R0: How Scientists Quantify the Intensity of an Outbreak Like Coronavirus and Its Pandemic Potential
On the other hand, the influenza virus is less infectious, with its R0 ranging from 2 to 3. Influenza, therefore, does not cause the same explosive outbreaks as measles, but it persists due to its ability to mutate and evade the human immune system.
What Makes R0 Useful in Public Health?
Demographer Alfred Lotka proposed the reproduction number in the 1920s, as a measure of the rate of reproduction in a given population.
In the 1950s, epidemiologist George MacDonald suggested using it to describe the transmission potential of malaria. He proposed that, if R0 is less than 1, the disease will die out in a population, because on average an infectious person will transmit to fewer than one other susceptible person. On the other hand, if R0 is greater than 1, the disease will spread.
When public health agencies are figuring out how to deal with an outbreak, they are trying to bring R0 down to less than 1. This is tough for diseases like measles that have a high R0. It is especially challenging for measles in densely populated regions like India and China, where R0 is higher, compared to places where people are more spread out.
For the SARS pandemic in 2003, scientists estimated the original R0 to be around 2.75. A month or two later, the effective R0 dropped below 1, thanks to the tremendous effort that went into intervention strategies, including isolation and quarantine activities.
However, the pandemic continued. While on average, an infectious person transmitted to fewer than one susceptible individual, occasionally one person transmitted to tens or even hundreds of other cases. This phenomenon is called super spreading. Officials documented super spreader events a number of times during the SARS epidemic in Singapore, Hong Kong and Beijing.
R0 for Coronavirus SARS-CoV-2
A number of groups have estimated R0 for this new coronavirus. The Imperial College group has estimated R0 to be somewhere between 1.5 and 3.5. Most modeling simulations that project future cases are using R0s in that range.
These differences are not surprising; there’s uncertainty about many of the factors that go into estimating R0, such as in estimating the number of cases, especially early on in an outbreak.
Based on these current estimates, projections of the future number of cases of coronavirus are fraught with high levels of uncertainty and will likely be somewhat inaccurate.
The difficulties arise for a number of reasons.
First, the basic properties of this viral pathogen – like the infectious period – are as yet unknown.
Second, researchers don’t know how many mild cases or infections that don’t result in symptoms have been missed by surveillance but nevertheless are spreading the disease.
Third, the majority of people who come down with this new coronavirus do recover, and are likely then immune to coming down with it again. It’s unclear how the changing susceptibility of the population will affect the future spread of infection. As the virus moves into new regions and communities, it encounters people with varying health conditions that affect their susceptibility to disease, as well as different social structures, both of which affect its transmissibility.
Finally, and likely the most important reason, no one knows the future impacts of current disease control measures. Epidemiologists’ current estimates of R0 say nothing about how measures such as travel restrictions, social distancing and self-quarantine efforts will influence the virus’s continued spread.
Joseph EisenbergProfessor and Chair of Epidemiology, University of Michigan. This article is published courtesy of The Conversation.
