Abstract
With case numbers exceeding 97,000 worldwide, the 2022 global mpox outbreak underscored the potential for zoonotic diseases with limited human-to-human transmission to trigger a widespread health crisis. Primarily men who have sex with men (MSM) were affected. Monitoring mpox-specific seroprevalences through epidemiological studies is essential, but challenging due to the cross-reactive antibody immune response which is induced by several orthopoxviruses including modified vaccinia virus Ankara (MVA)-based vaccines, which were used to help bring the outbreak under control. Here we show how machine learning (ML)-guided analysis of a serological multiplex assay that targets 15 immunogenic poxvirus proteins derived from monkeypox virus, vaccinia, and cowpox virus, can confidently discern between sera from patients post-mpox infection, post-MVA immunization, and pre-immunization or infection. Mean F1 scores representing the geometric means between precision and recall were calculated as metrics for the performance of six different ML models. The models were trained and tested on panels containing both sera taken in the early phase of seroconversion as well as sera taken six months after the peak of the mpox outbreak from individuals in an at-risk MSM population in Berlin. Scores ranged between 0.60 ± 0.05 and 0.81 ± 0.02 with Gradient Boosting Classifier (GBC) being the best performing algorithm. In order to ensure high confidence in our results, which is imperative in epidemiological studies, we excluded ambiguous results by using the robustly performing linear discriminant analysis’ (mean F1 scores 0.80 ± 0.02) classification confidence as a threshold. Hereby, sera with uncertain serostatus were segregated, leading to confident predictions with F1 scores above 0.90, at the cost of more inconclusive results for samples below the threshold. Beyond providing a valuable tool for monitoring mpox-specific antibodies, our work demonstrates how the combination of machine learning and multiplexing enables precise differentiation — and a deepened understanding — of complex antibody responses to closely related viruses.