When an infectious disease outbreak occurs, public health authorities are keen to determine its R0 value: the reproductive number of the pathogen, or how many people will become infected from contact with one individual.

Yet with SARS and other respiratory diseases, it turns out that the R0 value tells only part of the story about a disease’s spread. If Ris less than one—meaning that, on average, most people aren’t infecting anyone else—the disease will eventually die out on its own. If Ris more than one—on average, each case leads to more cases—public health authorities must contend with an epidemic.



Beyond the R0 Value

Transmission rates were once assumed to approximate a bell curve. However, some super-spreaders can be linked to dozens of cases, while the majority infected few or none. The phenomenon of super-spreading is as important to epidemiologists as the overall R0.


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Case Study: Beijing SARS Outbreak 2003
A woman entered a Beijing hospital in February 2003 to be treated for complications of diabetes. No one knows of any contact she had with a SARS patient, but the hospital did treat someone with SARS in March. On April 5, the diabetic woman developed a fever, headache and fluid on her lungs. On April 12, the day she died, eight of her relatives were diagnosed with probable SARS. She infected 33 of 74 people who had contact with her in the hospital; the chain of transmission ultimately included 77 SARS cases and 15 deaths.

Source and transmission of SARS cases

Beijing 2003

How to Stop the Spread?
Does this mean we should be on the hunt for modern-day Typhoid Marys?

Targeting infection control strategies—such as isolation or administering scarce vaccines—on the people who are most likely to spread disease would be an optimal way to halt an outbreak. But that raises both practical and ethical issues.