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Web Exclusives: Diseases

Computing the Contagious
Allison MacLachlan
Posted September 9, 2011

Dengue fever, tuberculosis and H1N1 influenza are tough competitors to face in a fight. Each can infect large populations in a matter of days, making public health responses difficult to target and implement in time. Bacteria and viruses have sneezes, coughs and quickly replicating DNA or RNA in their corner.

Scientists who study these diseases also have a powerful tool at their fingertips. Using computers, they can model how infectious diseases spread and conduct virtual test runs of how different interventions might help.

Three scientists are launching new projects under NIH’s Models of Infectious Disease Agent Study (MIDAS) that enlist the help of computers to identify strategies to keep contagious diseases under control.

Here’s a quick look at each project:

»  Modeling Vector-Borne Viruses
»  Information for Interventions
»  Quantifying the Uncertain

Modeling Vector-Borne Viruses

Christopher Mores, Louisiana State University

Image of mosquito
Urbanization is increasing the spread of mosquito-borne dengue fever.

In the year 2030, 60 percent of the world’s population will live in cities—a four-fold increase from a century ago. One problem that comes with this shift to denser populations is a rise in the risk of infectious disease, points out virologist Christopher Mores. Increased global travel, for instance, means viruses can easily land on new continents and infect unexposed populations.

A higher risk of infectious disease means more work for Mores. At Louisiana State University, he conducts research on the viruses that bloodsucking arthropods like mosquitoes and ticks—called vectors—spread to human hosts.

His current work focuses on the mosquito-borne dengue virus. Every year, 50 to 100 million people catch dengue fever, which presents like a severe flu and can sometimes be deadly. There is no specific medication to treat a dengue infection and no vaccine, so stopping transmission is the best way to avoid epidemics. Mores will use mathematical modeling to show how the disease spreads through places in Colombia, Puerto Rico and elsewhere, which will help inform how it might behave on the U.S. mainland.

“The entire Gulf coast is at potential risk from dengue,” says Mores. “Our borders are constantly being challenged by this introduced virus.”

Mores’s work will build on previous models to take a more detailed look at how modern organization—from houses and communities to nations and continents—and patterns of human movement affect the transmission of dengue and other vector-borne viruses.

The project also involves modeling how different interventions might help slow or prevent the spread of disease. Mores aims for a user-friendly model that will be valuable to public health officials.

Information for Interventions

Travis Porco, University of California, San Francisco

Map of San Francisco Bay Area
Porco's project will model tuberculosis outbreaks in the high-risk San Francisco Bay Area.

From the San Francisco Bay Area to the Amhara Region of Ethiopia, computer modeling is helping scientists understand how sickness spreads. Travis Porco and his colleagues at the University of California, San Francisco, are taking advantage of this tool to help improve emergency preparedness both at home and abroad.

Porco, a biostatistician, wants to improve control of tuberculosis epidemics in the Bay Area. According to the San Francisco Department of Public Health, the city has previously had one of the highest incidences of tuberculosis in the country and currently has rates three times the national average.

In an attempt to cut the spread of tuberculosis, public health officials often interview people who catch it to determine factors like their age, symptoms, HIV status and exposure to others. Working with the California Department of Public Health, Porco’s research team will develop computer simulations to see which pieces of this information are most useful in designing interventions to contain the spread of tuberculosis.

As a part of this work, Porco’s colleague Tom Lietman will apply similar ideas to try to stop the spread of trachoma, a contagious bacterial eye disease that can cause blindness, in Ethiopia. Recent research cites the country’s Amhara region as one of the most high-risk areas for trachoma in the world.

Scientists know that antibiotic treatment can eliminate trachoma, but they are not yet certain how to distribute the medication most effectively. Leitman and Porco will use computer simulations of Ethiopian communities to test intervention strategies.

Both projects will identify populations that are vulnerable to these infectious diseases and will identify action plans to contain the infections.

Quantifying the Uncertain

Sara Del Valle, Los Alamos National Laboratory

Image of survey form
Surveying people's behavior during a disease outbreak can help design realistic computer models.

When a disease epidemic breaks out, many important details are hard to quantify. For instance, how much do people avoid human contact during an outbreak to reduce their chances of getting sick, or how useful is a face mask?

These types of questions drive mathematical epidemiologist Sara Del Valle’s work at Los Alamos National Laboratory. Her research looks at how human behavior has historically changed in response to a disease outbreak.

Human behavior is an important—but largely unstudied—variable when it comes to containing disease. “How many people comply with public health guidelines?” she asks. Officials aren’t really sure.

With information from past epidemics in hand, Del Valle will measure the effects behavioral changes have had on the spread of disease. She will then design computer models that are sensitive to these details. With simulations that mimic real human behavior more precisely, her team can reliably test response strategies, like closing schools or advising people in some communities to stay home from work.

Del Valle notes that uncertainties in how people respond to infectious diseases limit public health workers’ ability to contain or mitigate an epidemic. Replacing “sparse, inexact” data with models that consider and quantify behavior changes is an important step in managing infectious diseases, she says.

Learn about related research

This page last reviewed on September 9, 2011