A scalable maximum-likelihood framework for near-real-time monitoring of MERS-CoV evolutionary and zoonotic dynamics

Understanding the drivers of viral spillover is critical for public health, yet phylodynamic inferences can be sensitive to the analytical methods used. Here, we use a comparative framework of four independent maximum-likelihood methods to analyze 643 MERS-CoV genomes sampled through January 2024. Our results confirm that recurrent, independent zoonotic transmissions from dromedary camels are the primary driver of MERS-CoV emergence, with all spillover events tracing back to Saudi Arabia and the United Arab Emirates. While all methods consistently reconstruct viral circulation within camels on the Arabian Peninsula, they yield significant discrepancies in key epidemiological estimates, with the inferred number of camel-to-human spillover events ranging from 15 to 34 events depending on the tool used. The utility of our framework is its ability to quantify this methodological uncertainty, providing a more robust assessment of zoonotic risk than any single maximum-likelihood tool could alone. Therefore, we propose a two-tiered surveillance strategy that combines rapid real-time tracking to identify new clusters with periodic, in-depth validation using a multi-method consensus approach to guide long-term public health interventions at key human-animal interfaces.