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IBM says big data has provided new insight into how Ebola spread


IBM researchers are employing big data analytics to zero in on a group of Ebola virus-infected animal carriers that had not been previously identified.

Direct contact with the infected animal – most likely a bat or large snake – whether by touching or eating it – causes the disease to enter the human population, and then spread with “a wildfire-like effect,” researchers found.

The IBM study shows the group has been excluded to date from existing epidemiological data models.

“By addressing the source of infection earlier in the disease-spread we believe that it increases the probability that an entity like the World Health Organization can not only reduce an ebola outbreak, but also help to prevent a possible pandemic,” said Simone Bianco, a member of the IBM research staff. “It is important and cannot be understated.”

To help researchers from humanitarian agencies, governments and others to better address the disease-spread chain, IBM Research has made available open-source computational models through the Eclipse Foundation’s free Spatio-Temporal Epimidemiological Modeling framework.

The goal is to identify the source of infection earlier in the disease-spread chain to boost the chances of preventing an Ebola epidemic, and also averting a pandemic, said IBM researcher Simone Bianco in a statement.

The 2014 West Africa Ebola outbreak caused the death of more than 11,000 people, while more than 28,000 cases were reported. The three African countries most directly impacted were Sierra Leone, Liberia, and Guinea.

During the outbreak and immediately after its decline, the World Health Organization launched a three-phase program, which aims to increase preparedness and first response, provide critical care, and prevent the spread of the disease by improving the resilience of the population to an initial infection.

Ebola, while a human disease, is not carried primarily by humans. Primates, including humans and primates like gorillas, are susceptible hosts, in that they are not able to fight the infection and are at high risk of illness and death.

The virus becomes human-borne following a contact with an infected carrier animal, a species of animal which has the disease, but does not show clinical symptoms – a frequent occurrence for many diseases. The avian and swine influenza are notable examples, as is Ebola.

In order to identify and successfully implement intervention measures, researchers and governmental agencies often apply epidemiological modeling – the analysis of large amounts of disease-related data.

During the epidemic, many researchers, including researchers at the U.S. Centers for Disease Control, created mathematical models and computer simulations to understand the course of the disease and investigate the potential impact of interventions they might implement to combat the spread of the disease.

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