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Emerging pests and pathogens of plants are a major threat to natural and managed ecosystems worldwide. Whilst it is well accepted that surveillance activities are key to both the early detection of new incursions and the ability to identify pest-free areas, the performance of these activities must be evaluated to ensure they are fit for purpose. This requires consideration of the number of potential hosts inspected or tested as well as the epidemiology of the pathogen and the detection method used. In the case of plant pathogens, one particular concern is whether the visual inspection of plant hosts for signs of disease is able to detect the presence of these pathogens at low prevalences, given that it takes time for these symptoms to develop. One such pathogen is the ST53 strain of the vector-borne bacterial pathogen Xylella fastidiosa in olive hosts, which was first identified in southern Italy in 2013. Additionally, X. fastidiosa ST53 in olive has a rapid rate of spread, which could also have important implications for surveillance. In the current study, we evaluate how well visual surveillance would be expected to perform for this pathogen and investigate whether molecular testing of either tree hosts or insect vectors offer feasible alternatives. Our results identify the main constraints to each of these strategies and can be used to inform and improve both current and future surveillance activities.

Early detection of invaders requires finding small numbers of individuals across large landscapes. It has been argued that the only feasible way to achieve the sampling effort needed for early detection of an invader is to involve volunteer groups (citizen scientists, passive surveyors, etc.). A key concern is that volunteers may have a considerable false-positive and false-negative rate. The question then becomes whether verification of a report from a volunteer is worth the effort. This question is the topic of this paper.We show that the maximum plausible incidence when the expert samples on its own, , and the maximum plausible incidence when the expert only verifies cases reported by the volunteer surveyor to be infected, , are related as Where θfp and θfn are the false positive and false negative rate of the volunteer surveyor, respectively. We also show that the optimal monitoring programme consists of verifying only the cases reported by the volunteer surveyor if Where TN is the cost of a sample taken by the expert and TX is the cost of an expert verifying a case reported by a volunteer surveyor.

ObjectivesTo investigate whether MTX should be combined with an additional DMARD and bridging glucocorticoids as initial treatment for patients with early RA to induce an effective long-term response.MethodsThe Care in early RA study is a two-year investigator-initiated pragmatic multicentre randomized trial. Early RA patients, naïve to DMARDs and glucocorticoids, were stratified based on prognostic factors. High-risk patients were randomized to COBRA-Classic (n = 98): MTX, sulfasalazine, prednisone step-down from 60 mg; COBRA-Slim (n = 98): MTX, prednisone step-down from 30 mg; or COBRA-Avant-Garde (n = 93): MTX, leflunomide, prednisone step-down from 30 mg. Low-risk patients were randomized to COBRA-Slim (n = 43); or Tight Step Up (TSU) (n = 47): MTX without prednisone. Clinical/radiological outcomes at year 2, sustainability of response, safety and treatment adaptations were assessed.ResultsIn the high-risk group 71/98 (72%) patients achieved a DAS28-CRP < 2.6 with COBRA-Slim compared with 64/98 (65%) with COBRA-Classic and 69/93 (74%) with COBRA-Avant-Garde (P = 1.00). Other clinical/radiological outcomes and sustainability of response were similar. COBRA-Slim treatment resulted in less therapy-related adverse events compared with COBRA-Classic (P = 0.02) or COBRA-Avant-Garde (P = 0.005). In the low-risk group, 29/43 (67%) patients on COBRA-Slim and 34/47 (72%) on TSU achieved a DAS28-CRP < 2.6 (P = 1.00). On COBRA-Slim, low-risk patients had lower longitudinal DAS28-CRP scores over 2 years, a lower need for glucocorticoid injections and a comparable safety profile compared with TSU.ConclusionAll regimens combining DMARDs with glucocorticoids were effective for patients with early RA up to 2 years. The COBRA-Slim regimen, MTX monotherapy with glucocorticoid bridging, provided the best balance between efficacy and safety, irrespective of patients’ prognosis.Trial registrationClinicalTrials.gov, http://www.clinicaltrials.gov, NCT01172639.

The emergence of Xylella fastidiosa in Europe has highlighted the importance of surveillance for protection of host plants against exotic pathogens. However, surveillance for such pathogens is resource intensive and must therefore be efficient and sustainable. Using the example of the Apulian outbreak, we consider here how best to conduct surveillance in pest-free areas which are at a high risk of infection due to spread from adjacent outbreaks. To do this, we consider where inspection and survey sites should be, which diagnostic test to use during surveillance, and whether to sample hosts or insect vectors. We identify the optimal locations of surveillance sites by linking together an epidemiological model of pathogen spread and a statistically-informed sampling model with a computational optimisation routine. This approach allows us to demonstrate the considerable differences in survey deployment in order to maximise the number of infected hosts detected compared with maximising the overall probability of detection. We go on to consider the impact of the ""detection lag"" before infected hosts become detectable on both the detection probability and the expected prevalence of infection at the time of first detection. Finally, we consider whether sampling of hosts or sampling of vectors would be expected to facilitate earlier detection, and discuss some of the challenges associated with these strategies. As well as providing specific recommendations for improving surveillance activities for X. fastidiosa in Apulia, our methods are generic and can be applied in other areas and for a range of pests and pathogens.

This paper reviews the evidence relating to the question: does the risk of fungicide resistance increase or decrease with dose? The development of fungicide resistance progresses through three key phases. During the 'emergence phase' the resistant strain has to arise through mutation and invasion. During the subsequent 'selection phase', the resistant strain is present in the pathogen population and the fraction of the pathogen population carrying the resistance increases due to the selection pressure caused by the fungicide. During the final phase of 'adjustment', the dose or choice of fungicide may need to be changed to maintain effective control over a pathogen population where resistance has developed to intermediate levels. Emergence phase: no experimental publications and only one model study report on the emergence phase, and we conclude that work in this area is needed. Selection phase: all the published experimental work, and virtually all model studies, relate to the selection phase. Seven peer reviewed and four non-peer reviewed publications report experimental evidence. All show increased selection for fungicide resistance with increased fungicide dose, except for one peer reviewed publication that does not detect any selection irrespective of dose and one conference proceedings publication which claims evidence for increased selection at a lower dose. In the mathematical models published, no evidence has been found that a lower dose could lead to a higher risk of fungicide resistance selection. We discuss areas of the dose rate debate that need further study. These include further work on pathogen-fungicide combinations where the pathogen develops partial resistance to the fungicide and work on the emergence phase.

When delivering several intravenous drug doses from a single bag, it is generally necessary to throw away any drug that remains in the bag, if the amount is insufficient to deliver the next dose. An algorithm has been developed to allow all of the drug in a bag to be used. Based on standard pharmacokinetic equations, the algorithm calculates the time when the remainder should be given, so that a desired peak serum drug concentration is achieved on the next dose. The algorithm requires as inputs the time limit on the bag, the dosing interval and the size of the dose. This algorithm applies only to drugs that obey single compartment, first-order linear kinetics (e.g. aminoglycosides), but is easily modified for other situations. In conjunction with computer-assisted infusion, use of the algorithm may potentially reduce the cost of aminoglycoside administration by reducing clinical drug waste.

PURPOSE OF REVIEW The different diseases of the spondyloarthropathy concept share common clinical characteristics. Nevertheless, the assessment of disease activity/severity in an individual patient remains difficult. Specific research is starting to focus on the development of valid, feasible and reliable outcome tools that are sensitive to change. RECENT FINDINGS No global assessment instrument is available that evaluates the whole disease spectrum in spondyloarthropathy. The majority of tools are designed to measure a specific aspect of the disease. The Assessments in Ankylosing Spondylitis international working group response criteria have been validated in multiple trials and more stringent criteria are now emerging to describe the major responses seen with biologic agents. The assessment of peripheral arthritis is still essentially borrowed from other rheumatologic conditions with some modification as to the number of joints that need to be evaluated. With regard to the assessment of specific features such as enthesitis and dactylitis, new methods are proposed that await validation. SUMMARY New assessment tools or modifications to existing tools have been described, but most still need validation and general acceptance. The debate is ongoing as to whether one should evaluate the different aspects of the spondyloarthropathies separately or an 'all-in' assessment tool should be constructed.

Pests and particularly diseases cause serious loss of yield and quality in winter oilseed rape estimated to exceed £80 million/annum in some years. These losses have occurred despite an annual expenditure of about £3.5 million for insecticides and £12 million on fungicides. Decision-making is difficult because there is complex spatial and temporal variation in pest and disease problems and improved guidance is required. The main objective of this project was to test new disease models developed in the first phase of this project and deliver a decision support system for both pest and disease control in oilseed rape. The regional light leaf spot forecast is well-established and reliable and indicates an increased risk of this disease in spring 2006. A new regional forecast for stem canker incidence preharvest has been developed and made available on the Internet. It was successful in 2004/05 and offers strategic guidance on risk provided weather factors are within the range used to develop the model. A four-stage crop-specific stem canker risk assessment method was developed that predicts the onset of phoma leaf spotting using post-harvest weather data and thermal time relationships for canker development and canker severity. Yield loss can then be calculated from canker severity and the economic impact of stem canker predicted. There is some flexibility in the timing of fungicide sprays to control stem canker. Delays of 2 to 3 weeks beyond a 10-20% plants affected threshold did not adversely affect yield. Stem canker severity and yield of different cultivars showed large variation between years and sites and smaller, but significant, variation in responses to fungicide. When phoma leaf spot appears in late autumn, it is only when plants are small that stem canker is likely to cause yield loss. In commercial crops, there were consistent trends for higher yields to be associated with higher fungicide inputs. Light leaf spot was very difficult to control with fungicides in the Aberdeen area where use of resistant cultivars is essential. The most effective disease control was obtained using a combination of resistant cultivars and fungicides. In some years, responses to fungicides were not cost-effective and targeting their use to high-risk situations is necessary to give the best margins over input costs. Close contact was maintained with potential users during the project and they influenced priorities and design features. The components of PASSWORD decision support system were tested and provide guidance for the management of invertebrate pests, phoma stem canker and light leaf spot. The system will be available to ArableDS for use in autumn 2006.

In this paper alternative organizational forms of R&D projects are analyzed. We try to develop a framework in which organizational forms, governance structures and explanatory variables are coupled. Evaluation of existing economic and organization theories results in two testable hypotheses. These hypotheses will be tested at industry level with data concerning R&D projects of U.S. biotechnology firms.

A graphical plot of disease intensity (y) versus time (t) is known as a disease progress curve (DPC). For many purposes, this is the primary depiction of an epidemic, serving to summarize the interactions of host, pathogen, and the biological and physical environment on disease development. For convenience of expression, ‘epidemic’ and ‘disease progress curve’ often are used interchangeably in this book, although, in fact, the latter is merely a graphical summary of the former biological process. Although researchers have published disease progress curves throughout the 20th century, it was Vanderplank who made the most compelling case for the preparation and analysis of disease progress curves. Vanderplank’s concepts, originally proposed four decades ago, will be brought up several times. Although considerable advances have been made in recent years, it should be pointed out here that the ideas and approaches of Vanderplank still have a large influence on plant pathologists who are trying to understand epidemics. Researchers may have many (possibly overlapping) goals in characterizing a plant disease epidemic. At one level, a researcher may only be interested in determining if two or more epidemics are different (in some sense) from each other. In this situation, the population dynamics of disease are not of direct interest; rather, one is interested in which ‘treatments’ have higher (or lower) intensities of disease or rates of disease change. At another level, a researcher may be interested in quantifying how the environment, host, and pathogen affect disease dynamics for the explicit purpose of understanding the complex interactions that influence disease development. This understanding can be used, among other things: to predict disease intensity at a particular time; to quantify the effects of control strategies on epidemics; or ultimately, to develop a theoretical basis for determining whether or not an epidemic will occur, and in the event of occurrence, to identify factors affecting the magnitude of the rate of disease increase and the final intensity of disease. Chapter topics include: Introduction General Concepts How Does an Epidemic Occur? Models Control Model Fitting Comparing Disease Progress Curves Models with Maximum Disease Intensity as a Parameter Time-Varying Rate Term

Disease spread is the result of inoculum dispersal. Dispersal is the movement of infectious units (e.g., spores) of a pathogen from one place to another or the movement of infectious units from the place they were formed to other locations. We consider the terms propagules, propagative units, and units of inoculum as being equivalent to infectious units in this chapter. Because propagative units mostly move short distances, new infections primarily occur near other infections and, therefore, there usually is higher disease intensity in some locations than others. If the inoculum initially is concentrated in one location, one can study dispersal by measuring the spore deposition on surfaces (e.g., leaves, flowers) at a range of distances from the inoculum source. Likewise, if diseased individuals are initially concentrated in a focus in one area, disease spread can be determined by measuring disease intensity at a range of distances from the initial disease source. However, if there are many inoculum sources, or diseased individuals are located at multiple locations at the start of an epidemic, then disease spread may not be easily measured (although the same biological process is occurring). This is because it will not be known where a deposited spore originated or which inoculum source produced the spore that resulted in a new diseased individual at a given location. Chapter topics include: Introduction Dispersal Gradients, Disease Gradients, and Disease Spread Models Model Fitting Disease Gradients—Correcting for Maximum Intensity Spatio-Temporal Dynamics of Disease Spread Disease Management

BACKGROUNDA new fungicide resistance risk assessment method is described, based on traits (of pathogens, fungicides and agronomic systems) that are associated with rapid or slow occurrence of resistance. Candidate traits tested for their predictive value were those for which there was a mechanistic rationale that they could be determinants of the rate of resistance evolution. RESULTSA dataset of 61 European cases of resistance against single-site-acting fungicides was assembled. For each case, the number of years from product introduction to first detection of resistance (the FDR time) was quantified-varying from 2 to 24 years. Short and long predicted FDR times represent high and low resistance risk respectively. Regression analysis identified traits that were statistically associated with FDR time. A model combining these traits explained 61% of the variation in FDR time. Validation showed that this predictive power was highly unlikely to have occurred by chance. CONCLUSIONUnlike previous methods, trait-based risk assessment can be used to assess resistance risk for fungicides with new modes of action, when there is no prior knowledge of resistance behaviour. Risk predictions using the new method provide a more reliable basis for resistance management decisions. (c) 2014 Society of Chemical Industry

Effects of regular treatments with the fungicides carbendazim and prochloraz applied to whole plots divided into subplots with different initial population mixtures of carbendazim-sensitive or carbendazim-resistant Tapesia yallundae or T acuformis were studied in successive crops of winter wheat from 1984/85 to 1999/2000. In unsprayed and carbendazim-sprayed whole plots, a stable coexistence of about 50% each of T yallundae and T acuformis developed within five seasons, but in whole plots sprayed with prochloraz or prochloraz plus carbendazim, the proportion of T acuformis increased to > 80%. A discrete time difference equation model was derived from knowledge of the biology of eyespot and competition theory to describe the population changes. The model was fitted to the data from treatments where coexistence occurred [subplots in unsprayed (1985-92) and carb endazim-sprayed (1985-89) whole plots], using nonlinear least squares regression. The optimized value of the resource overlap coefficient was small, suggesting niche differences between the two species. Populations were nearly 100% carbendazim-resistant in carbendazim-sprayed whole plots by July 1985 (one season) and in whole plots sprayed with prochloraz plus carbendazirn by July 1986 (two seasons). In prochloraz-sprayed whole plots, the proportion of carbendazim-resistant isolates decreased more rapidly than in unsprayed whole plots in the 1980s, but by July 1992 a shift in populations in unsprayed and prochloraz-sprayed whole plots towards predominantly carbendazim-resistant strains had occurred.

Plant disease epidemics involve changes in disease intensity over time and space in a host population. If the entire host population of interest is very small, it may be possible to actually measure or observe every individual (e.g., plant) for disease intensity (and other variables of interest). However, even having such a complete collection of data for disease intensity does not automatically lead to any insight into epidemic processes or help in communicating results to others. In general, one uses models (as defined below) to summarize the essential features of the observations or measurements of interest. With models, the many individual observations are reduced to a few model terms, making it much easier to visualize and ultimately to understand the phenomena being studied, as well as to communicate that understanding to others. In fact, the populations that epidemiologists are interested in are seldom very small. For instance, even when epidemics are studied in small experimental plots, where every plant (or leaf, fruit, etc.) is actually observed, the host population of interest is generally much larger, such as all plants of the same crop cultivar in commercial fields, grown under conditions similar to those studied in the small experimental plots. In other words, researchers usually want to apply results obtained with small data sets to larger populations that are not observed. Thus, models are needed here to allow inferences to be made about these large populations based on more limited direct information. Chapter topics include: Introduction Models Methods of Model Development Fitting of Linear Models to Data Fitting of Nonlinear Models to Data Applications Maximum Likelihood

When plant pathologists look at patterns of pathogens, disease, vectors or other components of pathosystems, it is the outcomes of dispersal processes and the effects of environmental heterogeneity that are being observed. Although it is not usually a good idea to draw conclusions about processes on the basis of outcomes, it is nevertheless true that one of the reasons why so much attention has been devoted to the study of patterns of plant disease is that these patterns are regarded as realizations of underlying dispersal processes of pathogens. Thus, in one of the first epidemiological studies of field-scale spatial patterns of disease, Bald monitored the incidence of tomato plants showing symptoms of infection with Tomato spotted wilt virus (TWSV). A program of intensive mapping of diseased plants in field plots was undertaken, with the objective of conducting: “…an ecological survey of incidence, to find out something about the natural dissemination of spotted wilt.” The first step in understanding ecological processes is to identify patterns. However, when the objective of an analysis of spatial pattern is an explanation of the underlying processes, statistical methods alone may not suffice. Instead, such methods may be used to formulate biological hypotheses that can be investigated by non-statistical means. For example, both Bald and Cochran, discussing the same TSWV data set, offered biological interpretations of their statistical analyses of pattern in terms of the behavioral ecology of the vector of the virus. Here, we distinguish between such studies, carried out primarily with the objective of finding a biologically based explanation for observed spatial patterns of disease, and studies in which the primary objective is to make statements about disease intensity, where the pattern of disease is of interest in that it affects the sampling distribution of the intensity estimator. Methods for both types of study are discussed in this chapter. Chapter topics include: Why We Look at Spatial Patterns Terminology Spatial Plant Disease Data Analysis of Sparsely Sampled Incidence Data Analysis of Sparsely Sampled Count Data Relationships between Distributions Spatial Hierarchies Sparsely Sampled Disease Severity Data Analysis of Intensively Mapped Disease Data Spatial Patterns and Dispersal Functions Distance-Based Methods

In the practice of disease management, the challenges faced by decision makers are many and various. We outline two approaches to evidence-based decision making that have application in disease management. First, sampling as a basis for decision making is discussed. The purpose of sampling is to gather information on disease intensity. This information is the evidence on which a decision about the need for some appropriate control action can be based. Sampling a crop for the purpose of deciding whether or not treatment is required entails classification of observed disease intensity in relation to some pre-specified critical intensity (threshold). This is not the same as sampling a crop for the purpose of estimating disease intensity. The theory of sampling for the purpose of classification has been developed most extensively in the area of statistical quality control. In this chapter, we concentrate our discussion on methodology for sampling disease incidence data, because sampling for counts is already so widely discussed in the economic entomology literature. Chapter topics include: Decision Making in Disease Management Acceptance Sampling Preliminaries Designing a Sampling Plan with a Specified Curve Zero Acceptance Number Sampling Plans Sequential Sampling for Classification Risk Algorithms as a Basis for Decision Making Predicting the Need for Treatment

In this chapter we will model the mechanisms at the level of the individual infection process, including latent period, infectious period, formation of daughter infections, etc., and study what type of dynamics result in the population of plants. Thus, the primary goal of Chapters 5 is not to choose equations to fit data, per se, but to determine the implications of basic biological processes on the population dynamics of disease. We first develop a series of disease progress models, from very simple caricatures of epidemics, to more complex and realistic ones. Based on the analysis of these models, we discuss when and why epidemics will develop, how fast they will increase and what levels of severity (or incidence) they will reach or approach. This chapter deals with models for polycyclic diseases with many disease cycles during an epidemic. Such epidemics are generally initiated with a very low level of infection. Chapter topics include: Introduction Disease Progress Models with Fixed Host Density

In this chapter, we study the spatio-temporal dynamics of plant disease epidemics using a more mechanistic modeling approach. The chapter starts with two examples of the expansion of plant diseases, and based on these examples, some key questions will be posed. These questions will be answered using a model for the spatial expansion of plant diseases. Through the developed models, we expand the overall understanding of epidemics. Finally, we then show an example of how to apply the derived models to evaluate a disease control strategy. Chapter topics include: Introduction Models for Spatial Population Expansion Extensions An Application

There are multiple uses for information on crop yield in relation to disease intensity. For instance, in addition to determining if a (potential) disease control method reduces disease intensity or the rate of disease increase, one can determine if the control has an effect on yield. In an extreme case, it is possible that a control reduces disease severity but does not change crop yield very much. Related to this, one can attempt to make optimal disease management decisions based on the cost of the controls and the benefit obtained from the controls (reduced yield loss and, thus higher income). One can also use this information to make predictions of yield for a given location or region, which is of benefit to crop producers and the individuals and groups who purchase the commodities. The relationship between yield and disease can furthermore be used to determine the economic importance of a given disease in a given region. Through risk assessment, one can use the information to assess the threat of a non-indigenous pathogen to a region or country. In this chapter we discuss general concepts of crop losses and the principles of data collection, followed by a presentation of the more empirical approaches to modeling that have been very popular over the last few decades. In the spirit of model parsimony, we show that a considerable understanding of crop losses can be obtained based on relatively simple models of yield in relation to epidemic and crop variables. In particular, we demonstrate how spatial heterogeneity of disease affects observed disease:yield relationships, and how temporal dynamics of disease increase can be linked to crop yield production. Chapter topics include: Introduction Definitions and Concepts Data and Relationships Modeling Yield in Relation to Disease An Example Analysis Mechanistic Approaches to Crop Loss Assessment Spatial Heterogeneity

BACKGROUNDIn the European Union, assessments of resistance risk are required by the regulatory authorities for each fungicide product and are used to guide the extent of anti-resistance strategies. This paper reports an evaluation of a widely used risk matrix', to determine its predictive value. Sixty-seven unique cases of fungicide resistance in Europe were identified for testing the risk assessment scheme, where each case was the first occurrence of resistance in a pathogen species against a fungicide group. RESULTSIn most cases, high-, moderate- and low-risk categories for fungicide, pathogen and agronomic systems were each associated with significant differences in the number of years from fungicide introduction to the first detection of resistance (FDR time). The combined risk, calculated by multiplying the individual risk factors using the risk matrix, had useful predictive power (72.8% of FDR time variance accounted for; VAF) for all fungicides, but only limited predictive power (25.8% VAF) for single-site acting fungicides (the predominant type). CONCLUSIONThe resistance risk matrix has significant, but limited, predictive value. New fungicide modes of action, or pathogens that have become newly prevalent, cannot be assigned to risk categories until new methods of resistance risk assessment are developed. (c) 2013 Society of Chemical Industry

An epidemic consists of a population of infected individuals in a (generally larger) host population, and the change in diseased individuals over time and space. Before one can understand or compare epidemics, one must first monitor them. We use the Merriam-Webster dictionary and define monitor as: “to watch, keep track of, or check usually for a special purpose.” For the purpose of epidemic analysis, this involves sampling for disease, and measurement of disease in the sample. Measurement is the primary subject of this chapter. Because plant disease involves the interaction of a plant and a pathogen as influenced by the environment, it is natural to consider measurements of plants (e.g., crops, forest trees), pathogens (e.g., spores), and the physical environment (e.g., ambient air temperature) when quantifying epidemics. Depending on the objectives of the study, an investigator may need to quantify variables explicitly representing one or more components of the disease triangle. Different approaches to the measurement of disease may be appropriate for different pathosystems. For instance, measuring a systemic disease caused by a virus may be very different from measuring the area of lesions caused by a fungus on leaves. There are, however, general principles and approaches that apply for all types of measurements, and these are discussed in this chapter. We start with an overview of concepts, and give relevant definitions and explanations of disease, measurement, and some statistical terms. Then, we present a review of common disease measurement methods, and discuss ways to determine the so-called reliability and accuracy of the disease measurements. Chapter topics include: Introduction Plant Disease Intensity Measurement Levels and Random Variables Assessing Disease Intensity Reliability, Accuracy, Agreement Attributes and Properties of the Crop

Sampling provides the data that link epidemiological theory to the solution of problems involving plant disease in fields, forests, and elsewhere. Without sampling, there are no data, and without data, our theories can only provide us with qualitative pictures of the dynamics of plant disease in time and space. Such qualitative pictures are useful in providing a strategic overview, but the extent to which this can contribute to practical problem-solving is limited if it is not eventually accompanied by quantitative data. At the outset we need to specify whether we are sampling to estimate the value of a population parameter, such as mean disease incidence, or to classify a population relative to a threshold level of some epidemiological significance. This chapter is concerned with various aspects of sampling for estimation. Sampling for classification in the context of crop protection decision making has been advanced by recent research in economic entomology. Chapter topics include: Why We Sample for Epidemiological Data Sampling Preliminaries Simple Random Sampling for Disease Incidence Data Simple Random Sampling for Count Data Cluster Sampling for Disease Incidence Data Regression Analysis of Disease Incidence Data Regression Analysis of Count Data Group Testing with Incidence Data Binomial Sampling for Count Data Estimation of Disease Severity Inverse Sampling for Disease Incidence Sequential Estimation of Disease Conclusions

The effect of temperature on latent period and aeciospore production of Puccinia lagenophorae on Senecio vulgaris was determined in small-scale experiments under controlled conditions. A clear effect of temperature on latent period was demonstrated. Latent period decreased exponentially with increasing temperature. Both total aeciospore production and net reproductive number increased linearly with increasing temperature in a range from 10 to 22 degreesC. The three parameters were incorporated in models to determine the effect of temperature on epidemic development. The present study suggests an increase in the exponential growth rate, r, and the velocity of focus expansion, V, with temperature. This increase in epidemic development was caused mainly by the effect of temperature on latent period and on net reproductive number. The effect of temperature on the sporulation curve appeared to be less important.

In this chapter we consider additional complexities of epidemics that are important in some circumstances. We introduce crop growth into the models. We will introduce two main categories of crop growth scenarios. This serves as an introduction to the many possible types of disease dynamics that can occur when the host population “size” changes. Then, we consider in more detail the primary infections that initiate an epidemic. We will discuss how disease dynamics change when these primary infections occur over the duration of the epidemic rather than just at the start. A special case of the model is the one for a monocyclic disease. We will then use the insight gained from the previous sections to study plant diseases caused by viruses. In particular, we deal with the situation in which the pathogen is transmitted from infected plants to healthy plants by the feeding activity of vectors, usually herbivorous insect species (such as aphids and leafhoppers). We will study the consequences of vectored transmission on the development of epidemics, and use this system to demonstrate how such models can be utilized to evaluate disease control strategies. We will discuss situations where insight into epidemics depends on more than the use of mathematical analysis, and results are obtainable only by the use of computer programs. Finally, we conclude the chapter by discussing ways of fitting some of the differential-equation models directly (or indirectly) to data. Chapter topics include: Introduction Models with Crop Growth The Role of Primary Infection Epidemics with Vector Transmission Transitional Dynamics and Other Complexities Parameter Estimation

A mathematical model was derived to predict selection for fungicide resistance in foliar pathogens of cereal crops. The model was tested against independent data from four field experiments quantifying selection for the G143A mutation conferring resistance to a quinone outside inhibitor (QoI) fungicide in powdery mildew (Blumeria graminis f.sp. hordei) on spring barley (Hordeum vulgare). Fungicide treatments with azoxystrobin differed in the total applied dose and spray number. For each treatment, we calculated the observed selection ratio as the ratio of the frequency of the resistant strain after the last and before the first spray. The model accurately predicted the variation in observed selection ratios with total applied fungicide dose and number of sprays for three of the four experiments. Underprediction of selection ratios in one experiment was attributed to the particularly late epidemic onset in that experiment. When the equation representing epidemic development was modified to account for the late epidemic, predicted and observed selection ratios at that site were in close agreement. On a scatter plot of observed selection ratios on predicted selection ratios, for all four experiments, the 1:1 line explained 89-92% of the variance in the mean of observed selection ratios. To our knowledge, this is the first fungicide resistance model for plant pathogens to be rigorously tested against field data. The model can be used with some degree of confidence, to identify anti-resistance treatment strategies which are likely to be effective and would justify the resources required for experimental testing.

This chapter focuses on the complexity of innovation strategies and the distinction between incremental and radical innovation in particular. It argues that the type of innovation asks for other types of knowledge to be created and shared and also focuses on the concepts of related and unrelated knowledge. The chapter suggests that certain knowledge management practices improve incremental product innovation performance, while others are more appropriate for radical product innovation. It also argues that the type of knowledge that is transferred and created plays a determining role in stimulating incremental versus radical innovation. Crucial to innovation and the subsequent development of sustainable competitive advantage is the organization’s ability to create and transfer knowledge. Tacit knowledge is considered to be the crucial ingredient of innovation and a source of sustained competitive advantage because it is unique, valuable, scarce and inimitable, and can only be acquired and exchanged through experience and interaction with others.

We investigate the correlation of star formation quenching with internal galaxy properties and environment by comparing observation and theoretical models. We first classify galaxies as the most massive “central” or “satellite” in each halo in order to investigate the environmental effect. For observed central galaxies, we are unable to determine whether star formation quenching is primarily connected with halo mass or stellar mass, because these two quantities are strongly correlated. For satellite galaxies, a nearly equal dependence on halo mass and stellar mass is seen. We find that theoretical models with AGN feedback reproduce the dependence on colours and specific star formation rates for central galaxies reasonably. However, the same models seriously fail to reproduce the star formation rates of satellite galaxies by over-quenching star formation via strong strangulation: satellite over-quenching problem .

NTRODUCTION:The Assessments of SpondyloArthritis international society Health Index (ASAS HI) measures functioning and health in patients with spondyloarthritis (SpA) across 17 aspects of health and 9 environmental factors (EF). The objective was to translate and adapt the original English version of the ASAS HI, including the EF Item Set, cross-culturally into 15 languages.METHODS:Translation and cross-cultural adaptation has been carried out following the forward-backward procedure. In the cognitive debriefing, 10 patients/country across a broad spectrum of sociodemographic background, were included.RESULTS:The ASAS HI and the EF Item Set were translated into Arabic, Chinese, Croatian, Dutch, French, German, Greek, Hungarian, Italian, Korean, Portuguese, Russian, Spanish, Thai and Turkish. Some difficulties were experienced with translation of the contextual factors indicating that these concepts may be more culturally-dependent. A total of 215 patients with axial SpA across 23 countries (62.3% men, mean (SD) age 42.4 (13.9) years) participated in the field test. Cognitive debriefing showed that items of the ASAS HI and EF Item Set are clear, relevant and comprehensive. All versions were accepted with minor modifications with respect to item wording and response option. The wording of three items had to be adapted to improve clarity. As a result of cognitive debriefing, a new response option 'not applicable' was added to two items of the ASAS HI to improve appropriateness.DISCUSSION:This study showed that the items of the ASAS HI including the EFs were readily adaptable throughout all countries, indicating that the concepts covered were comprehensive, clear and meaningful in different cultures.

We investigate the correlation of star formation quenching with internal galaxy properties and large scale environment (halo mass) in empirical data and theoretical models. We make use of the halo-based Group Catalog of Yang and collaborators, which is based on the Sloan Digital Sky Survey. Data from the Galaxy Evolution Explorer (GALEX) are also used to extract the recent star formation rate. In order to investigate the environmental effects, we examine the properties of "central" and "satellite" galaxies separately. For central galaxies, we are unable to conclude whether star formation quenching is primarily connected with halo mass or stellar mass, because these two quantities are themselves strongly correlated. For satellite galaxies, a nearly equally strong dependence on halo mass and stellar mass is seen. We make the same comparison for five different semi-analytic models based on three independently developed codes. We find that the models with AGN feedback reproduce reasonably well the dependence of the fraction of central red and passive galaxies on halo mass and stellar mass. However, for satellite galaxies, the same models badly overproduce the fraction of red/passive galaxies and do not reproduce the empirical trends with stellar mass or halo mass. This {\em satellite overquenching problem} is caused by the too-rapid stripping of the satellites' hot gas halos, which leads to rapid strangulation of star formation., Comment: 18 pages, 11 figures, Accepted for publication in MNRAS