Estimating cholera incidence with cross-sectional serology

Abstract

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 recentVibrio choleraeO1 infections. We found that an individual’s antibody profile contains information on the timing ofV. choleraeO1 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 withV. choleraeO1 (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.

Serological signatures of SARS-CoV-2 infection: Implications for antibody-based diagnostics

“…There is also substantial between-individual variation in the antibody response generated following SARS-CoV-2 infection. By measuring multiple biomarkers in large numbers of individuals, it is possible to create a serological signature of previous infection [17][18][19]. Although necessarily more complex than a single measured antibody response, such an approach has the potential of providing more accurate classification and being more stable over time.…”

“…The challenge lies in selecting appropriate biomarkers and choosing between the increasing number of commercial assays, many of which have not been extensively validated and may produce conflicting results. The opportunity is that with multiple biomarkers, it is possible to generate a serological signature of infection that is robust to how antibody levels change over time [17][18][19][20], rather than relying on classification of sero-positive individuals using a single cutoff antibody level.…”

An open source tool to infer epidemiological and immunological dynamics from serological data: serosolver

“…Mathematical models that harness the qualitative predictability of the post-exposure antibody response (boosting and subsequent waning of antibody levels) retain much of the information inherent in antibody titres that is otherwise lost using threshold or 4-fold rise metrics, enabling improved inference of unobserved single infections [28]. These methods have been applied in a number of human and wildlife disease systems to understand antibody waning rates and population trends in infection [13,[29][30][31][32][33]. However, dynamical models for antigenically variable pathogen systems with the potential for multiple exposures and cross-reactive antibody responses have been somewhat neglected until recently [27,[34][35][36].…”

Enteropathogen seroepidemiology among children in low-resource settings

“…Studies that pair stool-based molecular testing with antibody response could also assess whether approaches to estimate enteropathogen incidence from cross-sectional samples that rely on estimates of antibody decay with time since infection [6,49] could be used among children in low-resource settings. A similar approach was recently described to estimate cholera incidence among all ages [50] , but broader development across enteropathogens and among young children remains an open area of research.…”