Predicting danger of flu pandemic rests on differences in affected population

Simple Models of Influenza Progression Within a Heterogeneous Population, Dr. Richard C. Larson, a former president of INFORMS and a professor at MIT, discusses the importance of forecasting and ultimately limiting the spread of disease while taking into account the different infection rates among those who might contract the disease.

"We allow for socially active people who interact with many other people on a given day, and we allow for relatively inactive people who interact with few others," he writes. "We provide for highly susceptible people who are more likely to become infected once exposed to the virus, and we include those who are less susceptible. In a similar vein, we allow for highly contagious as well as less contagious infected persons.

"The reasoning behind these assumptions is that heterogeneity across the population in these attributes may affect in first-order ways the manner in which the disease propagates and, consequently, the manner in which we should address mitigation measures."

The paper introduces operations researchers to the need for new mathematical modeling of an influenza pandemic and its control. Operations research is the application of advanced analytical methods to help make better decisions.

The paper also explores 'social distancing' as a disease progression control method. Social distancing refers to steps aimed at reducing the frequency and intensity of daily contact among people. One example of social distancing is telecommuting to work rather than riding public transportation.

Two key findings are (1) early exponential growth of the disease may be dominated by susceptible people who have a great deal of contact with others and may not be indicative of the general population's risk of infection to the disease, and (2) social distancing with accompanying hygienic steps may be effective nonmedical ways to limit and perhaps even eradicate the disease.