Preventing Outbreaks with Herd Immunity

When a new virus emerges public health officials are on high alert.  This is because a novel virus means no one in the population is immune to it.  No one has been vaccinated against it, and no one carries protective antibodies from a previous infection.  In short, everyone is susceptible. This was the case when COVID-19 first appeared as well as the 1918 Spanish Flu and other viruses which have caused pandemics throughout history.

In this simulation we can see that if no other protective measures are in place (like social distancing, closures, or mask wearing), starting with ten infected individuals COVID-19 can quickly run through a population of 100,000.  The epidemic size, measured by the percentage of the population infected over the course of the outbreak, is a staggering 95.52% in this example.

If you zoom in you can see that the number of actively infectious people (blue line) rises slowly at first then zooms upwards exponentially as they continue to pass along the disease.  In this simulation the basic reproduction number, abbreviated as R₀, for COVID-19 is set to three (you can click on View Settings and look under Disease Characteristics to confirm this).  This means that on average, with no other mitigating effects, every infected person infects three more people. As people recover from the infection, the number of immune individuals (orange line) increases.  Immunity follows a similar exponential growth pattern, a slow start followed by a sharp rise in numbers, before plateauing at the end of the outbreak.    

It is worth noting that if we ran this same simulation repeatedly, we would see that the epidemic size (total number of people infected) and other numbers fluctuate slightly.  This is because infections have a bit of chance associated with them.  Under most cases we cannot predict who might be coughed or sneezed on by someone who is sick, nor whether or not the virus will get into their body and cause an infection.  However, we can estimate the chances of that occurring across a population (in this case, a population of 100,000) given what we know about the virus. 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 settings where we input information about viral and population parameters. To better understand the assumptions and parameters involved in SimPandemic, read the FAQ.

Community immunity refers to the fact that if enough people in a population are immune to a disease (through vaccination, previous exposure, or other biological factors) then they protect others, who are not immune, from contracting the disease.  Community immunity, sometimes called herd immunity, works because someone who is immune to a disease cannot catch it, nor can they transmit it (pass it along).  People with immunity act as roadblocks blocking the virus’s path from one person to another.  If there are enough of these roadblocks, the virus’s progress from person-to-person is significantly slowed down or even stopped. 

In this simulation we can see the positive impact of community immunity.  This simulation starts with 30% of the population being immune to COVID-19 before the outbreak begins. Like in the first scenario, ten COVID-19 infected people are present on Day 1.  The course of the outbreak looks completely different though; the epidemic size (total number of people infected) is only 60% at the end of this simulation compared to the over 95% epidemic size we saw when there was no immunity in the population. The shape of the infected (blue) curve is shorter and wider.  This indicates that fewer people are infected at any one time, which places less burden on the hospitals, although they still exceed capacity at the height of the outbreak, and the outbreak takes place over a longer period of time but with fewer deaths.  In short, the outbreak lasts a bit longer but is not as bad. 

Community immunity is particularly important in protecting people who are in high risk categories for a disease, but cannot be effectively vaccinated. For example, infants and the elderly – both of whom have weak immune systems compared to the general population and often either cannot be vaccinated or do not develop immunity  -  as well as people, like those undergoing chemotherapy, whose immune systems are not functional.

In the previous simulation we saw that starting with 30% of the community immune to COVID-19 reduced the severity of a COVID-19 outbreak, but the outbreak still occurred. To prevent an outbreak from ever happening, which is the goal of many vaccination campaigns, you need to reach the herd immunity threshold.   The herd immunity threshold is defined as the minimum percentage of the population that must be immune to a disease in order to prevent that disease from spreading. 

A disease's herd immunity threshold depends on how easy it is to transmit. The easier a disease is to transmit, the higher the herd immunity threshold has to be. What is the herd immunity threshold for COVID-19? You can explore this question using this Sandbox. 

To get started, press the Customize Settings button.

• In Population Statistics, increase the “Percentage of immune individuals” to 50% in all demographic groups.
• Click the “Done” button
• Change the “Name for simulation” and “Description for simulation” to reflect the new scenario you are testing.
• Click the “Run Simulation!” button”

Look at the new graph and table created.  Does an outbreak still occur?  How does the total epidemic size compare to no starting immunity and 30% starting immunity?

Continue to explore different starting percentages of immune individuals.  Can you identify the herd immunity threshold?  Here are a few starting tips:

• In Population Statistics, keep increasing the “Percentage of immune individuals” by 10% each run until you narrow in on the right range for the herd immunity threshold.
• If the line for infected (blue line) is hard to see on the graph, but there are several hundred or more total infections, use the “Zoom In” button to see the line better.
• If the infected line includes a hill or mountain shape, that indicates an outbreak occurred.
• If the infected line is more than zero at the end of the simulation period, go into the Customize Settings menu and increase the “Duration for simulation” and rerun the simulation. Repeat as needed.

Read the FAQ if you have questions about how to use the Sandbox, how to save and share your work, or what the different settings mean.

Once you have figured out COVID-19’s herd immunity threshold there are many more related questions you can explore.  Here are a few to get you started.

• How close to the herd immunity threshold for COVID-19 is your community? To answer this, you’ll need to look for data about local herd immunity. SeroTracker, a database of antibody prevalence by country, is one place to start looking for data. 
• Is the herd immunity threshold disease dependent? Try modeling a different disease like polio or measles. 
• In theory, the herd immunity threshold can be overwhelmed by a large influx of infected individuals all at once. Try modeling this.  How many people does it take to overwhelm herd immunity for COVID-19?