Informatics Resources for Ebola Epidemic Response

NDSSL has supported the DoD, NIH and other federal agencies during the 2014 Ebola outbreak in West Africa by providing them analytical results, enabling policy makers to make more informed scientifically backed decisions and policies.

About Ebola

The latest Ebola outbreak in Western Africa has illuminated the significant threat posed by infectious diseases to human lives and society. The ongoing Ebola outbreak is by far the largest in history. As of October 12, 2014, over 5500 individuals have been confirmed infected and have caused almost 4500 deaths. It has ravaged Guinea, Liberia, Sierra Leone, with smaller outbreaks in Nigeria and Senegal, causing significant social, health and economic impact.

Ebola is a challenging disease to combat. There is no known cure and the mortality rate is high. While its origins are zoonotic in nature, once spread to humans, Ebola can be passed onto another through bodily fluids. The average incubation period is six days, but can last as long as 21 days. During that time, those that are infected are not contagious. However, given the means of transmission, the outbreak can be controlled using proper precautions.

The current web page is created so as to make the data, methods and analysis available to the world community in an attempt to speed up the scientific progress on this important public health crisis.

Modeling Challenges

This particular West African outbreak brings its own set of challenges. Data is not readily available, making modeling and analysis efforts all the more difficult. The populations being affected are rather mobile and the areas involved in the outbreak include significant population centers.

The outbreak’s severity has been influenced by several factors. Cultural aspects involving burial practices and fear of authority figures have complicated the response to Ebola. Those living in the affected area were not familiar with the disease; their medical facilities were not equipped to handle such an infection.

Applying interventions in a resource-poor area during an ongoing epidemic is not easily done and the level of success can be full of uncertainty. Modeling disease outbreaks can therefore be helpful by providing epidemic forecasts that explain the complex dynamics of infectious diseases. Simulation and modeling can predict the likely impact of possible interventions before they are implemented. As a result, policy makers and public healthcare workers are provided with measurable guidance and support.

Role of computational modeling and data analytics

Computational and data scientists can play an important role in supporting planning and response efforts. This includes:

  • development of high performance computing based simulations to understand the disease characteristics, including spatial and temporal spread, and identify vulnerable populations
  • forecasting the extent of the epidemic, and also taking into account the potential impacts of various interventions
  • logistical analysis including medical equipment, hospital staffing and manpower planning, placement of emergency response facilities
  • pervasive computing based apps and middleware to support rapid prototyping of tools and their integration within a working eco-system

High performance computing, big-data analytics, algorithms, visualizations and distributed systems are all important while developing scalable tools and decision support systems. Real-time computational epidemiology wherein new analysis needs to be done rapidly while taking the currently available ground information to support critical decisions in the field make this even more important. Effective computing support enables improved readiness, planning and decision making in the domains of public safety and security. A state of the art environment can deliver detailed situational awareness and decision support for an epidemic such as Ebola.

Our Approach

NDSSL is developing a synthetic information environment comprised of:

  • disaggregated and highly resolved synthetic representations of the countries
  • high performance computing based models for understanding the spatio-temporal spread of the disease
  • a pervasive computing oriented middleware and application eco-system to support analysts, data scientists, field scientists and concerned citizens in their goal of participating in the overall response to contain the epidemic

NDSSL is supporting the DoD, NIH and other federal agencies by providing them analytical results enabling policy makers to make more informed scientifically backed decisions and policies. NDSSL has performed several computational studies that have been used by these agencies in their decision making process:

  • infer disease parameters in the very early phase of the epidemic
  • produce weekly forecasts of disease incidence and mortality
  • understand the chances of Ebola spreading in the Continental US
  • provide input on the location of Ebola treatment units
  • study efficacy of various interventions

NDSSL has also been collecting and synthesizing novel high resolution data. This includes:

  • synthetic populations, social contact networks, GIS information for developing a statistically accurate but normative computer model of the three West African countries
  • collecting published data from various open source sites that are relevant to modeling and analytics
  • developing methods to develop RDF-based linked representations of the data so that the data can be processed by computer programs capable of working with semantic web technology

We are also adapting our high performance computing oriented modeling environments so that we can study a broad range of counter-factuals (what-if scenarios) and provide analytics to the relevant agencies.

Finally, we are creating web-apps that can be used by a broad range of individuals interested in participating in the broad goal of “computing and analytics for social good” as it pertains to the Ebola epidemic.

Collaborations

NDSSL is actively collaborating with the following entities in order to successfully complete our Ebola research.

Name Affiliation Area
Kathy Alexander Virginia Tech Fish and Wildlife Conservation
John Brownstein Children’s Hospital Boston Computational Epidemiology
Matt Clay US Department of Health & Human Services
V.M. Dato University of Pittsburgh Biomedical Informatics
Deena Disraelly Institute for Defense Analyses
John Drake University of Georgia Ecology
Joseph Eisenberg University of Michigan Epidemiology
Marisa Eisenberg University of Michigan Epidemiology
Josh Epstein Johns Hopkins University
Matthew Ferrari The Pennsylvania State University Biology
Manoj Gambhir Centers for Disease Control Mathematical Epidemiology
Dylan George US Department of Health & Human Services  
Jerry Glasow Defense Threat Reduction Agency  
Jeffrey Grotte Institute for Defense Analyses  
Rebecca Gurba Defense Threat Reduction Agency  
Christopher Kiley Defense Threat Reduction Agency  
M Elizabeth Halloran, University of Washington Biostatistics  
Todd Hann Defense Threat Reduction Agency  
James Hyman Tulane University Mathematics
Ronald Meris Defense Threat Reduction Agency  
Lauren Meyers University of Texas at Austin Integrative Biology
Rafael Meza University of Michigan Epidemiology
David Myers Defense Threat Reduction Agency  
Michael Phillips Defense Threat Reduction Agency  
Travis Porco Francis I. Proctor Foundation, University of California  
Kathryn Raymond Defense Threat Reduction Agency  
Naren Ramakrishnan Virginia Tech Computer Science
Claire Sanderson Virginia Tech Fish and Wildlife Conservation
Samuel Scarpino Santa Fe Institute  
Jeffrey Shaman Mailman School of Public Health, Columbia University Environmental Health Sciences
Rachel Torman Defense Threat Reduction Agency  
Alessandro Vespignani Northeastern University Network Science
Aiguo Wu Defense Threat Reduction Agency
Wan Yang Mailman School of Public Health, Columbia University Environmental Health Sciences

Funding for our Ebola Modeling Effort

The Ebola modeling effort has been directly funded by the DoD Defense Threat Reduction Agency (DTRA). Previous disease modeling work has been funded by the NIH Modeling of Infectious Disease Agent Study (MIDAS) program. Other aspects of this work have been funded over the last twenty years by the Centers for Disease Control, the Department of Defense, the Department of Energy, the Department of Health and Human Services, the Department of Homeland Security, the Department of Transportation, the Intelligence Advanced Research Projects Activity, and the National Science Foundation.

The views expressed herein are those of the researchers and our collaborators and should not be attributed to any of these agencies.

Publications

Lofgren E, Halloran M, Rivers C, Drake J, Porco T, Lewis B, Yang W, Vespignani A, Shaman J, Eisenberg J, Eisenberg M, Marathe M, Scarpino S, Alexander K, Meza R, Ferrari M, Hyman J, Meyers L, Eubank S (2014) Opinion: Mathematical models: A key tool for outbreak response. Proceedings of the National Academy of Sciences (PNAS).

Rivers C, Alexander K, Bellan S, Valle S, Drake J, Eisenberg J, Eubank S, Ferrari M, Halloran M, Galvani A, Lewis B, Lewnard J, Lofgren E, Macal C, Marathe M, Mbah M, Meyers L, Meza R, Park A, Porco T, Scarpino S, Shaman J, Vespignani A, Yang W (2014) Ebola: models do more than forecast. Nature, 515(492).

Alexander K, Sanderson C, Marathe M, Lewis B, Rivers C, Shaman J, Drake J, Lofgren E, Dato V, Eisenberg M, Eubank S (2014) What factors might have led to the emergence of Ebola in West Africa? .PLOS Neglected Tropical Diseases, 1418-1425.

Rivers CM, Lofgren ET, Marathe M, Eubank S, Lewis BL. Modeling the Impact of Interventions on an Epidemic of Ebola in Sierra Leone and Liberia. PLOS Currents Outbreaks. 2014 Oct 16. Edition 1.

Halloran M, Vespignani A, Bharti N, Feldstein L, Alexander K, Ferrari M, Shaman J, Drake J, Porco T, Eisenberg J, Valle S, Lofgren E, Scarpino S, Eisenberg M, Gao D, Hyman J, Eubank S, Longini Jr. I (2014) Ebola: Mobility data. Science, 346(6208): 433.

Alexander K, Lewis B, Marathe M, Eubank S, Blackburn J (2012) Modeling of Wildlife Associated Zoonoses: Applications and Caveats. Vector-Borne and Zoonotic Diseases, 12(12): 1005-1018.

Barrett C, Beckman R, Khan M, Kumar VS Anil, Marathe M, Stretz P, Dutta T, Lewis B (2009) Generation and analysis of large synthetic social contact networks. In Proceedings of Winter Simulation Conference (WSC), 1003-1014.

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