correlation with actual diagnosis in cross validation, and on an external dataset from a new study. Analysis of the importance of attributes showed that the biochemical CSF markers contributed the most to the predictions, and that added value was gained by combining several biochemical markers. Despite a correlation with the biochemical markers, the genetic marker APOE ε4 did not contribute to the predictive power of the model.

Uncertainty from sampling in measurements of aflatoxins in animal feedingstuffs: application of the Eurachem/CITAC guidelines.

The duplicate method for estimating uncertainty from measurement including sampling is presented in the Eurachem/CITAC guide. The applicability of this method as a tool for verifying sampling plans for mycotoxins was assessed in three case studies with aflatoxin B(1) in animal feedingstuffs. Aspects considered included strategies for obtaining samples from contaminated lots, assumptions about distributions, approaches for statistical analysis, log(10)-transformation of test data and applicability of uncertainty estimates. The results showed that when duplicate aggregate samples are formed by interpenetrating sampling, repeated measurements from a lot can be assumed to approximately follow a normal or lognormal distribution. Due to the large variation in toxin concentration between sampling targets and sometimes very large uncertainty arising from sampling and sample preparation (U(rel) ≥ 50%), estimation of uncertainty from log(10)-transformed data was found to be a more generally applicable approach than application of robust ANOVA.

Transmission and control of Salmonella in the pig feed chain: a conceptual model.

Infected breeder pigs and contaminated feed represent potential sources of Salmonella introduction to fattening pig herds and may thereby cause human infections acquired via consumption of contaminated pork. Modelling approaches such as quantitative microbial risk assessment could improve the design of strategies for control and tracing of Salmonella in the feed chain. However, the construction of such models requires a thorough understanding of the dynamics of the feed chain, including production processes, microbial processes and transport logistics. The present article illustrates a conceptual model of Salmonella in the pig feed chain and explores the possibilities for quantitative modelling including identifying major gaps in data. Information was collected from peer-reviewed scientific journals, official documents and reports and by means of interviews with experts from authorities and the feed industry. Data on prevalence of Salmonella in different parts of the feed chain are difficult to compare as observed prevalence may be biased by variations in sampling procedures as well as limitations of the detection methods. There are almost no data on numbers of Salmonella in commodities of the feed chain, which often makes it difficult to evaluate risks, intervention strategies and sampling plans in a quantitative manner. Tracing the source of Salmonella contamination is hampered by the risk of cross-contamination as well as various mixing and partitioning events along the supply chain, which sometimes makes it impossible to trace the origin of a lot back to a batch or producer. Available information points to contaminated feed materials, animal vectors and persistent contamination of production environments as important sources of Salmonella in feed production. Technological procedures such as hydrothermal or acid treatment can be used to control Salmonella in feed production. However, a large fraction of pig feed is produced without decontamination procedures. Prevention of recontamination and control of moisture throughout the chain are thus critical factors for controlling Salmonella in feed production. To verify successful control it is necessary to have monitoring strategies able to detect low levels of Salmonella heterogeneously distributed in large volumes of feed and feed material in bulk. Experience from monitoring programs and research investigations indicates that sampling of dust and sweepings from control points along the production line is an efficient strategy to gain an indication of Salmonella contamination.