Veterinary Epidemiology: Principles and Methods

About this book

Veterinary Epidemiology: Principles and Methods by S. Wayne Martin, Alan H. Meek, and Preben Willeberg explains animal health at population level. Twelve chapters connect sampling, measurement, descriptive patterns, causation, observational studies, trials, models, economics, control, monitoring, and field investigation. This guide follows the complete 1987 textbook, replacing the catalog link that previously opened only Chapter 3. Its enduring value is methodological reasoning; disease examples, diagnostic practices, and control recommendations must be understood in their historical context rather than applied as current veterinary instructions.

Chapter-by-chapter summaries

1. Epidemiologic Concepts

Epidemiology studies patterns and determinants in populations, connecting description with explanation and action. A case seen in a clinic and a population pattern answer different questions. Define the population, outcome, and purpose before choosing a method. The chapter introduces causal reasoning as a sequence rather than an immediate leap from observation to certainty. A plausible explanation still needs comparison and evidence.

2. Sampling Methods

A sample supports inference only through its relationship with the target population and selection process. Estimation, disease detection, and hypothesis testing require different planning. Distinguish sample size from representativeness: a large biased sample can remain misleading. Identify the sampling unit and possible selection errors. The chapter connects practical access to animals with the limits of the conclusion drawn from them.

3. Disease Frequency and Production

Counts become interpretable through denominators, time, and definitions. Morbidity, mortality, production measures, screening tests, and agreement describe different aspects of evidence. Distinguish incidence from prevalence and sensitivity from predictive value. Construct a small table and state precisely which population each fraction describes. The chapter shows why the same test performance can lead to different interpretations in different settings.

4. Descriptive Epidemiology

Host, environment, agents, place, and time organize patterns that generate questions. Age and temporal relationships require attention to the structure of the population. Describe what the data show before proposing a cause. Compare rates using meaningful denominators and consider changes in observation. A visual cluster or trend can suggest investigation without proving the mechanism responsible.

5. Disease Causation

Statistical association and causal explanation are not equivalent. Measures of association, confounding, bias, temporal sequence, and judgment inform interpretation. Draw a possible causal structure and identify alternative explanations. A detected organism in a diseased animal does not by itself establish its role in population disease. The chapter encourages evidence about the conditions and mechanisms through which a factor contributes.

6. Surveys and Observational Studies

Cross-sectional, case-control, and cohort approaches organize observations differently. Selection and timing determine what can be estimated and which biases threaten interpretation. Begin with the question and identify how participants enter the study. Compare the direction from exposure to outcome or outcome to prior exposure. A design name is useful only when its actual sampling and measurement procedures are understood.

7. Field Trials

Trials require a defined objective, appropriate units, comparison, allocation, measurement, and analysis. Distinguish an animal from a herd when the intervention or exposure operates at group level. Explain how randomization and masking address particular risks without assuming they solve every problem. The chapter connects practical field conditions with the need for a credible comparison of outcomes.

8. Theoretical Epidemiology

Models represent selected processes under assumptions. Their value depends on the question, parameters, and relationship with observed conditions. Separate model output from direct measurement. Change one assumption and examine whether the conclusion is robust. The chapter introduces modeling as a disciplined aid to reasoning rather than a prediction made certain by mathematics.

9. Animal Health Economics

Partial budgets, gross margins, payoff tables, and benefit-cost analysis connect health decisions with resource use. Identify the comparison and include the costs and benefits that actually change. Distinguish fixed and variable costs where relevant. An economically favorable result depends on assumptions about effectiveness, prices, and time. Keep the source’s historical monetary examples separate from present values.

10. Disease Control Concepts

Transmission pathways and population effects inform prevention and control strategies. Identify the target, mechanism, and conditions required for an approach to work. Compare reducing exposure with changing susceptibility or interrupting transmission. The chapter provides a conceptual framework; contemporary disease-specific measures require current authoritative guidance and professional assessment.

11. Monitoring Disease and Production

Ongoing records can reveal changes and support evaluation, but consistency of definition and ascertainment matters. Distinguish a true rise in disease from improved detection or altered participation. Identify who contributes data and what is missing. Monitoring produces information for decisions only when the observations remain comparable and the meaning of a signal is investigated.

12. Field Investigations

Outbreak, endemic, sporadic, observational, and ecological investigations combine earlier methods in practical problem solving. Establish a case definition, population, time sequence, and competing explanations before choosing analyses. Revise the inquiry as new evidence emerges. The final chapter integrates descriptive patterns, causal questions, study design, and resource considerations without implying that every field problem follows one rigid recipe.

Study instructions

1. Define population, outcome, time period, unit, and comparison before calculating.
2. Practise incidence, prevalence, association, and test tables with explicit denominators.
3. For each design, list selection, information, and confounding threats and how they affect interpretation.
4. Compare model and economic results under alternative assumptions.
5. Use the complete source for historical methods study and current professional guidance for actual animal-health decisions.

Questions for reflection

• Why can a larger sample remain biased?
• How do prevalence and test interpretation interact?
• What distinguishes association from causal evidence?
• How can changing surveillance alter an apparent trend?

Sources and coverage

This original study guide covers all main chapters of the linked edition, or all instructional sections where the work has no numbered chapters. The explanations and suggested practice are written for study; consult the full source for cases, worked examples, illustrations, exercises, and references. Complete 1987 textbook. Historical methodological study, not current veterinary diagnosis or treatment guidance. Read the source edition.