National Institutes of Health study adds dengue, TB and H1N1 models

The National Institutes of Health’s Models of Infectious Disease Agent Study recently added three new projects and several new investigators to its international research network and is expected to receive grants worth nearly $9 million over the next five years.

Several new investigators will join MIDAS and the study is expected to now include models of the spread of the dengue virus, tuberculosis, H1N1 and other infections.

MIDAS builds mathematical and computational models used in the study of how infectious diseases spread. The models are used to help public health officials and policymakers prepare for and respond to outbreaks, according to

“The investigators who are joining MIDAS bring a major focus to areas that are less represented in the current network,” Jim Anderson, MIDAS' administrative director, said, reports. “There will be an emphasis on vector-borne diseases as well as diseases primarily found in developing countries. Infectious disease is a global issue, and so must be the reach of MIDAS.”

Sara Del Valle of the Los Alamos National Laboratory will be brought into the project to develop computer simulations that will study aspects of how humans behave during an epidemic. Quantifying human behavior is a key but largely understudied variable when it comes to containing a disease.

Christopher Mores of Louisiana State University will join to study the spread of the mosquito-borne dengue virus that infects 50 to 100 million people every year. Mores will use mathematical models to show how human organization and movement patterns have affected dengue’s spread in Colombia and Puerto Rico, and how the disease can be slowed on the U.S. mainland.

Travis Porco of the University of California - San Francisco will use computer modeling to study tuberculosis epidemics in the Bay Area, where TB rates are still three times the national average. His work is expected to focus on what characteristics, such as age, symptoms and exposure to others, are critical in predicting the disease’s spread.