New Technology to Predict Future Disease Outbreaks
In a paper published early in Emerging Health Threats Journal, researchers from Tufts University details innovations that they believe will facilitate earlier detection and improved public health. The goal is to develop analysis tools that can predict the severity and probability of an outbreak.
The difficulties faced by researching in predicting outbreaks include definitions of an outbreak, differences in quality and source of data, and the differences in how the diseases progress and spread. The study proposes new methods that should overcome these obstacles.
One of the newly proposed solutions include updating data analysis tools to be able to process data streams, such as those transmitted to and from satellites from outbreak sites. “Dynamic mapping, multivariate visualization, flow mapping, outbreak signature forecasting and large-scale simulations of infection spread are just a few of the tools being developed that might help us better detect the complexities of disease spread.” says one author, Dr. Naumova.
Dr. Naumova and Dr. Fefferman (authors) are co-directors of the Tufts University Initiative for the Forecasting and Modeling of Infectious Diseases (Tufts InForMID). Tufts InForMID works to assist life science researchers by developing computational tools. By developing these new tools, researchers see a clearer future to improving public health. Some others have suggested using social media as a predictor and tracker of disease spread. Either way, the future of technology will help the future of public health.
The study was funded by the US National Institute of Allergy and Infectious Diseases, Center for Discrete Mathematics and Theoretical Computer Science, and the Department of Homeland Security Command, Control and Interoperability Center for Advanced Data Analysis (both at Rutgers University).
For more about disease spread and outbreak predictions, please visit the World Health Organization website
Fefferman NH, Naumova EN. Emerging Health Threats Journal. 2010. “Innovation in Observation: A Vision for Early Outbreak Detection.” Published online July 6, 2010, doi: 10.313/ehtj. 10.006.