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Quantitative Analysis in Veterinary Epidemiology
( 25 Modules )

Module #1
Introduction to Quantitative Analysis in Veterinary Epidemiology
Overview of the importance of quantitative analysis in veterinary epidemiology, course objectives, and expected outcomes
Module #2
Descriptive Statistics in Veterinary Epidemiology
Descriptive statistics, data types, and measures of central tendency and variability in veterinary epidemiological data
Module #3
Data Visualization in Veterinary Epidemiology
Introduction to data visualization principles, types of plots, and best practices for visualizing veterinary epidemiological data
Module #4
Probability Theory and Concepts
Basic probability concepts, probability rules, and conditional probability in the context of veterinary epidemiology
Module #5
Statistical Inference in Veterinary Epidemiology
Introduction to statistical inference, hypothesis testing, and confidence intervals in veterinary epidemiology
Module #6
Study Design in Veterinary Epidemiology
Types of study designs, experimental and observational studies, and sampling strategies in veterinary epidemiology
Module #7
Measures of Disease Frequency
Incidence rates, prevalence, and mortality rates in veterinary epidemiology, with examples and case studies
Module #8
Measures of Disease Association
Odds ratio, relative risk, and correlation coefficients in veterinary epidemiology, with examples and case studies
Module #9
Diagnostic Test Evaluation
Sensitivity, specificity, positive and negative predictive values, and receiver operating characteristic (ROC) curves in veterinary epidemiology
Module #10
Survival Analysis in Veterinary Epidemiology
Introduction to survival analysis, Kaplan-Meier estimates, and Cox proportional hazards model in veterinary epidemiology
Module #11
Linear Regression in Veterinary Epidemiology
Introduction to simple and multiple linear regression, model assumptions, and interpretation of coefficients in veterinary epidemiology
Module #12
Logistic Regression in Veterinary Epidemiology
Introduction to logistic regression, odds ratios, and model interpretation in veterinary epidemiology
Module #13
Generalized Linear Mixed Models (GLMMs) in Veterinary Epidemiology
Introduction to GLMMs, model specification, and interpretation of results in veterinary epidemiology
Module #14
Time Series Analysis in Veterinary Epidemiology
Introduction to time series analysis, autoregressive integrated moving average (ARIMA) models, and seasonal decomposition in veterinary epidemiology
Module #15
Spatial Analysis in Veterinary Epidemiology
Introduction to spatial analysis, spatial autocorrelation, and spatial regression in veterinary epidemiology
Module #16
Risk Analysis in Veterinary Epidemiology
Introduction to risk analysis, risk assessment, and decision analysis in veterinary epidemiology
Module #17
Machine Learning in Veterinary Epidemiology
Introduction to machine learning, supervised and unsupervised learning, and model evaluation in veterinary epidemiology
Module #18
Model Validation and Selection
Model validation techniques, model selection criteria, and avoidance of overfitting in veterinary epidemiology
Module #19
Veterinary Epidemiology Software and Tools
Overview of software and tools commonly used in veterinary epidemiology, including R, Python, and Excel
Module #20
Data Management and Quality Control
Best practices for data management, data quality control, and data cleaning in veterinary epidemiology
Module #21
Ethics and Communication in Veterinary Epidemiology
Ethical considerations, communication strategies, and reporting guidelines in veterinary epidemiology
Module #22
Case Study 1:Investigating an Outbreak
Real-world example of investigating an outbreak, with hands-on practice using epidemiological methods and software
Module #23
Case Study 2:Analyzing Surveillance Data
Real-world example of analyzing surveillance data, with hands-on practice using statistical methods and software
Module #24
Case Study 3:Evaluating a Diagnostic Test
Real-world example of evaluating a diagnostic test, with hands-on practice using statistical methods and software
Module #25
Course Wrap-Up & Conclusion
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