Online Programs

What we'll do

The primary goals of the course are to extend students’ knowledge and understanding of mathematical models used to influence intervention and control measures in order to curb disease growth and spread. The course consists of nine units. Each unit includes a synchronous group meeting held on Zoom. Each unit also contains asynchronous activities including lessons, videos, written assignments, modeling, and projects.

What we’ll learn

• In the swiftly shifting environment of COVID-19, there are key qualms about viral forces at work and policy consequences, as well as plenty of chances for models to give the wrong idea
• We should assume that the evidence and epidemiological framework will continue to evolve, occasionally making previous modeling outcomes outdated
• Modeling is expected to persist as new policies arise, yet the indiscriminating reception of modeling outcomes will not serve public health or the area of modeling
• Vigilant assessment and evaluation of results—and juxtaposing against observed findings wherever likely—will be vital for unveiling assumptions and possible prejudices, as well as stimulating open-minded enhancement in modeling tactics

By the end of the course, you will be able to:

• Create mathematical models showing how infections run their course through a population where species mixed and mingle with each other
• Compute the basic reproductive number, R0, otherwise known as the average number of secondary infections generated by one typically infected species through the lifetime of the infection in a fully susceptible population
• Include vaccination that will aid in a kind of herd immunity situation, where individuals who can’t get vaccinated from the disease (perhaps for medical reasons) receive some protection from catching the disease
• Distinguish between models that determine the long-term behavior of the course of a disease vs. models that seek to stop the disease cold in its tracks using vaccination and treatment protocols
• Work with real data sets to compute a threshold value used to decide on whether a sample observation or experiment has statistical significance results and will form an unbiased conclusion informed by the data
• Analyze data by utilizing the platform R and R-studio to code, plot, and fit the data to a curve
• Review, summarize, and analyze peer-reviewed research papers in order to assess data, methodology, and conclusions drawn on disease behavior, and how to control it

How we'll measure learning

Each asynchronous session consists of assignments that need completion before the beginning of the next synchronous session. There are terminologies that students will be assessed on indirectly throughout. Each asynchronous task must be completed and uploaded before the next synchronous class. At the tail end of the course, students must research, build, and present their own disease model.