Individual risk-factor data could help predict the next Ebola outbreak, new study shows
Several years ago, a team of scientists at Lehigh University developed a predictive model to accurately forecast Ebola outbreaks based on climate-driven bat migration. Ebola is a serious and sometimes-deadly infectious disease that is zoonotic or enters a human population via interaction with animals. It is widely believed that the source of the 2014 Ebola outbreak in West Africa, which killed more than 11,000 people, was human interaction with bats.
Now members of the team have examined how social and economic factors, such as level of education and general knowledge of Ebola, might contribute to “high-risk behaviors” that may bring individuals into contact with potentially infected animals. A focus on geographical locations with high concentrations of individuals at high-risk could help public health officials better target prevention and education resources.
For example, the team’s data and analyses suggested Kailahun, a town in Eastern Sierra Leone, and Kambia in the northern part of the country, as the rural districts in the country with the highest likelihood of infection spillover, based on individual risk factors accurately identifying the location, Kailahun, where the 2014 Ebola epidemic is believed to have originated.
The results are detailed in a paper, “Estimation of Ebola’s spillover infection exposure in Sierra Leone based on sociodemographic and economic factors,” published in PLOS ONE. Additional authors include: Lehigh University graduate student Sena Mursel, undergraduates Nathaniel Alter, Lindsay Slavit and Anna Smith; and Javier Buceta, a faculty member at the Institute for Integrative Systems Biology in Valencia, Spain.
Among the findings: young adults (ages between 18-34) and adults (ages between 34 - 50) were most at risk in the population they studied. This group constituted 77% of the investigated sample, but 86% of the respondents were at risk. In addition, those with agricultural jobs were among the most at risk: 50% of the study respondents have an agriculture-related occupation, but represent 79% of respondents at risk.
