Epidemiology Without Math

Learn the fundamentals of Epidemiology without having to worry about statistics, numbers, or calculations.

What you'll learn

Different types of study designs

The history of Epidemiology and public health

Types of biases implicit in medical research

How to infer causality from medical research

Requirements

No mathematical background is required. However, a basic high school education would be useful.

Description

In the wake of the COVID pandemic, interest in the science of Epidemiology is soaring. But if you don't have a background in medical science, you probably have a skewed impression of what it's all about. Yes, there is a lot of math and statistics built into Epidemiology. After all, it is essentially the science of population health research without the ickiness of blood, ooze, or other fluids to deal with. Our major tools are logic, reason, and statistics!However, the core concepts of Epidemiology are useful and relevant for everyone, not just researchers. It can help you read newspaper reports about research findings more fluently, for example. If you understand how the experts know what they claim to know, then you're halfway to being an expert yourself. I think it's a kind of super power. And as a result, I wish everyone could receive a minimal grounding in Epidemiology.But because a lot of people are spooked by the prospect of mathematics, I've created this course specifically to avoid all computations and calculations. I have a follow-up course with that content included, for those who are interested. But for everyone else, these nine lectures are really all you need to get a solid footing in this science. I think it's ideal for journalists, math-phobic students, and any citizen interested in adding to their intellectual arsenal.I hope you enjoy it!

Overview

Section 1: The History of Epidemiology

Lecture 1 The History of Epidemiology

Section 2: Natural History of Disease

Lecture 2 Natural History of Disease

Section 3: Overview of Basic Study Designs

Lecture 3 Overview of Basic Study Designs

Section 4: Bias

Lecture 4 Bias

Section 5: Reliability & Validity

Lecture 5 Reliability & Validity

Section 6: Experiments

Lecture 6 Experiments

Section 7: Causality

Lecture 7 Causality

Section 8: Surveillance

Lecture 8 Surveillance

Section 9: Systematic Reviews

Lecture 9 Systematic Reviews

Journalists, health sciences students who want an introduction to Epidemiology without the math, people interested in medical research.