Connect with us
[the_ad_placement id="manual-placement"]


The ‘R factor’ in predicting the spread of COVID-19



(Illustration by Viruscorona2020 - Own work + L'épidémie au 02/02/2020 - Pr G Pialoux, CC BY-SA 4.0,

If you’ve been following the news about COVID-19, you may have heard talk about the “R value” or “R factor.”

R0, pronounced “R naught” is a term used in the world of infectious diseases to indicate just how infectious a disease is.

  • If a disease has an R0 of one, it means every infected person will probably infect one person he or she encounters. At that rate, the disease is stable, but a big outbreak or an epidemic is unlikely.
  • An R0 of less than one means each infected person will infect less than one person. In that case, the disease is in decline and will eventually die out.
  • An R0 of more than one means each infected person will infect more than one person and the infection is spreading. If the R0 is three, for example, the probability is that each infected person will infect three others.

Hypothetically, the higher the R0 value, the more likely it is that a disease will spread, potentially causing an epidemic. However, every disease is different, and the R0 really only comes into play when no one in a population has been exposed to the given disease and there is no way to control the spread. That combination of circumstances is rare.

The website Healthline provides the following example:

In 1918 there was a worldwide outbreak of the swine flu (often called the Spanish flu) that killed 50 million people. According to a review article published in BMC Medicine, the R0 value of the 1918 pandemic was estimated to be between 1.4 and 2.8.

But when the swine flu, or H1N1 virus, came back in 2009, its R0 value was between 1.4 and 1.6, report researchers in the journal Science. The existence of vaccines and antiviral drugs made the 2009 outbreak much less deadly.

Researchers have estimated a range of R0 values for COVID-19. In the early days of the pandemic, the value was estimated between 2.2 and 2.7. A July study published online in the journal Emerging Infectious Diseases estimated the value to be much higher at 5.7.

What that means is potentially every infected person can infect another five to six people they contact.

But, as previously stated, R0 is conditional. One condition is the method of transmission. Another factor is the number of people one person encounters. Someone who lives in a crowded city and uses public transportation, or who works in a setting with numerous people, such as a factory assembly line, necessarily will have the opportunity to infect more people. Conversely, a person living and working on a farm in a rural community may only come into contact with his immediate family for extended periods of time. It’s unlikely that the R0 will be the same from one country to another, between states in the same country, or even between metropolitan and rural areas of the same state.

In the U.S. the website estimates R0 values range from a low of 0.83 in South Carolina to a high of 1.27 in Wyoming as of Sept. 29. Mississippi is at 0.99. COVID-19 Projections puts it as low as 0.92 for Mississippi. Both values indicate that every infected person will infect fewer than one other person. The same source puts the value at 1.05 for the U.S.

How reliable is R0 as a predictor for the future of COVID-19?

“R0 is notoriously tricky to nail down,” wrote Katarina Zimmer in “Why R0 Is Problematic for Predicting COVID-19 Spread” published in the July/August edition of The Scientist. “It depends not only on the biological characteristics of a virus—which are a mystery at the beginning of an outbreak—but also on understanding how often people come into contact with one another. Faced with uncertainty, modelers have to make assumptions about the factors that determine human movement, which can limit the precision of their models and the accuracy of the predictions they generate.”

Still, R0 is, with few exceptions, the basis for modeling the spread of infectious diseases.

The adage about modeling is that all models are wrong, but some are useful. There are rarely sure things in the world of infectious disease, but having useful information is a step in the right direction. And R0, most scientists agree, is useful, even in a limited way.

Copyright © 2021 Vicksburg Daily News.

Do NOT follow this link or you will be banned from the site!