Research shows that the Ebola outbreak could be predicted based on individual risk factor data

Research shows that the Ebola outbreak could be predicted based on individual risk factor data

A few years ago, a team of scientists at Lehigh University developed a predictive model to accurately predict Ebola outbreaks based on climate-related bat migrations. Ebola is a serious and sometimes fatal infectious disease that is zoonotic, or enters the human population through interaction with animals. It is widely believed that the cause 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 education levels and general knowledge of Ebola, can contribute to "high-risk behavior" that can bring people into contact with potentially infected animals. A focus on geographic locations with high concentrations of high-risk individuals could help public health officials better target prevention and education resources.

We created a survey that combined collection of social, demographic and economic data with questions about general knowledge about Ebola transmission and potentially high-risk behaviors. "Our results show that it is indeed possible to calibrate a model to predict a person's propensity to engage in risky behaviors with a reasonable level of accuracy."

Paolo Bocchini, a professor of civil and environmental engineering at Lehigh and one of the study leaders

For example, the team's data and analysis suggested that Kailahun, a town in eastern Sierra Leone, and Kambia in the north of the country are the country's rural districts with the highest likelihood of infection spillover, based on the precise identification of individual risk factors in the location of Kailahun, where the 2014 Ebola epidemic is believed to have originated.

The results are detailed in an article titled “Estimation of Ebola’s spillover experience exposure in Sierra Leone based on Sociodemographic and Economic Factors,” forthcoming in PLOS ONE. Additional authors include: Sena Mursel, a graduate student at Lehigh University, Nathaniel Alter, Lindsay Slavit and Anna Smith; and Javier Buceta, faculty member at the Institute for Integrative Systems Biology in Valencia, Spain.

Among the findings: Young adults (ages 18 to 34) and adults (ages 34 to 50) were most at risk in the population they studied. This group made up 77% of the sample studied, but 86% of respondents were at risk. Furthermore, those with agricultural occupations were among the most at risk: 50% of study participants have an agricultural occupation but represent 79% of at-risk respondents

“We confirmed a relationship between social, economic and demographic factors and the propensity of individuals to engage in behaviors that expose them to Ebola spillover,” says Bocchini. “We have also calibrated a preliminary model that quantifies this relationship.”

The authors say these results point to the need for a holistic approach to any model that attempts to accurately predict disease outbreaks. Their results may also be useful to population health officials, who may be able to use such models to better focus scarce resources.

“You have to look at the big picture,” says Bocchini. “We collected satellite images that showed the evolution of environmental climate data and combined them with ecological models and random field models to capture the spatial and temporal fluctuations of natural resources and the resulting continent-wide movements of infected animal carriers, the social, economic, demographic and behavioral characteristics of the human population and integrated everything to arrive at our predictions.”

“Only this broad perspective and interdisciplinary approach can truly capture these dynamics, and with this line of research we prove that it works,” adds Bocchini.

“In the end, the conclusions of our study are not so surprising: Greater economic resources, more education and access to information are key factors in reducing health-related risk behaviors,” Buceta said. "Indeed, some of these factors have been linked to the so-called 'health poverty trap'. Our study and methodology show how quantitative analysis, involving individual rather than aggregated data, can be used to identify these factors."

To collect data for their study, Bocchini and Buceta traveled to Sierra Leone with a delegation of Lehigh students with support from the National Institutes of Health, Lehigh's Office of Creative Inquiry and in collaboration with the nonprofit organization World Hope International. The support of two local translators was crucial to the team's success in conducting the door-to-door survey. The students who worked on the project were part of Lehigh's Global Social Impact Fellowship program, which engages undergraduate and graduate students in work focused on addressing sustainable development challenges in low- and middle-income countries.

“This is exactly the type of ambitious interdisciplinary project with enormous potential for social impact that we want to engage Lehigh students in through the Global Social Impact Fellowship,” says Khanjan Mehta, Vice Provost for Creative Inquiry at Lehigh. “Students from various disciplines from across Lehigh had the opportunity to contribute to this work under the leadership of Dr. Bocchini and Dr. Buceta.”

The team's promising results make a strong case for broader data collection, and they are in discussions with Statistics Sierra Leone, the country's census agency, to conduct a nationwide version of their study.

Source:

Lehigh University

Reference:

Mursel, S., et al. (2022) Estimating Ebola spillover infection exposure in Sierra Leone based on sociodemographic and economic factors. PLUS ONE. doi.org/10.1371/journal.pone.0271886.

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