Whose Line Is it Anyway? Defining Seropositivity Cutoffs for Infectious Disease Surveillance

Serological assays are key tools in infectious disease surveillance, enabling detection of past infections by measuring antibody responses. Determining appropriate seropositivity cutoffs, thresholds distinguishing between positive and negative antibody responses, remains a critical methodological challenge.

We present a framework for selecting seropositivity cutoffs based on immunoassay characteristics, availability of validation samples, and intended use case. Four principal methods are evaluated: receiver operating characteristic curves based on confirmed positive and negative samples; the negative sample distribution method when only negative controls are available; the positive sample distribution method for scenarios with only positive controls; and mixture models for samples of unknown serostatus. Each method's assumptions, advantages, and limitations are detailed, and guidance is provided for selecting the most appropriate approach under varying constraints.

The framework is first illustrated using simulated data, and then applied to data from multiplex serological surveys of neglected tropical diseases in the Senegalese villages of Dielmo and Ndiop. We demonstrate that no universal approach suffices across pathogens or populations. Cross-reactive antibody responses, variations in total IgG levels between populations, and assay-specific features can all confound interpretation.

We advocate for a context-dependent, evidence-based selection of cutoff methodologies, informed by panels of confirmed positive and negative samples. As multiplex serological platforms become increasingly central to integrated disease surveillance, robust frameworks for interpreting antibody data are needed. Our proposed framework offers a pragmatic path forward, bridging immunological theory and statistical rigor to improve the reliability and comparability of seroepidemiological insights across diverse epidemiological settings.