Comparing COVID-19 Variants

As soon as a new human virus, like COVID-19, is discovered, scientists and public health officials start the process of learning as much as possible about the disease. Two of the most important things researchers want to establish are the virus's transmissibility and virulence.

Transmissibility refers to how the virus is transmitted (spread) 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 (capable of passing the infection to another person).
• The infectious period of the disease: the average length of time, usually measured in days, during which an infected individual can infect other people.
• The mode of transmission of the disease: the way the disease passes from one person to another, 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.

The 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. (See How R₀ Shapes an Epidemic to learn more about R₀ and how it varies in different communities.)

In addition to the transmissibility of a virus, scientists are interested in characterizing its virulence. 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: the likelihood that a disease patient has no symptoms, even though the patient can still infect others.
• The rate of hospitalization: the likelihood that an infected patient will be so ill that they need to be hospitalized.
• The chance of death: the likelihood that an infected patient will die of the virus.

Here, we simulate a COVID-19 outbreak with the original strain of the virus, starting with ten infected individuals. The resulting graphs and table show that if no public health mitigation measures (like social distancing, masks, or closures) are implemented, a large portion of the population is infected.

Using the "Zoom In" button, you can see that in this simulation, the number of people infected (blue line) rises quickly after the first 6-8 weeks. As the infected patients recover, the number of immune individuals (orange) also increases. In this simulation, the infection runs through over 95% of the population (total infected) in 150 days. This leads to overwhelmed hospitals with more patients needing hospitalization than the hospitals have the capacity for.

Viruses replicate (make many copies of themselves) inside infected individuals' cells. During replication, the genome of the virus is sometimes accidentally changed. Each genetic change is a virus variant. For a virus like COVID-19, which has infected millions of people, thousands of variants exist, and new ones are constantly being created.

Many of the genetic changes do not make any functional difference in the virus, but some do. Scientists and public health officials track and research the variants to understand how the changes may affect the course of an outbreak. They are most interested in whether the genetic changes in a variant will lead to changes in transmission and/or the virulence of the virus.

For COVID-19 an international classification scheme for variants has emerged:

• Variant of interest (VOI): any variant that has genetic changes that scientists think (based on their understanding of the viral genes and their functions) might change the virus's transmission or virulence.
• Variant of concern (VOC): any variant where data indicates that there is more virulence and/or more transmission.
• Variant of high concern (VOHC): any variant that fits the definition of a variant of concern and where there is clear data that either mitigation measures (like vaccination, masking etc.) are less effective or known medical treatments (like hospital measures to treat COVID-19 patients) do not work as well.

Since the original COVID-19 virus emerged, a few variants have emerged as VOIs, a handful have gone on to become VOCs, and none have been classified as VOHCs.

Starting with ten infected patients in a population of 100,000 with no previous immunity or mitigating factors, we simulate below an outbreak of the Delta variant, which was the first COVID-19 VOC. It is clear that the Delta strain was far more transmissible than the original COVID-19 strain. In the Delta variant simulation, the number of people infected (blue line) increases much sooner and at a much faster rate than in the original COVID-19 simulation (above). This in turn leads to the hospitals being even more overwhelmed. The total number of infections is also higher, reaching 99.88% of the population. Despite the higher transmission, the virulence is similar to the original COVID-19, with approximately the same number of people experiencing symptoms (purple line) and a similar number of deaths.

As mentioned above, over the course of the COVID-19 pandemic several significant variants of concern (VOCs) have emerged. If they were introduced into populations which had never seen COVID-19, how would the community fair with each variant? So far, virulence for all the variants has been similar. Transmission has not been similar though. Several of the variants have significantly higher R₀ values than the original COVID-19 strain. Use the pre-set buttons to run simulations for each variant. Compare what you see happen between variants as well as in relation to the original COVID-19 strain.

Which variant is most transmissible?

How much time would a community have to react to the appearance of a given variant compared to the original COVID-19 strain?

How would the health care system, including hospitals, be able to cope with each variant?

The difference between the graphs of the three Omicron variants is small, even though the R₀ changes from 9.0 for Omicron (BA.1) to 15.1 for Omicron (BA.2.12.1). What happens if you use Customize Settings – Disease Characteristics to reduce the Latent Period? Can you explain what's going on?

Here are a few tips for when you are carrying out the simulations:

• Run the simulation for each variant several times to get an idea of the range of outcomes. You can do this by selecting the Re-Run button. 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) which is why the outcomes of the simulation can change slightly each time you run them. To better understand the assumptions and parameters involved in SimPandemic, read the FAQ.
• Pay particular attention to the timeline of the outbreak.
• Remember that these simulations are set up under conditions where a population is not putting in place any mitigation factors (vaccinations, masking, and/or social distancing), nor does anyone in the population have immunity due to a previous COVID-19 infection. This is done so that the full effect of each variant can be directly compared to the original COVID-19 strain.
• If you would like to try to model a situation closer to what is happening right now in your own community, you can use the "Customize Settings" button to add mitigation factors, previous infections, etc.