Comparing Cross-Sectional and Longitudinal Study Designs for Accurate Viral Dynamics Estimation: Insights From the...

Area of Science:

• Infectious Disease Epidemiology
• Computational Biology
• Virology

Background:

• Viral load data are crucial for understanding host-pathogen interactions and informing clinical decisions.
• Frequent viral load testing is often impractical, necessitating the use of viral dynamics models to infer infection trajectories.
• Optimal strategies for collecting viral load data remain undefined.

Purpose of the Study:

• To compare the accuracy and precision of longitudinal versus cross-sectional sampling for estimating SARS-CoV-2 viral load dynamics.
• To evaluate the impact of sampling strategy on key viral dynamic parameters, including peak viral load, timing, and shedding duration.
• To determine efficient study designs for viral load data collection under resource constraints.

Main Methods:

• A viral dynamics model was fitted to existing SARS-CoV-2 data to establish ground truth parameters.
• Synthetic viral load data were generated using both longitudinal (every 3 days) and cross-sectional (single time point) sampling designs.
• Models were refitted to synthetic data to assess accuracy in estimating viral load over 30 days, peak load, peak time, and shedding duration.

Main Results:

• Longitudinal sampling consistently demonstrated lower root mean squared error and narrower standard deviation intervals compared to cross-sectional sampling.
• Cross-sectional designs tended to underestimate peak viral load and resulted in wider intervals.
• Coverage of viral load estimates was significantly higher with longitudinal designs (>0.90) versus cross-sectional designs (~0.10).
• High accuracy and coverage (>0.96) were achieved with as few as two longitudinal tests per individual.

Conclusions:

• Longitudinal sampling strategies, even with limited data points, substantially enhance the accuracy and precision of viral load estimation.
• These findings provide evidence for efficient study designs and resource allocation in infectious disease research.
• Longitudinal sampling is recommended for more robust reconstruction of infection trajectories and accurate parameter estimation.