Estimating cholera incidence with cross-sectional serology

Science Translational Medicine
20 February 2019  Vol 11, Issue 480

Research Articles
Estimating cholera incidence with cross-sectional serology
By Andrew S. Azman, Justin Lessler, Francisco J. Luquero, Taufiqur Rahman Bhuiyan, Ashraful Islam Khan, Fahima Chowdhury, Alamgir Kabir, Marc Gurwith, Ana A. Weil, Jason B. Harris, Stephen B. Calderwood, Edward T. Ryan, Firdausi Qadri, Daniel T. Leung
Science Translational Medicine20 Feb 2019 Open Access CCBY
Cross-sectional Vibrio cholerae–related antibody measures can be used to estimate cholera incidence in a population.
Estimating the true prevalence of cholera
Successful development of anti-cholera measures requires accurate estimates of infection incidence. Reporting of cholera cases, however, typically relies on clinical assessment at the time of patient presentation and can be problematized by lack of access to health care and variable, nonspecific symptomatology. Combining a small number of serological markers with machine learning methods, Azman et al. were able to accurately detect individuals who had had cholera infections within the previous year. Simulated serosurveys showed that this simple antibody-based approach could potentially be used as an alternative method to estimate cholera incidence in a population.
The development of new approaches to cholera control relies on an accurate understanding of cholera epidemiology. However, most information on cholera incidence lacks laboratory confirmation and instead relies on surveillance systems reporting medically attended acute watery diarrhea. If recent infections could be identified using serological markers, cross-sectional serosurveys would offer an alternative approach to measuring incidence. Here, we used 1569 serologic samples from a cohort of cholera cases and their uninfected contacts in Bangladesh to train machine learning models to identify recent Vibrio cholerae O1 infections. We found that an individual’s antibody profile contains information on the timing of V. cholerae O1 infections in the previous year. Our models using six serological markers accurately identified individuals in the Bangladesh cohort infected within the last year [cross-validated area under the curve (AUC), 93.4%; 95% confidence interval (CI), 92.1 to 94.7%], with a marginal performance decrease using models based on two markers (cross-validated AUC, 91.0%; 95% CI, 89.2 to 92.7%). We validated the performance of the two-marker model on data from a cohort of North American volunteers challenged with V. cholerae O1 (AUC range, 88.4 to 98.4%). In simulated serosurveys, our models accurately estimated annual incidence in both endemic and epidemic settings, even with sample sizes as small as 500 and annual incidence as low as two infections per 1000 individuals. Cross-sectional serosurveys may be a viable approach to estimating cholera incidence.