An Algorithm Against Cholera

Machine-learning algorithms may offer a better system than ever for tracking deadly cholera outbreaks.

Current methods of tracking cholera outbreaks are fairly inaccurate, based on reports of patients with watery diarrhea at local hospitals. Researchers may have found a way to get more accurate cholera case counts, which are key to identifying hotspots and measuring how different cholera-control interventions stack up.  

Using data from multiple antibody signatures in peoples’ blood, researchers were able to identify those recently infected with the disease, according to research from the Johns Hopkins Bloomberg School of Public Health published in Science Translational Medicine.

Johns Hopkins Bloomberg School of Public Health via Newswise (news release)

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