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.

Accurately tracking zoonotic spillover is essential for public health, yet rapid genomic tools can yield a range of epidemiological estimates. While full Bayesian phylodynamic models can comprehensively capture uncertainty, they are often too computationally intensive for practical use during a real-time outbreak response. Our study demonstrates a powerful and efficient alternative, using a multi-method maximum-likelihood framework to analyze MERS-CoV, a high-priority pathogen. This approach allows us to quantify significant methodological uncertainty without the computational overhead. Applying this to MERS-CoV, we confirm that recurrent camel-to-human transmissions are the primary driver of emergence, while also showing how key estimates like spillover frequency can vary between methods. Our work provides a practical surveillance framework that balances speed with analytical rigor, offering a robust path for monitoring MERS-CoV and future zoonotic threats.