System can predict disease spread

Scientists have developed a new system that uses basic information about the ecology of “vector” borne diseases – malaria, Lyme disease or some of the new emerging diseases such as Avian flu – to mathematically predict how they might change, spread and pose new risks to human health.

The approach, developed by researchers from Oregon State University and the U.S. Environmental Protection Agency, could become enormously valuable to agencies that are trying to understand what a disease might do, how it may spread or how it could best be controlled.

The findings are being published in a professional journal, Transactions of the Royal Society of Tropical Medicine and Hygiene.

“Our climate is changing, we face overpopulation and overcrowding, and increasing encounters between humans and other animal species,” said Phil Rossignol, a medical entomologist and professor in the Department of Fisheries and Wildlife at OSU. “It’s clear that these demographic and ecological changes may cause significant changes and potential new risks from many diseases, but it’s been very difficult to predict just what we can expect.”

In the past, Rossignol said, the changes and spread of a disease were only clear in retrospect. Until now researchers have rarely had any type of system that could accurately suggest how a disease might react, based on changes in climatic, ecological or other conditions.

The new approach was developed by Rossignol and Jennifer Orme-Zavaleta, associate director for science at the Corvallis laboratory of the EPA.

“This actually builds upon the science of loop analysis, an engineering technique that was first developed in the 1950s,” Orme-Zavaleta said. “It combines an understanding of the ecology of a disease with a mathematical system that can predict what might happen if any part of its ecology changes, such as habitat alterations or climatic change.”

In one working example, researchers examined the links between climate and Lyme disease, which is spread to humans by ticks. Something as remote as an El Nino event in the tropical Pacific Ocean has caused increases in precipitation thousands of miles away in the eastern United States. The extra rainfall, in turn, caused an increase in acorn production by oak trees, an increase in the numbers of mice and other animals such as deer that feed on acorns, an increase in the number and life expectancy of ticks living on those animals, and ultimately an increase in the number of human cases of Lyme disease.

“In this case, our predictions with the new model closely paralleled what had actually been observed in the field,” Orme-Zavaleta said. “If you understand the community actions and the ecology of a disease, we now have a new tool to better predict how external changes will affect the spread of the disease.”

That’s not always easy, the scientists said. The model assumes knowledge of the life expectancy of the vector – an insect, rodent, etc. – that can carry the disease. One must know the ratio of the vectors to the host that gets the disease, the relative abundance of the vector, the number of “non-competent” or dead end hosts that can’t transmit the disease, and many other factors.

Ultimately, all of that “qualitative” data is used to create a “vectorial capacity” formula that can yield quantitative predictions – in other words, the growth rate of a disease.

“Predicting risk is very complicated, but at least we have a better chance now,” Rossignol said. “And the good thing about this approach is it primarily requires an understanding of the disease processes, not a lot of field data that is very expensive and time-consuming to obtain. In that sense, there’s now a much better reason to really understand how diseases are spread and what the variables are, because we have a much better way to use that knowledge.”

The OSU and EPA researchers are already working with the new system to explore a resurgence of malaria in Uzbekistan, and are developing predictions about what global climate change might mean in terms of disease spread.

The system could also be applicable to other emerging community diseases of major concern in recent years, such as West Nile Virus, SARS, monkey pox, or the newest threat that has medical researchers around the world alarmed – Avian flu.

“This new system should be useful with almost any zoonotic disease, or those types of infectious diseases that evolve or are spread by some mechanism relating to other animals in a natural setting,” Rossignol said. “Many of these diseases may have been around for quite some time, but are becoming more of a risk with the increasing contacts between crowded human populations and the natural world.”

Understanding the probable spread and potential risks of an emerging disease, the researchers said, may help significantly in targeting control efforts