Using Vaccines to Fight Outbreaks

In the first seven months since it was identified in Wuhan, China (starting late December 2019), SARS-CoV-2, the virus behind COVID-19, has infected over 15 million people and killed over 600,000 world-wide. Since then, biotechnology companies and organizations around the world have scrambled to create a vaccine to address this pandemic. In this notebook we will explore how vaccines can impact outbreaks using COVID-19 as an example.

A simulation of a COVID-19 outbreak starting with ten infected individuals shows that if no public health mitigation measures (like physical distancing, masks, or closures) are implemented, a large portion of the population can be quickly infected. This in turn causes hospitals to be overrun and many deaths.

If you look carefully, you’ll see how the different lines on the graph are interdependent. As the number of infected (blue line) rises, so do the number of symptomatic (purple line) and dead (red line; you’ll need to zoom in to see the increase). Not all people who contract the disease have symptoms which is why the symptomatic curve is shorter than the infected curve. Similarly, only a small percentage of those that have symptoms die from COVID-19. The curves for both those who are symptomatic and those who die from COVID-19 start several days after the rise of the infected curve. It takes time for infected individuals to show symptoms and feel the negative effects of the virus.

The number of immune also rise as the number of infected rise. In this simulation, anyone who recovers from the virus is considered to be immune. This means they cannot get the virus again until their immunity goes away. In the real world, we do not yet know how long immunity lasts after recovery from COVID-19, but for the purpose of this simulation we have chosen to model an immunity of over a year (longer than the duration of the simulation) as another coronavirus, SARS, results in immunity for at least three years.

A dip in the economy (green line) occurs at the peak of the infection (blue line). Note that the simulation uses a simplified economic model that assumes that people with symptoms cannot work, depressing economic output. See the FAQ for more details. However, when more than 20% of the population is sick at one time, as in this scenario, this simple model underestimates the economic impact.

When the COVID-19 outbreak started in Wuhan, China, it was a novel virus. What might have happened if it wasn’t novel? What if it was a virus we already had a good vaccine for? This simulation explores that possibility.

Vaccines trick our immune systems into thinking they have been attacked by the infectious agent we want to inoculate against. Vaccines can be made in many ways including using a weakened or dead version of the real virus, proteins from the virus, or RNA or DNA to make proteins from the virus. In the case of a COVID-19 vaccine, our bodies would be tricked into thinking they have been attacked by the SARS-Cov-2 virus. The vaccine would cause the body to mount an immune reaction and create a memory of how to recognize and fight off the real virus. If the vaccine is successful, the immune system of people who have had the vaccine would be so quick and efficient at fighting off the real virus, that they would not get sick with COVID-19 or pass it on to other people. In other words, they’d be immune to COVID-19.

No vaccine works perfectly. A vaccine’s effectiveness is measured by the percentage of people who successfully become immune. The effectiveness of the measles vaccine is approximately 98%. In contrast, the yearly vaccine for the flu virus has ranged from 10% to 60% since 2010.

Even when they work, vaccines do not take effect immediately. It takes days or even weeks for the body to build the initial immune response and build the memory part of the response.

In this simulation we explore what would have happened if 90% of the population had received a COVID-19 vaccine with 90% efficiency before an outbreak of COVID-19 occurred. Since manufacturing vaccines and administering them to a large population takes a while, the simulation settings have the vaccination campaign occurring on Days 1 through 30. The simulation is set to allow up to two weeks for the vaccine to take effect. As a result of these choices, the percentage of immune individuals in the population (yellow line) can be seen increasing through Day 44. On Day 45, ten individuals become infected with COVID-19.

Of course, we did not have a vaccine for COVID-19 before the outbreak. Several months into this pandemic we are seeing major outbreaks in many countries including the United States and the race is on to develop a COVID-19 vaccine. If we do develop a good vaccine, will it still help us even though the outbreak is already underway? This simulation explores that possibility.

This simulation starts with ten COVID-19 infected individuals present on Day 1. On Day 45 a vaccination campaign starts. It takes 30 days to complete all the COVID-19 vaccinations and 14 days for the vaccine to take effect. In total, 90% of the population is vaccinated and the vaccine is 90% effective.

Compare Graph 1, an identical outbreak scenario but without a vaccine intervention, and Graph 3. Look at the height and duration of each curve. Compare the total infections and total deaths caused by the pandemic in both scenarios. At the end of each scenario, compare many people are immune. [Notice, that in SimPandemic all of the data is either given as a percentage of the population or per capita (in this case, per 100,000). This makes it a fair direct comparison. Total infected or dead is not a fair comparison if you start with wildly different populations. For example, if 17 people die in town A and 300 people die in town B you might conclude that town B’s infection rate is higher. But, if you From all of the data what is your conclusion, can an effective vaccine administered to a large percentage of the population during a pandemic help change the pandemic’s outcome?

Notice, that in SimPandemic all of the data is either given as a percentage of the population or per capita (in this case, per 100,000). This makes it simple to make meaningful direct comparisons between scenarios. Total infected or dead is not a meaningful comparison if you start with wildly different populations. For example, if you learned that 15 people were infected in town A and 300 people were infected in town B you might conclude that town B’s outbreak is worse. If you then learned that only 30 people live in town A while 3000 people live in town B you would draw a different conclusion. The outbreak in Town A infected 50% of the population which is far worse than in Town B where only 10% of the population was infected.

So far we’ve compared how a highly effective (90%) vaccine adopted by most of the population (90%) can change the outcome of an outbreak when given either before (Graph 2) or after (Graph 3) the outbreak starts. What if the COVID-19 vaccine is not highly effective? What if many people refuse to get the vaccine or cannot get it? Just how effective and/or how high does the adoption rate have to be in order for a vaccine to protect people who cannot be vaccinated? Use this Sandbox to explore all of those questions and more.

To get started, press the Customize Settings button.

• In Population Statistics, increase the “Number already infected (Adult)” from 1 to 10.
• In Vaccine, set Implement vaccine to Yes.
• In Vaccine, set “Start day” to 45, “How long does it take to vaccinate everyone?” to 30, and “How long does it take for the vaccine to become effective?” to 14 days.
• Experiment with different “Percentage vaccinated” and “Effectiveness” settings.
• To fully understand the impacts of vaccines, do not add in any other interventions until you have a good handle on the outcomes with different vaccine settings.

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.

Below is some information that can put your explorations into context.

Often the first vaccine created for a disease is not the most effective one. Effectiveness usually increases over the years as scientists continue to study, experiment, and perfect the vaccine. In June of 2020 the United States Federal Drug Administration (US FDA) released guidance to biotechnology companies that in order to be approved for use in the United States a COVID-19 vaccine would have to be at least 50% effective. In order to be approved, vaccines must be shown to have few or no severe side effects although mild side effects, like sore muscles or a short mild fever, are acceptable. To be given to the general population, the FDA requires that the benefits of the vaccine far out weight any possible problems.

Effectiveness is sometimes age dependent too. In general, the immune systems of the elderly are not as robust. This means that they sometimes do not mount a very strong protective reaction to either real viruses or vaccines. This is doubly bad as it makes it less likely that the vaccine will be effective in them and more likely that any real virus will cause them more harm. We have already seen that the elderly are more susceptible to developing difficult symptoms from COVID-19 and are more likely to die than younger patients.

Vaccinating everyone can also be very difficult. It involves producing enough vaccine, paying for it, transporting it, and administering it. Even if all of that is accomplished, not everyone is willing to get a particular vaccine. In fact, polls throughout the United States in June of 2020 show that on average approximately 50% of people say they will be willing to get a newly developed COVID-19 vaccine even after FDA approval.

Further complicating things, not everyone can be vaccinated. People who are immunocompromised, like cancer patients undergoing chemotherapy, cannot be vaccinated. The immune systems of young babies are still developing and they, like the elderly, do not always create effective antibodies. When individuals who can be vaccinated opt out, they not only increase their own risk of contracting the disease, but also increase the risk to the sub-populations who cannot be vaccinated.