Ebola: New studies model a deadly epidemic

Feb. 10, 2015

Researchers from Arizona State University and Georgia State University are trying to better understand the epidemiology and control of Ebola Virus Disease in order to alleviate suffering and prevent future disease outbreaks from reaching the catastrophic proportions of the current Ebola crisis.

In reports appearing in the current issue of the journal The Lancet Infectious Disease, ASU researchers report on new efforts to model the impact of timely diagnostic testing on the spread of Ebola across populations. A better understanding of viral dissemination and techniques for disease management is vital if a similar calamity is to be avoided in the future.

Researchers from the ASU Biodesign Institute and the Simon A. Levin Mathematical, Computational and Modeling Sciences Center present a new study: “Modeling the effect of early detection of Ebola.” The study examines the levels of detection and patient isolation required to shut down transmission of Ebola.

As the authors of the Lancet modeling study emphasize, breaking the chain of Ebola transmission presents intimidating challenges. After the development of symptoms, the virus is highly contagious, and each new contact presents an opportunity for further spread of the disease. Polymerase chain reaction (PCR) can be used for pre-symptomatic identification of the Ebola virus. The current study models the expected outcomes on viral transmission of Ebola using PCR-based pre-symptomatic diagnosis and isolation of infected patients within three days of the onset of symptoms.

According to Biodesign's Karen Anderson, PhD, “Early detection of Ebola infection provides the opportunity and time to safely isolate and treat individuals before they become contagious. Our findings show two key things: first, that the predicted impact of early diagnostic tests depends on existing public health measures. Second, there appears to be a tipping point, where early diagnosis of high-risk individuals, combined with adequate isolation, can markedly decrease the predicted number of infected individuals.”

Read more at the Biodesign Institute website