Computing to Save Lives

If you think computing doesn't have much to do with reducing the burden of diarrheal diseases in the developing world, one computer scientist at least would disagree. Sean Green, a Ph.D. student at Pittsburgh's Carnegie Mellon University, is using computer modeling to identify the best ways to curb the spread of diarrheal illness — which kills well over 2 million people the world over each year.

Green, a veteran traveller of developing countries, wanted to do something to help people in the countries he visited. As an engineer, he saw an opportunity to use modeling of water and sanitation problems to get the maximum bang out of the bare bones infrastructure budgets in developing countries.

According to an article on Carnegie Mellon's website, Green says that artificial intelligence can "fill in the gaps when a country has missing inputs or messy data. This type of analysis tells you a different side of the story than the one you get with regular statistics."

Green estimates that improving rural sanitation in the world by 65 percent could save more than 1 million lives per year. He and his professors published an article to that effect in the Environmental Science & Technology journal, Green.

Green is in India now, applying his modeling findings with the help of the Center for Study of Science, Technology and Policy. He's developing a series of surveys to help communities and agencies measure the affect of poor water and sanitation. Part of his work with the Center involves collaborating with a doctor who serves an area of India that has one of that nation's highest child mortality rates. They've set a goal of cutting the area's infant mortality rate in half in five years by combining Green's research with medical screening.

"It's not the end-all-be-all, but it is a great way to help policymakers with decisions regarding sanitation or health spending," explained Green in the article. "It gives credence to policymakers' recommendations, because they're driven by data."