Computer models are being used to help resource-poor nations improve supply chain decisions related to the distribution of breast milk and nonpharmaceutical interventions for malaria.

Researchers at the Georgia Institute of Technology (Atlanta, USA) have developed several global health case studies to aid resource allocations in poor nations. The first study was designed to determine strategically how a nongovernmental organization (NGO) should expand its breast milk donation and distribution network in South Africa. In the network, healthy mothers donate breast milk, which is stored in a local repository, transferred to a milk bank to be processed, and then distributed to neonatal units where mothers cannot provide it themselves because of disease status or physical inability.

To determine where the organization should expand its network and the best way to do so, the team used operations research to examine the existing and proposed locations in the network, as well as what type of transportation would work best to cover the increased geographic area. The model identified that breast milk supply increases with higher income and education levels and low HIV prevalence, while breast milk demand increases with lower income and education levels and high HIV prevalence.

In another project, in collaboration with the World Health Organization (Geneva, Switzerland), the researchers used models to optimize the distribution of nonpharmaceutical interventions for malaria, such as nets or aerial spraying using pilot flight data from Swaziland. Their models provided a time-based deployment plan for the country, including details on what geographic zones to target for spraying, when to deploy in each zone, how many people can be protected in each zone, what resources should be located at the distribution centers, and the opening and closing dates of the distribution centers. The researchers showed that using a systems approach to examine allocation decisions could increase the number of people covered with the same amount of funding by more than 25%.

A third project uses technology to estimate the performance of disaster preparedness plans in advance of an event, as part of the Caribbean Hazard Assessment Mitigation and Preparedness (CHAMP) initiative. In Puerto Rico, population data, projections of earthquake locations and severity, and existing hospital networks and other health care provider locations were overlaid to estimate the capacities and amount of congestion that would result at health care facilities. The initial results were use to make recommendations for health care resources and hospital capacities, based on predicted bottlenecks in the system. The three projects were presented at the annual meeting of the American Association for the Advancement of Science (AAAS; Washington DC, USA), held during February 2012 in Vancouver (Canada).

“We have found that technology innovations like mathematical models can help to solve problems in global and public health,” said studies presenter Julie Swann, PHD. “We are using mathematical models implemented in user-friendly tools like Microsoft Excel to improve the allocation of limited resources across a network, especially in resource-poor settings.”

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