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Malaria Eradication Research Agenda (malERA)

Can computer models help control infectious diseases?

5 Jan 2009

Paul Chinnock

Source: VOA News (see original article)
Source: ZAMP Bionews (see original article)

Computer models are increasingly seen as potential new tools in the control of the infectious diseases of poverty. One new computer program, developed at the Massachusetts Institute of Technology (MIT) and based on four years of observations in Niger, was recently presented at the annual meeting of the American Geophysical Union in San Francisco. The program creates environmental models intended to help in the control of malarial mosquitoes.

The model compares conventional strategies, such as insecticide-treated bed nets, to physical changes such as levelling land to eliminate depressions in soil where water can accumulate. MIT civil engineer Elfatih Eltahir says environmental changes can be a significant factor in the fight against malaria: “Some of the experiences regarding elimination of malaria in some parts of North America and Europe and South America in the past relied maybe exclusively on these kind of approaches. So they definitely have a significant level of efficiency.”

Describing his team’s model Dr Eltahir says: “It is like many applications of computer models we use to screen and compare different alternatives. I think we could bring that technology to look at the malaria problem in Africa too, as an additional tool to plan environmental management to help in those kind of efforts.”

Meanwhile, another US team – at Case Western University, Ohio – is developing models to analyse environmental conditions, symptoms and treatment outcomes for a range of diseases that include malaria and schistosomiasis. Mathematics professor David Gurarie said: “Generally there is a fair amount of interest in using mathematic models in life sciences. Such models applied to biological systems can help researchers to understand the data they collect and address problems where traditional methods (based lab or filed study) fail. By understanding the data, we can make predictions about where new diseases may emerge, how they spread in the environment, and whether we’ll be able to control future outcomes.”

“Realistically”, said Professor Gurarie, “you can’t put the entire complex system into your model. But one tries to include enough ‘realism’ along with mathematical/computational efficiency, then plug it into the computer to run simulations and make predictions. Essentially you create the tool to run experiments that would be prohibitive or unfeasible in real life. It’s like creating virtual mice in the computer.”

It will be interesting to see how practitioners on the front line of disease control respond to the new trend for computer modelling. Some may be sceptical as to what mathematicians and software developers can offer, given the difficulty in allowing for all the complexities and variables between local settings.