This course will illustrate epidemiological concepts that we are exposed to in everyday life, and help you interpret these concepts for your own understanding of current events related to diseases in animals and people.

What you will learn:
- Basic understanding of how epidemiology touches our daily lives
- What data are needed for informed interpretation of disease spread
- The difference between association, correlation and causality
- The importance of visualization of data

The course consists of seven modules that are based on the most relevant and frequently occurring examples of public health and veterinary public health diseases. Real-life examples will be used to illustrate and explain theoretical concepts of epidemiology.

This course is framed in and embraces the One Health concept approaching diseases in animals and in people in equal measure: for every example in human health used to illustrate an epidemiological concept, an example in animal health is used. Participants are able to draw parallels between human and animal diseases recognizing how epidemiology for animals is very much the same as epidemiology for people. For example, an epidemiological task force on SARS-CoV-2 requires an understanding of how the virus behaves among animals as well as how the disease behaves among people in order to control it.

The format includes lecture slides with voiceover audio, video sequences, excel sheets, and webpages with self-assessment quizzes.

Certificates of completion will be available for download for the successfully completed course.

Module 1: Introduction
A brief history of epidemiology and how epidemiological concepts are used in the media today

Module 2: Case Definition and Population at Risk
The importance of using case definitions and the population at risk for disease prevention and control.

Module 3: Measures of Disease
Commonly used measures of disease in the media illustrated with real disease examples.

Module 4: Spread of Disease
Measures of disease spread using real disease examples.

Module 5: Association, Correlation and Causality
Detailed examination of the fundamental differences between these concepts and their influence on the interpretation of data.

Module 6: Bias, Missing Data, Confounding Variables
Understanding how missing data, variables not taken into consideration, and author biases can influence the interpretation of data.

Module 7: Visualization
How the data is visualized is crucial to interpretation. Visualization must be appropriate to the data and the conclusions.