Statistical model could predict future disease outbreaks

A team from University of Georgia have teamed up to create a statistical method that may allow public health and infectious disease forecasters to better predict disease re-emergence.

In recent years, the reemergence of measles, mumps, polio, whooping cough and other vaccine-preventable diseases has sparked a refocus on emergency preparedness.

The researchers focused on “critical slowing down,” or the loss of stability that occurs in a system as a tipping point is reached. This slowing down can result from pathogen evolution, changes in contact rates of infected individuals, and declines in vaccination. All these changes may affect the spread of a disease, but they often take place gradually and without much consequence until a tipping point is crossed.

“We saw a need to improve the ways of measuring how well-controlled a disease is, which can be difficult to do in a very complex system, especially when we observe a small fraction of the true number of cases that occur,” said Eamon O’Dea, a postdoctoral researcher in Drake’s laboratory who focuses on disease ecology.

The team created a visualization that looks like a series of bowls with balls rolling in them. In the model, vaccine coverage affects the shallowness of the bowl and the speed of the ball rolling in it.

Very often, the conceptual side of science is not emphasized as much as it should be, and we were pleased to find the right visuals to help others understand the science.

If a computer model of a particular disease was sufficiently detailed and accurate, it would be possible to predict the course of an outbreak using simulation, researchers say.

“But if you don’t have a good model, as is often the case, then the statistics of critical slowing down might still give us early warning of an outbreak.”

Reference: https://www.sciencedaily.com/releases/2019/05/190521124653.htm

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