Anticipating Epidemics using Computational Models

Infectious diseases threaten public health, national security, and economic prosperity. Outbreaks can spread faster today than ever before as our world becomes increasingly interconnected. New diseases are emerging as pathogens that at one time were seen only in animals jump to humans, while other pathogens that have been known for a long time are becoming newly resistant to drugs. The recent outbreaks of Ebola in West Africa and Zika across the Americas highlight the need for global preparedness for emerging infectious diseases.

President Obama has promoted global health security as a core objective of the Nation’s strategy for countering biological threats, and led America and international coalitions in strengthening global defenses against them. For example, in November President Obama signed an Executive Order to advance and institutionalize the Global Health Security Agenda, a multi-country partnership to prevent, detect, and respond to infectious disease threats. Federal agencies are making progress in implementing the National Action Plan for Combating Antibiotic-Resistant Bacteria. 

Today, building on the Administration’s efforts in global health security, the White House is releasing a report that lays the foundation for a proactive, anticipatory, and data-driven approach to emerging infectious diseases. Issued by the National Science and Technology Council, Towards Epidemic Prediction: Federal Efforts and Opportunities in Outbreak Modeling draws on experience across the Federal government in the new interdisciplinary science of outbreak prediction.

The emerging science is maturing with recent advances in genomic technology, pathogen biology, bioinformatics, ecology, and machine learning. Scientists are growing the knowledge base and developing the computational tools needed for predicting infectious disease outbreaks. Public health responders increasingly use these tools to determine how best to contain an outbreak. In the future, understanding the processes that drive disease emergence and transmission could help to predict and prevent large-scale outbreaks.

Towards Epidemic Prediction highlights Federal investments in predictive modeling of human, animal, and plant disease outbreaks. These programs range from foundational research into disease emergence and spillover, to predictive modeling contests, to the development of decision-support technologies for public health responders. The report provides specific recommendations in three key areas where further efforts are needed:

• improving sharing of data and information to accelerate the development, validation, and application of computational outbreak models;
• integrating outbreak prediction into public health programs; and
• understanding the factors contributing to infectious disease emergence.

Towards Epidemic Prediction emphasizes that a One Health approach—encompassing human, animal, plant, and environmental health priorities, science, and communities—is an essential foundation for these efforts.

Through the National Science and Technology Council, Federal agencies will continue to coordinate efforts to advance scientific understanding of infectious disease emergence and transmission, and translate that knowledge into actions that reduce the impact of outbreaks.
 

Jean-Paul Chretien is Senior Policy Advisor for Biological Threat Defense at the White House Office of Science and Technology Policy, and chairs the NSTC Pandemic Prediction Science and Technology Working Group

Eleanor Celeste is Policy Analyst for Medical and Forensic Sciences at the White House Office of Science and Technology Policy

Caitlin Rivers is an Epidemiologist with the US Army Public Health Center, Department of Defense; and leads the Open Science focus area for the NSTC Pandemic Prediction Science and Technology Working Group