Accounting for reporting delays in real-time phylodynamic analyses with preferential sampling

Area of Science:

• Epidemiology
• Computational Biology
• Genetics

Background:

• Accurate infectious disease surveillance is vital for public health policy.
• Phylodynamic analysis uses pathogen genetic data to track epidemics but is hampered by reporting delays.
• Traditional surveillance data can have biases, such as preferential testing of symptomatic individuals.

Purpose of the Study:

• To develop a phylodynamic method that provides reliable estimation of effective population size closer to data collection.
• To mitigate the impact of reporting delays on real-time phylodynamic analyses.
• To enable more timely public health policy decisions based on recent epidemiological data.

Main Methods:

• Developed a novel phylodynamic method incorporating historical sampling-to-reporting times into the sampling model.
• Utilized coalescent-based phylodynamics to estimate effective population size (a proxy for genetic diversity).
• Applied the methodology to simulated data and SARS-CoV-2 sequences from Washington State in 2021.

Main Results:

• The new method provides reliable estimation of effective population size trajectories closer to the time of data collection.
• The approach successfully mitigates the negative effects of reporting delays on real-time phylodynamic analyses.
• Demonstrated the methodology's effectiveness on both simulated and real-world SARS-CoV-2 surveillance data.

Conclusions:

• This phylodynamic method enhances real-time infectious disease surveillance by overcoming reporting delays.
• The improved accuracy allows for more informed and timely public health policy and situational awareness.
• Phylodynamics offers a robust tool for epidemic tracking, especially when integrated with methods addressing data limitations.