Click to enlargeThe Study of Plant Disease Epidemics

By Laurence V. Madden, Gareth Hughes, and Frank van den Bosch

“…provides a substantial amount of information…well-written and readable, quite pleasing considering the complexity of the subject…should be available in all college, university, research laboratories and institutional libraries where there are courses and research in plant pathology.”
-- Fungal Diversity

“Without any question this is an excellent treatise concerning such an important subject as evaluation of occurrence of plant pathogens and estimation of economic losses they cause in crop production. The authors and the APS Press merit congratulations on providing the researchers and practitioners such an excellent treatise concerning plant protection.”
-- Journal of Plant Protection Research

"The Study of Plant Disease Epidemics clearly and cohesively synthesises many aspects of plant disease epidemiology from the principles of disease intensity assessment to the various modeling approaches to crop loss assessment. Its tone is instructional and would be of benefit to plant pathologists wishing to augment their skills in plant disease epidemiology, or for teachers of graduate/advanced level classes in plant disease epidemiology, biological systems modeling, and disease management." Click here to read the full review.
-- Australasian Plant Pathology

Click here for more reviews.

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:

  • 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.

“This is an excellent book suitable for specialists in plant disease epidemics at every level. Nematologists would benefit form knowing how plant pathologists approach the study of disease epidemics and how their approach could also be used in nematology especially with nematodes that multiply continuously and have a rather short life cycle.”
-- Nematologia Mediterranea


“…its completeness and accessibility make it very well suited as a reference book. It is a great companion to understanding the dense and abundant epidemiology literature. Probably every plant pathologist should have this text, and any mycologist who wants to keep a food in the world of plant epidemiology would find this book quite useful.”
-- Inoculum



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 lesionsnsns

            Type I and Type II curveses

            Time of infection

            Discussion

Spatial Heterogeneity

            General concepts

            Models

            An approximation (but a good one)

Discussion and Conclusions

Suggested Readings
 

References

 

Index

 


2007; 8½” X 11” hardcover; 432 pages; 170 black and white illustrations; (3 lbs.); ISBN 978-089054-354-2; Item No. 43542


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