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Global Obesity Prevention Center

Computational Methods & Tools

 
 

To tackle non-communicable and communicable diseases as well as other public health challenges, the Global Obesity Prevention Center (GOPC) uses computational modeling to build virtual representations of supply chains, neighborhood food environments, and other systems. Through computational modeling, we can test various policies, interventions, and possible solutions in the safety of a computer and save time, money, and resources before implementing them in real life. Computational modeling can be utilized in a variety of public health challenges.

Examples of our modeling work:

 

Example 1: Helping a Variety of Decision Makers During the 2009 Influenza Pandemic

 

TT immunizationDuring the 2009 H1N1 influenza pandemic, our team worked closely with state and local health departments, the US Department of Health and Human Services (DHHS) including being embedded in DHHS for a period of time (as detailed in this paper in Clinical Microbiology and Infection), the Centers for Disease Control (CDC), the US Department of Homeland Security (DHS), and the President’s Council for Advising Science and Technology (PCAST) to assist with the national and local response. For example, initially available vaccine doses were limited due to production delays, therefore decision makers such as DHHS had to determine who should get the limited number of vaccines first. Our modeling helped identify the priority populations such as ACIP priority populations as reported in Vaccine and lower income population as reported in  Health Affairs. Our modeling also helped show businesses how vaccinating their employees could actually save them money (as reported in Vaccine).

Example 2: Helping Decision Makers Plan Food and Health-Related Supply Locations

 

example 2We're working with food providers, health care providers and other decision makers to understand how the geographical location of a resource changes the impact of that resource. For example, we have worked with health care providers in Mozambique to identify the number of pregnant women who cannot access existing tetanus toxoid (TT) immunization locations because they live in geographically hard to reach areas. We’ve further demonstrated the importance of expanding access to TT immunizations by quantifying the resulting economic and disease burden (as reported in Vaccine).

Example 3: Helping Health Officials and Decision Makers Understand the Spread and Control of Health Care Associated Infections (HAI) Throughout a Region

 

RHEA We have worked with health care facilities, local and state officials such as the Orange County Department of Health and the California Department of Health and national health agencies such as the CDC to identify and test different policies for controlling HAIs. In multiple scenarios, our modeling showed the benefits of health care facilities in a region working together as opposed to independently in preventing HAIs, as reported in Health Affairs,  AJE, and in the CDC’s report, Vital Signs. Our models also showed that implementing coordinated protocols specifically at ICUs reduces infections for the entire county population (as reported in AJE). 

Example 4: Evaluating and Improving the Delivery of Medical and Health-Related Products Throughout the World

 

dronesOver 8 years ago, we formed collaborations with the Bill and Melinda Gates Foundation (BMGF), Gavi the Vaccine Alliance, World Health Organization (WHO), UNICEF and others with the goal of improving health product delivery across the globe. For example, with the Beninese Ministry of Health (MOH), Agence de Médecine Préventive (AMP) and PATH, we modeled an immunization supply chain in Benin to identify vulnerabilities and bottlenecks and evaluated the impact of various supply chain redesign options. This work helped identify a design that not only cost less to operate but also was more effective at getting vaccines to the population, reported here in Vaccine. This design improved performance and efficiency in one commune and Benin has since decided to scale up the strategy to the entire country. In work with Village Reach, our models have quantified the benefits of using drones to deliver vaccines, as reported here in Vaccine and here in Forbes, prompting BMGF and others to invest in the technology.

Example 5: Helping Decision Makers Understand the Total Costs of Disease

 

SUPER BUGWe are working with various decision makers to help identify what diseases should be focused on and how much efforts and resources are needed for each disease. For example, our models have shown that norovirus carries billions in individual, health system and societal costs, and is deserving of increased attention, as reported here in PLoS One. In another example, the economic burden of Chagas, a lesser known, neglected tropical disease, rivals that of than many globally prominent diseases, as reported here in The Lancet.

Example 6: Evaluating the Contributors to Obesity and Other Chronic Diseases and Designing and Testing Prevention and Control Measures

 

urban farmingWe’re working with the National Institutes of Health (NIH), city health departments, and a variety of decision makers and stakeholders to design and test prevention measures, policies, and interventions for obesity and other health challenges. For example, our models have shown that increasing physical activity for children could improve their health in adulthood and avert billions in individual and societal costs over the years, reported in Forbes here. Working with the Baltimore City Health Department, Baltimore City Council, and other members of the Baltimore Policy Working Group, we showed that converting vacant lots into urban farms would greatly increase the availability of healthy foods and improve dietary behaviors in Baltimore City. This work helped form the Urban Farm Tax Credit Bill, which was adopted in 2015, reported here in the Baltimore Sun


Additional Information

If you're interested in learning more about the GOPC's computational models, please contact us.