Fig 1.
Example of an effective population size trajectory (top figure) and corresponding genealogy (bottom figure), with reporting probabilities and corresponding unobserved tips denoted by purple coloring.
For a real-time analysis the reporting probability decreases as the collection date gets closer to present time, time zero.
Fig 2.
Simulation details: effective population trajectories (upper left plot), reporting probability by sampling time (upper right plot) obtained from the Washington state data, and histograms of sampling times from the last simulation of in each simulation scenario, approximately 1500 samples each, colored by whether sample was reported by time of analysis (bottom plots).
Each simulation scenario had a different time zero, i.e., time of latest sample (dashed lines). The earliest sampling time in each scenario was at the same point in the trajectory (dotted line).
Fig 3.
Comparison of real-time phylodynamic methods to infer the effective population size trajectory for a single simulation in scenario C, with reporting delays and preferential sampling present.
Median estimates of the effective population size and 95% credible intervals are plotted. The BNPR PS model was also applied retrospectively with all of the sampled sequences, regardless of if they were reported by time zero, to serve as a reference for comparison for the three real-time inference methods. The white background indicates the recent time period likely suffering from reporting delays, specifically where reporting probabilities (RPs) are below 90%, and is therefore the region of interest.
Fig 4.
Seven-day moving averages of the mean relative deviation, mean percent coverage, and mean interval width by the 95% Bayesian credible intervals, for each real-time phylodynamic strategies to infer the effective population size in each simulation scenario with preferential sampling (PS) and reporting delays in the observed data.
Real-time inference was performed with the Bayesian nonparametric phylodynamic reconstruction (BNPR) model, BNPR PS model, and with the delay-aware BNPR PS model with 500 simulations per scenario.
Fig 5.
Left plot is the seven-day rolling average of positive COVID-19 tests per 100,000 people in the state of Washington.
Middle panel shows number of SARS-CoV-2 genetic samples collected in Washington state, colored by whether the sample was reported by the time of analysis, August 1, 2021. Right panel shows empirical cumulative distribution of reporting delays from the month prior to time of analysis.
Fig 6.
Bayesian nonparametric phylodynamic reconstruction (BNPR) methods used to infer effective population size trajectory for SARS-CoV-19 in Washington state.
Each panel shows the inference from a real-time analysis on data suffering from reporting delays and from a retrospective analysis with completely reported data. The white background indicates the recent time period likely suffering from reporting delays.