Current research uses smart surveillance to rapidly identify emEcoHealth Alliance, the nonprofit organization that focuses on local conservation and global health issues, announced new research focused on the rapid identification of disease outbreaks in the peer reviewed publication, Journal of the Royal Society Interface.
EcoHealth Alliance, the nonprofit organization that focuses on local conservation and global health issues, announced new research focused on the rapid identification of disease outbreaks in the peer reviewed publication, Journal of the Royal Society Interface. The article, authored by leading scientists in the fields of emerging disease ecology, biomathematics, computational biology and bioinformatics, shows how network theory can be used to identify outbreaks of unidentified diseases. The strategy builds on the wealth of online surveillance data and increased reporting and tracking of emerging infectious diseases via the Internet. Pandemics often first emerge in remote regions, and early in their development, the identity of the cause is often unknown. In many cases these events turn out to be known diseases that don't require emergency action, and cutting through the clutter and uncertainty to determine which outbreaks are important is a critical challenge.
The newly released research used a simple set of data collected at the earliest stages of an outbreak such as symptoms, time of year, and percentage of the population that died (the case fatality rate). This information was collected from 125 reports of outbreaks on 10 known infectious diseases causing encephalitis (brain or neural infection) in South Asia - a known 'hotspot' for new disease outbreaks. The data was analyzed to examine whether outbreaks of the same disease clustered together, based on basic outbreak properties (symptoms, timing and case fatality rate). Results showed that diseases such as Nipah virus - an emerging and very lethal disease - showed distinct characteristic patterns within such a network and clustered separately to other more established diseases such as malaria and Japanese encephalitis. The team was then able to take outbreaks caused by unknown pathogens and provide a probable diagnosis for these 'mystery diseases'. The initial analysis shows a promising advantage to aid in predicting and preventing possible pandemic diseases that can result in devastating losses in life and global economic crises. "This application of network theory is exciting not only because it provides a fast, affordable method for associating undiagnosed outbreaks with a set of most likely known diseases, but perhaps most importantly because it provides a method for researchers to work with public health experts to identify potentially novel pathogen threats, as these agents will not fall into any of the known disease clusters and therefore can be easily identified," said Dr. Tiffany Bogich, Princeton University.