Model design for non-parametric phylodynamic inference and applications to pathogen surveillance

Area of Science:

• Genomics
• Epidemiology
• Statistical Modeling

Background:

• Effective population size inference from genomic data offers insights into demographic and epidemiological dynamics.
• Phylodynamic inference combines non-parametric population models with molecular clock models for time-stamped genetic data.
• Existing non-parametric effective population size inference methods are primarily Bayesian.

Approach:

• Developed a frequentist approach for phylodynamic inference using non-parametric latent process models.
• Optimized model parameters based on out-of-sample prediction accuracy for population size dynamics.
• Applied the methodology to reconstruct cholera pandemic waves and estimate COVID-19 intervention impacts.

Key Points:

• Demonstrated flexibility and speed through simulation experiments.
• Reconstructed historical epidemic waves, including the seventh cholera pandemic.
• Estimated the impact of non-pharmaceutical interventions on SARS-CoV-2 transmission in England.

Conclusions:

• The novel frequentist method provides a fast and flexible tool for phylodynamic inference.
• The approach successfully models population dynamics and quantifies the effects of public health interventions.
• This work advances the application of genomic data in understanding epidemic spread and control.