How We Used Machine Learning to Investigate Where Ebola May Strike

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When David Pigott and his team analysed environmental data to find areas at most risk of an Ebola outbreak, Nigeria — a country that had never been the starting point for an outbreak — was at the top of their list (via propublica). Read more:

The model looks at factors like how patchy the area had become in the previous two years and how much “edge” has been created.We were initially surprised to see the cluster of flagged locations in the southwest region of Nigeria, since the nation has never been the starting point for an Ebola outbreak.

Our model showed that this rapid forest clearing has happened in the dangerous, patchy pattern that researchers say leads to more interactions between humans and wildlife, and therefore increases the chances of spillover. The virus has been circulating in areas where people burn trees to create farmland, said Michael, destroying the rodents’ habitat. “They go to human habitats as a result of bush burning and deforestation to find food,” he said. “As we continue to alter the environment, the risk of disease outbreaks are increasing significantly.”

, published in the journal Nature Communications, identified Nigeria as a country at risk for an Ebola outbreak based on both current conditions and future climate and socioeconomic drivers.of an Ebola outbreak. Among countries that had never reported an Ebola spillover before, Nigeria was at the top of their list. We know that Ebola isn’t constrained to country borders — after all, the worst Ebola outbreak to date started in Guinea, where the virus hadn’t previously been thought to be a threat.

to assess the risks of dozens of diseases that come from animals. The process has local experts select five criteria, commonly including epidemic potential or a country’s diagnostic capacity, and answer questions about different diseases for each criteria. Based on the answers, the diseases get scored as having a higher or lower priority.

 

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