Okeke, a professor of biology, is the lead author of a study with Manning, a professor of mathematics and statistics, and another researcher that used computer simulation to study Ebola outbreaks and ways to reduce the spread of the deadly virus.
One of the early telltale signs of the scourge Ebola is a severe fever. Unfortunately, that's also a symptom of malaria, another disease common in West Africa, which is often the diagnoses given to patients who present with a high fever. Only after anti-malaria medication fails is an alternative such as Ebola considered. By then, a viral outbreak could be well on its way.
Better diagnosis would make a significant difference in the battle against the deadly disease plaguing West Africa, according to an important study that includes work by two Haverford College researchers.
“When Ebola outbreaks do occur, they infect and kill many more people than they should because disease-specific precautions are not put in place early enough simply because no one recognizes that an Ebola outbreak is in play,” explainsIruka Okeke, a professor of biology and lead author on the study. She collaborated with Robert Manning, a professor of mathematics and statistics at Haverford, as well as with researcher Thomas Pfeiffer, who is a professor of computational biology and biochemistry at Massey University in New Zealand.
“The result is that the case numbers and case fatalities in all outbreaks of Ebola and other hemorrhagic viruses have been extraordinarily high in Africa,” says Okeke, who is on sabbatical at the University of Ibadan in Nigeria for the 2014-2015 academic year.“Since Ebola was, and still is, uncommon, the cost of testing every suspected case would be high but the chance of getting a positive would be low. This means that testing would be cost-ineffective.”
The study in the September 2014 issue of The Journal of Infection in Developing Countries (JIDC) used computer simulation models to show that the use of certain strategies—isolating patients who do not respond to anti-malaria therapy, conducting post-mortem testing of unresolved cases, and regular testing of health workers—are effective ways to identify potential Ebola outbreaks before it spreads out of control.
Her colleagues, Okeke says, used“clever modeling ideas that made the model more realistic and more useful than earlier models.”
Okeke initiated the study with Pfeiffer when both were Society in Science Branco Weiss Fellows in Zurich, Switzerland. At the time, she was researching the under usage of diagnostic tests in Africa.“I wanted quantitative evidence that tests would save lives—and save health systems money—in settings where resources are limited,” she says. Okeke says she had ethical concerns over conducting field research in which some patients would have access to the diagnostic test and others would not.
Instead, she sought out a theoretical way to test the hypothesis. Pfeiffer agreed that modeling the epidemiology of a rare hemorrhagic viral disease in an area where less deadly diseases with similar symptoms were common would be the best way to test the hypothesis. To move the project along, Manning was recruited. With the research housed mainly at Haverford, students got involved in developing and testing early versions of the model, Okeke says.“We involved some excellent mathematics majors when we started, and Rob was their day-to-day mentor,” she says.
Even though the research was conducted before the recent headline-grabbing Ebola epidemic, it garnered lots of interest when the West Africa outbreak escalated a few months after the paper was accepted.“People who had heard about the work earlier on asked for pre-publication copies of the manuscript,” Okeke says,“and the paper was editorially fast-tracked.”
