BIOL 282 - Modeling and Simulations in Public Health

Prerequisite: MATH 101
FDR: Lab Science Distribution (SL)
Credits: 4

Where are infections spreading? How many people will be affected? What are some different ways to stop the spread of an epidemic? These are questions that all of us ask during an outbreak or emergency. In a process known as modeling, scientists analyze data using complex mathematical methods to provide answers to these and other questions during an emergency response. Models provide the foresight that can help decision-makers better prepare for the future. In this course you will learn how to develop a simple mathematical models using data. You will learn basic epidemiological concepts, computational data analysis tools and relevant mathematical techniques to integrate existing data into the model and generate relevant predictions. In an open-ended project, you and several of your classmates will develop a model and recommendation about potential public health threat. No prior programming experience required - you will learn to use Python, a popular open-source programming language and Jupyter Notebook data analysis environment, to interactively explore data.

Note(s): Laboratory course.