Preventing Outbreaks with Herd Immunity
What is herd immunity, how is it achieved, and what impact does it have on outbreaks? Students will explore these questions and more in this lesson plan. They will then use SimPandemic, a free online tool, to model different levels of viral immunity in communities to understand how a population can reach the herd immunity threshold and the impacts that has on individuals and populations during a COVID-19 outbreak.
Remote learning adaptation: This lesson plan can be conducted remotely. Students can work independently on the Explore section of the lesson plan using the Student Worksheet as a guide. The Engage and Reflect sections can either be dropped entirely, done in writing remotely, or be conducted over a video chat.
- Describe what community immunity and the herd immunity threshold are.
- Investigate how increasing community immunity lowers the effective size of an outbreak.
- Deduce the herd immunity threshold for COVID-19 and articulate its impact on a population.
NGSS AlignmentThis lesson helps students prepare for these Next Generation Science Standards Performance Expectations:
- MS-LS2-4. Construct an argument supported by empirical evidence that changes to physical or biological components of an ecosystem affect populations.
- HS-LS2-8. Evaluate evidence for the role of group behavior on individual and species' chances to survive and reproduce.
|Science & Engineering Practices||Disciplinary Core Ideas||Crosscutting Concepts|
|Science & Engineering Practices||MS
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.
Developing and Using Models. Develop, revise, and/or use a model based on evidence to illustrate and/or predict the relationships between systems or between components of a system.
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||MS
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.
LS2.D: Social Interactions and Group Behavior. Group behavior has evolved because membership can increase the chances of survival for individuals and their genetic relatives.
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.
Cause and Effect. Cause and effect relationships can be suggested and predicted for complex natural and human designed systems by examining what is known about smaller scale mechanisms within the system.
Systems and System Models. When investigating or describing a system, the boundaries and initial conditions of the system need to be defined and their inputs and outputs analyzed and described using models.
- Computer with internet connection
- Student worksheet
Background Information for TeachersThis section contains a quick review for teachers of the science and concepts covered in this lesson.
Community immunity (sometimes called herd immunity) is the concept that people who are immune to an infectious disease help break the chain of transmission of the disease, thus protecting others. This works because people who are immune cannot contract or pass along the disease, so they essentially form a roadblock in the person-to-person disease transmission.
Three scenarios are shown: 1) No one is immunized. This results in a contagious disease spreading through the population. 2) Some of the population is immunized. This results in the contagious disease spreading through some of the population. 3) Most of the population gets immunized. This results in the contagious disease being contained with little to no spread.
Figure 1. This graphic created by NCAID shows how as community immunity increases, so does a population's protection against an outbreak.
Community immunity can be achieved in several ways 1) naturally, through enough people contracting and recovering from the disease, 2) through vaccination 3) biologically, through some people simply not being susceptible to the disease, or 4) a combination of these factors. The more people in a population who are immune, the less chance a disease has of spreading and causing an outbreak. The minimum percentage of a population that needs to be immune to a disease to completely prevent its spread is called the herd immunity threshold.
The herd immunity threshold plays an important role in protecting community members who cannot be effectively vaccinated. For example, elderly people and young infants have weaker immune systems that sometimes do not create protective antibodies, even after vaccination. Similarly, people who are immunocompromised, like cancer patients undergoing chemotherapy, may have immune systems that are so weak they are not able to be given vaccines at all. Incidentally, the same people who cannot be vaccinated effectively or at all, are often the people most at risk of the worst symptoms, or even death, if infected. When immunity is at or above the herd immunity threshold, it is far less likely, statistically, that these vulnerable populations will encounter and contract the disease.
In this lesson, students will use SimPandemic to run simulations of what happens to a population of 100,000 individuals when a few COVID-19-positive individuals are present. As students increase the level of community immunity (i.e. the percent of the population who is immune to COVID-19 at the start of the simulation), they will see that the effective size of the outbreak (the total number of people infected over the course of the simulation) drops. When the community immunity is set at or above the herd immunity threshold (approximately = 66% in this COVID-19 simulation) person-to-person transmission is thwarted to the point that no outbreak occurs. Increasing immunity beyond the herd immunity threshold has little to no value.
If students explore further—on their own or guided by some of the Lesson Plan Variations listed—they will see that not all diseases have the same herd immunity threshold. Infectious diseases have a basic reproduction number (also called R-naught and abbreviated R0). The basic reproduction number quantifies, on average, how many more people someone who has the disease will infect. The higher R-naught, the higher the herd immunity threshold. In fact, the herd immunity threshold for any disease using this formula:
Students can also see what happens if they try to overwhelm herd immunity with a rapid influx of infected people. While it is theoretically possible to do so, the number of infected individuals required is quite high (often in the thousands) and is, from a practical standpoint, unlikely.
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.