The Study of Plant Disease Epidemics (Επιδημιολογία των ασθενειών των φυτών - έκδοση στα αγγλικά)

The Study of Plant Disease Epidemics

Συγγραφέας: Laurence V. Madden, Gareth Hughes, Frank van den Bosch
ISBN: 9780890543542
Σελίδες: 432
Σχήμα: 22 Χ 28
Εξώφυλλο: Σκληρό
Έτος έκδοσης: 2007


Plant disease epidemics, caused by established and invasive pathogen species, continue to impact a world increasingly concerned with the quantity and quality of its primary food supply. The Study of Plant Disease Epidemics is a comprehensive manual that introduces readers to the essential principles and concepts of plant disease epidemiology. This useful reference and textbook provides a detailed exposition on how to describe, compare, analyze, and predict epidemics of plant disease for the ultimate purposes of developing and testing control strategies and tactics.

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:

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Measuring plant disease
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Quantifying and modeling disease development in time and space
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Quantifying patterns of disease and sampling for disease in populations
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Determining decision thresholds for control interventions
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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

∂s/∂t

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 lesionsns

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