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Comparing COVID-19 Variants: A Real-World Look at the Effect of Mutations

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Grade Range
Group Size
1-2 students
Active Time
90 minutes
Total Time
90 minutes
Area of Science
Human Biology & Health
Pandemics – COVID-19
Genetics & Genomics
Key Concepts
epidemiology, genetic variation, replication, mutations, R naught, viruses, transmission, infection, virulence
Sandra Slutz, PhD, Science Buddies
The same outline of a coronavirus is shown in three different colors.


We hear about COVID-19 variants all the time, but what is a virus variant, how do they come about, and why do they matter? Students will explore these question and more in this lesson plan. They will use SimPandemic, a free online tool, to model what COVID-19 outbreaks look like when communities are exposed to different COVID-19 variants and understand how genetic mutations in a virus can lead to functional changes.

Learning Objectives

NGSS Alignment

This lesson helps students prepare for these Next Generation Science Standards Performance Expectations:
This lesson focuses on these aspects of NGSS Three Dimensional Learning:

Science & Engineering Practices
Developing and Using Models. Develop and/or use a model to generate data to test ideas about phenomena in natural or designed systems, including those representing inputs and outputs, and those at unobservable scales.

Using Mathematics and Computational Thinking. Use mathematical models and/or computer simulations to predict the effects of a design solution on systems and/or the interactions between systems.
Disciplinary Core Ideas
LS2.A: Interdependent Relationships in Ecosystems. Organisms, and populations of organisms, are dependent on their environmental interactions both with other living things and with nonliving factors.

LS3.B: Variation of Traits. In addition to variations that arise from sexual reproduction, genetic information can be altered because of mutations. Though rare, mutations may result in changes to the structure and function of proteins. Some changes are beneficial, others harmful, and some neutral to the organism.
Crosscutting Concepts
Cause and Effect. Cause and effect relationships may be used to predict phenomena in natural or designed systems.

Systems and System Models. Models can be used to represent systems and their interactions—such as inputs, processes, and outputs—and energy and matter flows within systems.

Structure and Function. Complex and microscopic structures and systems can be visualized, modeled, and used to describe how their function depends on the shapes, composition, and relationships among its parts, therefore complex natural structures/systems can be analyzed to determine how they function.


Background Information for Teachers

This section contains a quick review for teachers of the science and concepts covered in this lesson.

As viruses replicate within their host, mutations arise. Every genetic change is a variant of the virus which means that over time viruses have many variants. Not all genetic changes matter though. In terms of a virus's fitness (ability to survive and replicate), mutations fall into three categories.

  • Beneficial mutations: these are genetic changes which increase the virus's fitness.
  • Harmful mutations: these are genetic changes which decrease the virus's fitness.
  • Neutral mutations: these are genetic changes which do not alter the virus's fitness.

Scientists and public health officials are interested in tracking virus variants to determine which, if any, could have beneficial mutations that could have a profound effect on communities. In particular, scientists track whether or not the genetic changes in a variant also change the virus's transmissibility and virulence.

Transmissibility refers to how the virus spreads from person to person and includes information like:

  • The latent period of the disease: the average time, usually measured in days, from when a person is infected until they are infectious.
  • The infectious period of the disease: the average length of time, usually measured in days, during which an infected individual is infectious.
  • The mode of transmission of the disease: diseases can be passed from one person to another in many ways, including through direct contact with bodily fluids like blood, droplet spray from coughing or sneezing, or in the case of airborne diseases, small particles that stay suspended in the air for minutes or hours, even after the infected person has left the area.

In general, mutations which increase a virus's transmissibility are beneficial to the virus and likely to propagate.

Virulence measures the likelihood that infection leads to disease and the severity of the disease, if it occurs. Measures of virulence include information like:

  • The chance of being asymptomatic: some people infected with a virus, like COVID-19, are asymptomatic. Asymptomatic patients do not have any disease symptoms, but they can infect others.
  • The rate of hospitalization: this is the likelihood that an infected patient will be so ill that they need to be hospitalized.
  • The chance of death: this is the likelihood that an infected patient will die of the virus.

Mutations which increase a virus's virulence are bad for the human host, but could be beneficial, harmful, or neutral for the virus. A mutation that causes more symptoms for the host, like an increase in coughing, could be beneficial if it feeds back into the disease's transmissibility. But if the virus becomes so virulent that it kills the host very quickly, this may be a harmful mutation as it may mean that the host does not have enough time to come into contact with other potential hosts and spread the virus. Other mutations could alter the virulence without altering fitness and thus be neutral mutations for the virus.

The original COVID-19 virus quickly caused a pandemic largely because it was novel. Our immune systems had not seen anything like it before so there was no herd immunity nor any vaccines. The original virus was moderately transmissible. A virus's basic reproduction number or R₀ (pronounced R naught) describes its transmissibility. The original COVID-19 virus had an R₀ of 3, which meant that on average every infected individual would infect three more people. Since then, there have been a number of variants of concern (variants where data indicates that there is more virulence and/or more transmission). To date, these variants of concern (like Delta and Omicron) have had beneficial mutations resulting in increased R₀.

Students will learn all of this by using SimPandemic to model what a COVID-19 epidemic looks in a population that has not seen any strain of COVID-19. They will compare the outcomes of an outbreak of each variant and understand how genetic changes can lead to different outcomes for populations.

You will see that students obtain slightly different results, even when they have the same inputs for SimPandemic. Many events in the real world and in a simulation of the real world are based on chance. When you become infected in the real world, you often don't know when or where the infection occurred. Perhaps someone sneezed when you were randomly walking by them. A simulation cannot predict that you were going to get infected (except in special cases), but it can predict fairly well that someone would get infected. In SimPandemic there are many events where the simulation literally rolls virtual dice to determine when an infected individual will transmit the disease to another (all within the bounds specified by the input parameters). To better understand the assumptions and parameters involved, read the SimPandemic FAQ .

Important sidenote: The COVID-19 virus is an RNA virus. This means that it has an RNA genome rather than a DNA genome. Students may not be familiar with RNA genomes. For this reason, the lesson refers to the virus's "genetic material", rather than RNA, as the focus is on the genetic principles being taught and not the similarities and differences of DNA versus RNA genomes.

Prep Work (15 minutes)

Engage (15 minutes)

Explore (60 minutes)

Reflect (15 minutes)


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