关于举办“植物病害流行学高级培训班”的通知

为了促进我国植物病害流行学的研究与发展，由植物病理学会病害流行学专业委员会、中国农业
大学宏观植物病理学研究室和莱阳农学院植保系植病流行室联合举办“植物病害流行学高级培训班”。
培训班于2005 年3 月15 日在山东省青岛市莱阳农学院青岛校区开班，届时聘请英国植物病害流行学
专家徐向明博士主讲“植物病害时空流行动态模型”(中文授课)，徐向明博士的授课提纲见附页。同
时邀请部分国内知名的流行学专家讲授相关内容和植物病害流行学的研究进展，并讨论植物病害流行
学的发展，欢迎国内的植病流行学工作者和研究生参加。现将培训班的有关事宜通知如下：
一、培训班开班时间及费用
培训班开班时间2005 年3 月15— 19 日共5 天，3 月14 日报到，培训班不收培训费，学员食宿
费用自理。
二、住宿
学员住宿地点为《广源发城阳宾馆》，离莱阳农学院青岛校区仅500 米。该宾馆具体地点：青岛
市城阳区明阳路202 号（在城阳区区政府北侧），离青岛流亭机场仅6 公里，距青岛火车站约30 公里。
住宿费标准： （1）标准间（2 人间） 160 元/天
（2）标准间（2 人间） 260 元/天
三、乘车路线
若乘飞机，可直接打车到城阳宾馆(车费约15 元)。
若乘火车，则市内乘车路线为：自青岛火车站乘305 路公共汽车到流亭站下车，换乘3 路、103
路、373 路到太阳城站下车，步行向东到宾馆，或换乘374 路到城阳区政府站下车，步行向北到宾馆；
或自青岛火车站乘303 路汽车到李村站下车，向北步行换乘103 路到太阳城站下车，步行向东到宾馆。
四、联系人
李保华博士 莱阳农学院植保系植病流行室， 邮编：265200
电话：0535-2922013（H）（莱阳） E-mail: baohuali@lyac.edu.cn
马占鸿博士 中国农业大学宏观植物病理学研究室， 邮编：100094
电话：010-62733008 (O)（北京） E-mail: mazh@cau.edu.cn
周益林博士 专业委员会主任，中国农业科学院植保所 邮编：100094
电话：010-62815946 (O)（北京） E-mail: yilinzhou6@yahoo.com.cn
中国植物病理学会流行学专业委员会
中国农业大学宏观植物病理学研究室
莱阳农学院植保系植病流行室
2005 年元月2 日
附件
Introduction to modelling spatio-temporal dynamics of plant disease
epidemics
Xiangming Xu
An introduction course on modelling disease development in time and space is planned for a period of 10
lectures (20 hours in total).
Lecture 1: Introduction to epidemiology and models
1.1 Introduction
· What is epidemiology
· Structure and contents of the lecture
1.2 Properties of disease measurements and their implications on data analyses and modelling
· Incidence
· Severity
· Density
1.3 Measurement agreement
· Accuracy
· Reliability
· Concordance line
1.4 Models and modelling
· What is a model
· Why modelling
· Mathematical vs. statistical model
· Empirical vs. Theoretical model
· Linear vs. nonlinear model
· Fitting models
Lecture 2: Temporal data analysis and modelling
2.1 Objectives of data analysis and modelling
2.2 Temporal (dynamic) patterns
2.3 Analytical methods
2.3.1 Data reduction
· Fitting growth curves
· AUDPC
· Multivariate-based methods
2.3.2 Comparing epidemics
· ANOVA (inc. REML)
· Cluster analysis
· Discriminant function analysis
· Fitting nested models
2.4 Fitting disease progressive curves
2.4.1 How does an epidemic occur?
· Inoculum
· Susceptible but healthy plant tissue
· Inoculum dispersal
· Contact
· Infection leading to sporulation
2.4.2 Epidemic classifications
· Monocyclic vs. polycyclic
· Primary vs. secondary infections
2.4.3 Common growth curves
· Exponential model
· Monomolecular model
· Logistic model
· Other models
Lecture 3: Temporal disease progressive curves
3.1 Properties of common growth curves
3.2 Fitting curves: linearised or nonlinear form?
3.3 Model assumptions
3.4 Model parameters in relation to disease management strategies
3.5 Comparing models
3.6 Problems with simple growth curves
· Maximum disease
· Host growth
· Epidemic components (latent, infectious and removed disease)
· Time as the independent variable
Lecture 4: Temporal analysis: Linked Differential Equations
4.1 A few key epidemiological concepts
4.2 SEIR models vs. simple growth model
4.3 Basic reproduction number (R0)
· What is R0?
· Why is R0 important?
4.4 Steady state analysis (final disease etc.)
Lecture 5: Linked differential equations: an example
5.1 Biocontrol of plant diseases
5.2 Current state in biocontrol of plant diseases
5.3 Potential use of modelling in biocontrol research
5.4 A full model
5.5 A simpler model
5.6 Steady state analysis
Lecture 6: Spatial disease gradients
6.1 Concept: dispersal and disease gradient
6.2 Type of inoculum source
6.3 Primary vs. secondary gradient
6.4 Modelling disease gradient
· Exponential model
· Power-law model
· What are the main differences between the two models?
6.5 Gradient in time
Lecture 7: Spatio-temporal dynamics
7.1 General approach
7.2 Logistic -logistic model
7.3 Logistic -power-logisitc model
7.4 Gradient in time
· Wave-like spread
· Dispersive-wave spread
7.5 Possible extensions
· Infectious disease
· Explicit spatial structure
Lecture 8: Spatial disease pattern
8.1 Why study spatial pattern?
· Aggregation
· Random
· Uniform
· Over-dispersed
8.2 Data type
· Incidence vs. density data
· Sparse vs. mapped data
8.3 Modelling clustering incidence data
· Binomial distribution
· Beta-binomial distribution
· Index of dispersion
· Intraclass correlation
8.4 Analysis of count data based on Poisson distribution
8.5 Power-law model (variance-mean ratio)
Lecture 9: Spatial disease pattern and incidence-severity relationship
9.1 Quadrat-based analysis
9.2 Analysis and modelling of mapped data
· Spatial autocorrelation
· Semivariogram analysis
· SADIE
9.3 Other models
· Time-series method
· Fit a stochastic spatial model based on the MCMC method
9.4 Incidence-severity and incidence-incidence relationships
· Why study IS and II relationships
· Empirical based approach
· Distribution-based approach
· Implications on sampling
Lecture 10: Computer simulation and disease forecasting
10.1 Why simulation?
10.2 Stochastic vs. deterministic simulation
10.3 Formulating hypothesis
10.4 Key to stochastic simulation models: simulating a random number with certain distribution properties
10.5 Strategies for disease forecasting
10.6 Empirical vs. mechanistic approach
10.7 Forecasting model structure in relation to pathogen characteristics and control strategies
10.8 Examples of disease forecasting models: apple scab and powdery mildew

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