The Study of Plant Disease Epidemics

Hardcover  432 pp  ISBN 9780890543542      £86.00

The authors have synthesized the research advances from the last four decades, with a special emphasis on research done in the last 15 years, to produce a useful framework for:

• Measuring plant disease
• Quantifying and modeling disease development in time and space
• Quantifying patterns of disease and sampling for disease in populations
• Determining decision thresholds for control interventions
• Characterizing the relationship between disease development and crop loss

This new reference introduces a coherent theory of disease development in plant host populations over time and space, coupled with detailed explanations of the components of diseases in crops and forests. This theory demonstrates how different levels of mathematical complexity can lead to unifying principles of disease invasion, persistence, and rates of temporal increase and disease expansion from foci. In addition, the book shows how disease control strategies are intricately related to fundamental population-biology parameters.

The information on modeling and statistical analysis provides the needed tools and procedures for researchers to help them properly measure and analyze collected epidemiological data and maximize its value. The methods and principles described throughout the book explain how to translate this valuable data and utilize it to make informed disease management decisions.

The Study of Plant Disease Epidemics is the highly anticipated original work by three of the leading plant disease epidemiologists of the last quarter century. This manual is an essential tool intended for graduate students, researchers, and teachers of plant pathology, as well as crop consultants and those in disease management positions. It will be an excellent teaching tool for courses in Plant Disease Epidemiology, Plant Disease Management, Invasive Species Risk Assessment, and Plant Pathogen Ecology.

Contents

Chapter 1: Introduction: Plant Disease Epidemics
Some Concepts Epidemics Epidemiology Epidemic versus epiphytotic Some Historical Developments Up to 1963 After 1963 Some conferences and books, starting in 1963 Final thoughts on the review of historical developments Prelude to the Rest of the Book Possible Course Outlines Suggested Readings

Chapter 2: Measuring Plant Diseases
Introduction Plant Disease Intensity Concepts Severity versus incidence: some considerations Measurement Levels and Random Variables Measurement level Random variables Plant disease variables Assessing Disease Intensity Incidence, counts, and severity: some general comments Visual assessment of disease severity Direct estimation Direct estimation with use of disease diagrams Estimation with use of disease scales Estimation with use of ordinal rating scales Random variables for severity of disease Remote-sensing and electronic assessment of disease severity Spectral signature Multispectral radiometry Image analysis Indirect measurement of severity Reliability, Accuracy, Agreement General concepts Reliability Accuracy Ordinal and binary data Improving disease measurements Attributes and Properties of the Crop Some useful static and dynamic properties Leaf area index Conclusion and Prelude to Following Chapters Suggested Readings

Chapter 3: Introduction to Modeling in Epidemiology
Introduction Models Definition and general classification Quantitative (mathematical) models - some general concepts Probability distributions Is the model linear? Methods of Model Development Fitting of Linear Models to Data Introduction Least squares regression - general concepts Distributional results Model evaluation Model adjustments Other considerations Fitting of Nonlinear Models to Data General considerations Nonlinear least squares Linearized models From nonlinear to linear Model fitting Where is the error additive? Nonlinear or linearized statistical models? Applications Disease intensity in relation to inoculum density The cumulative response Maximum Likelihood Discussions and Prelude to Later Chapters Suggested Readings

Chapter 4: Temporal Analysis I: Quantifying and Comparing Epidemics
Introduction General Concepts Notation and introduction to models Disease progress curves How Does an Epidemic Occur? Contact of inoculum with the crop host Epidemic classification Nuances of classification of epidemics Models Exponential model Monomolecular model Logistic model Some other population dynamics models Gompertz model Richards model Model comparisons Calculations with the models Control Control strategies for polycyclic diseases Calculations for polycyclic diseases Control for monocyclic diseases Summary of disease control strategies Model Fitting Choosing a model Estimating parameters and assessing model fit - linear least squares Estimating parameters-nonlinear least squares Parameter estimation-generalized linear models for disease incidence Comparing Disease Progress Curves Simple comparison of epidemics Epidemics in designed experiments Choosing a disease progress model Fitting one or more disease progress models Comparing models with different error (residual) variance- covariance structures Summary of model fitting and comparisons General repeated measures analysis Area under the disease progress curve Some other approaches Models with Maximum Disease Intensity as a Parameter General concepts Choosing a model Parameter estimation Time-Varying Rate Term Concluding Comments and Prelude to Advanced Topics Suggested Readings

Chapter 5: Temporal Analysis II: The Components of Disease
Introductions Terminology Disease Progress Models with Fixed Density A simple discrete-time model Model derivation Model simulation The threshold for epidemic development Initial disease increase Concluding remarks The H-I-R epidemic model Model derivation Model simulations The threshold for epidemic development Initial disease increase Final disease level Concluding remarks The H-L-I-R epidemic model Model derivation Model simulations The threshold for epidemic development Initial disease increase Final disease level Some concluding remarks Recapitulation of the model equations - role of latent and infectious periods The Vanderplank model Model derivation The threshold for epidemic development Initial disease increase Final disease level Concluding remarks The Kermack and McKendrick model The sporulation curve Model derivation The exponential growth rate and derived R0 The exponential growth rate for sporulation curve 5.50 Final disease level Concluding remarks Conclusions

Chapter 6: Temporal Analysis III: Advanced Topics
Introduction Models with Crop Growth Continuous crop growth Model derivation Model simulations The removed category Steady states and thresholds for epidemic development Initial disease increase Threshold of epidemic development of model equations 6.5 Concluding remarks Seasonal cropping Model derivation Model simulations Threshold for epidemic development Concluding remarks The Role of Primary Infections Model derivation Model simulations Discussion Epidemics with Vector Transmission Model derivation Model simulations Steady states and thresholds for epidemic development Some notes on disease management Concluding remarks Transitional Dynamics and Other Complexities Models considered so far More complicated models Computer simulation modeling? Stochasticity Parameter Estimation Estimating parameters without direct curve fitting Fitting models to data Suggested Readings

Chapter 7: Spatial Aspects of Epidemics - I: Pathogen Dispersal and Disease Gradients
Introduction Dispersal Gradients, Disease Gradients, and Disease Spread Concepts Inoculum sources Models Exponential Power model Power versus exponential model Contact distributions Some other dispersal models Some calculations Model Fitting Choosing a model Estimating parameters - linear methods Estimating parameters - nonlinear methods Disease Gradients - Correcting for Maximum Intensity Simple adjustment Generalizations of the exponential and power models Other models Model fitting General comments Example - graphical evaluation Example - linear regression Example - comparing parameter estimates Spatio-Temporal Dynamics of Disease Spread General comments Two spatio-temporal models Isopaths Two models Other models Analysis Disease Management Concluding Comments and a Prelude to the Following Chapters Selected Readings

Chapter 8: Spatial Aspects of Epidemics - II: A Theory of Spatio-Temporal Disease Dynamics
Introduction Large scale spread: the case of potato light blight Small scale, focus expansion Common features of spatial disease expansion Models for Spatial Populations Expansion Introduction Model derivation Rates of expansion in relation to contact distributions Gaussian contact distribution Double exponential contact distribution Root contact distribution Modified power law contact Comparisons Some Extensions One dimensional versus two dimensional epidemic expansion Continuous time and more Model and simulations Disease expansion rates - traveling waves Disease expansion rates - dispersive traveling waves Multi-seasonal epidemic expansion Disease expansion with monocyclic diseases Multiple foci and temporal dynamics An Application Concluding Remarks Selected Readings

Chapter 9: Spatial Aspects of Epidemics - III: Patterns of Plant Disease
Why We Look at Spatial Patterns Terminology Spatial Plant Disease Data Data collection Analysis of Sparsely-Sampled Incidence Data Summary statistics The binomial distribution The index of dispersion Intra-cluster correlation The beta-binomial distribution The index of dispersion revisted A power law relationship between variances How the power law is related to statistical probability distributions Unequal size sampling units Two-stage sampling Analysis of Sparsely-Sampled Count Data Summary statistics The Poisson distribution The negative binomial distribution The index of dispersion for counts Taylor€s power law Relationships between Distributions Spatial Hierarchies Disease incidence in a spatial hierarchy Counts in a spatial hierarchy Sparsely-Sampled Disease Severity Data The severity-incidence relationship - regression models The severity-incidence relationship - a mathematical model Another regression model Overview of the severity-incidence relationship Analysis of Intensively-Mapped Disease Data Join-count statistics The cross-product statistic Spatial autocorrelation Semivariance Spatial analysis by distance indices Spatial patterns and Dispersal Functions Simulation models Inference of dispersal from pattern using stochastic models Distance-Based Methods Events and intervals Neighbors The K(distance) function Conclusions Suggested Readings

Chapter 10: Estimating Plant Disease by Sampling
Why We Sample of Epidemiological Data Sampling Preliminaries Terminology Sample size Sample design Variability Population size Reliability of the estimated sample mean Simple Random Sampling for Disease Incidence Data Sample size calculations Inspection errors Exact binomial confidence intervals Simple Random Sampling for Count Data The Poisson distribution The negative binomial distribution Taylor's power law Sample size calculations Exact Poisson confidence intervals Cluster Sampling for Disease Incidence Data The binomial distribution The beta-binomial distribution The power law Sample size calculations Exact confidence intervals for cluster sampling data Regression Analysis of Disease Incidence Data Logistic regression Beta-binomial regression Logistic regression with deff-transformed data Fitting statistical probability distributions Regression Analysis of Count Data Poisson and negative binomial regression Group Testing with Incidence Data The estimator Choice of group size Sample size calculations Exact confidence intervals Group testing using generalized linear models Binomial Sampling for Count Data Binomial sampling based on probability distributions Binomial sampling based on empirical models Estimation of Disease Severity Inverse Sampling for Disease Incidence How many positives? Exact confidence intervals The geometric series Sequential Estimation of Disease Sequential estimation of disease incidence from simple random sampling Sequential estimation for count data Sequential estimation of disease incidence from cluster sampling Conclusions Suggested Readings

Chapter 11: Decision-Making in the Practice of Plant Disease Management
Decision-Making Disease Management Acceptance Sampling Preliminaries Probability and likelihood Thresholds The operating characteristic curve The binomial distribution The hypergeometric distribution Inspection errors in simple random sampling Designing a Sampling Plan with a Specified Curve Plans based on the producer's and consumer's risks Plans based on the indifference quality level Finding a sampling plan by iteration Zero Acceptance Number Sampling Plans The operating characteristic curve Sample size calculations The mailroom problem Sequential Sampling for Classification Sequential classification with simple random sampling data Sequential classification with cluster sampling data The need for simulation Risk Algorithms as a Basis for Decisions-Making Risk factors Risk algorithms The receiver operating characteristic curve Sensitivity and specificity as conditional probabilities Likelihood ratios Predicting the Need for Treatment Bayes€ theorem Predicting unusual events is problematic Conclusion Suggested Readings Chapter Twelve: Epidemics and Crop Yield Introductions Definitions and Concepts Yield Impacts of disease on crops Data and Relationships Graphs of yield and disease Obtaining data from a range of epidemics Experimental and sampling units Planned experiments Surveys Yield per unit area Expert opinion Modeling Yield in Relation to Disease Notation and general concepts Single point models Linear models Nonlinear models Model fitting Some considerations regarding the response and predictor variables in single-point (and other) models Multiple-point models Integral models Other predictor variables in empirical models An Example Analysis Mechanistic Approaches to Crop Loss Assessment General considerations based on crop physiology Radiation interception and yield Characterizing crop losses in relation to HAA and RUE Virtual lesionsnsns Type I and Type II curves Time of infection Discussion Spatial Heterogeneity General concepts Models An approximation (but a good one) Discussion and Conclusions Suggested Readings References

Index