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

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.