Tracking the Spread of Deadly Diseases

By Amber Hsiao | Wednesday, December 7, 2005

A team led by UC Berkeley researchers found that a single, infectious individual is less likely than previously thought to be capable of starting an epidemic. Disease outbreaks are much more likely and faster at propagating throughout the population if a group of individuals has become infected with the disease.

Eight deadly diseases were tracked and analyzed to determine how and to what extent disease is propagated in the population. Disease outbreaks that stem from groups and spread quickly were termed "superspreading events."

Such events often lead to epidemics, such as Severe Acute Respiratory Syndrome (SARS) or measles. The team defined a superspreading event having a threshold number of other people infected as a result of any one infected individual.

"A superspreading even occurs when conditions are just right, like in a perfect storm, for one individual to transmit a disease to a much larger group of individuals than one would expect purely by chance," said Wayne Getz, principal investigator and UC Berkeley professor of environmental science, policy and management. "A good example is an individual with a highly infectious disease taking a long ride in a crowded bus or train, or a sick child in a crowded school room."

Superspreading events occur for a number of reasons. Infected individuals who are in crowded spaces-such as hospitals, schools, and airplanes-put others at high risk for infection. Air transmission and misdiagnosed cases by physicians also lead to higher rates of transmission, since the disease continues to spread for a longer period of time without treatment.

"We analyzed detailed epidemiological records describing how infections were transmitted during particular outbreaks, or else how many cases were caused in many separate introductions of a disease," said James Lloyd-Smith, lead author of the paper and UC Berkeley postdoctoral researcher of environmental science, policy and management. "Using these data, we plotted histograms of how many secondary cases each infected person caused, for each disease."

Though the research has helped better understand disease transmission, researchers still hope to verify their data by studying other diseases.

Researchers were able to understand the individual variation of infectiousness in various diseases. Disease transmission is determined by the duration that an individual is contagious, in addition to calculating the rate at which this individual contacts others. The probability that transmission occurs is then determined, and often depends on the type of contact required for transmission.

A stochastic epidemic model was used to analyze data. Stochastic refers to a model that allows researchers in take into account unpredictable events that are a result chance. This is important, since chance is a variable in the early stages of a disease outbreak.

"This model is what allowed us to understand how variation influences disease extinction probabilities and epidemic growth rates," said Lloyd-Smith. "We also extended the model to think about how disease outbreaks can be controlled."

Researchers found two main conclusions to explain disease control. The first method involves identifying highly infectious people followed by targeted control measures to stop outbreaks. The second involves comparing control measures that target the entire population versus selected individuals.

Measures put in place with regards to the entire population include asking individuals to reduce the number of people they interact with, while individual-focused measures include isolating infected individuals to receive treatment.

"For situations where the two types of measures have the same impact on population-average infectiousness, we found that individual-specific measures were always more likely to stop an outbreak," said Lloyd-Smith.

The importance of this variation among diseases was not quantified until now.

"Earlier models may not have captured the way outbreaks really grow, and the chance that they'll die out by chance," said Lloyd-Smith.

Though the research has helped better understand disease transmission, researchers still hope to verify their data by studying other diseases.

"The really important challenge is figuring out what factors make some people more infectious than others, and how we can identify potential superspreaders in advance," said Lloyd-Smith. "The next big steps to be taken are figuring out what are the predictors of infectiousness, and how they can be measured and used in practice."

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Originally appeared in the Daily Californian, Science-Technology section. See the original article.