Algorithms using data from antibody signatures in peoples’ blood may enable scientists to assess the size of cholera outbreaks and identify hotspots of cholera transmission more accurately than ever, according to a study led by scientists at the Johns Hopkins Bloomberg School of Public Health.
Current methods for tracking cholera outbreaks rely heavily on local hospital reports of cholera-like diarrhea cases and are relatively inaccurate. In the new study, published online February 20 in Science Translational Medicine, the researchers developed machine-learning algorithms that use results from multiple cholera antibody tests to accurately identify individuals recently infected.
The team found that an algorithm using a set of antibody measures could determine very sensitively, with very low rates of false positives, whether a person had had a cholera infection within different windows of time in the year prior to giving the blood sample — for example, within the previous 45 days or previous 100 days.
“We think this could be a useful new tool not only for tracking cholera incidence in different populations, but also for measuring how well different cholera-control interventions work,” says study lead author Dr. Andrew Azman, assistant scientist in the department of epidemiology at the Bloomberg School.
Cholera is thought to infect two to three million and kill more than 100,000 people around the world every year, especially in Africa, South Asia and Haiti.Friday Letter Submission