  The COVID-19 pandemic demonstrated that fast and accurate analysis of
continually collected infectious disease surveillance data is crucial for
situational awareness and policy making. Coalescent-based phylodynamic analysis
can use genetic sequences of a pathogen to estimate changes in its effective
population size, a measure of genetic diversity. These changes in effective
population size can be connected to the changes in the number of infections in
the population of interest under certain conditions. Phylodynamics is an
important set of tools because its methods are often resilient to the
ascertainment biases present in traditional surveillance data (e.g.,
preferentially testing symptomatic individuals). Unfortunately, it takes weeks
or months to sequence and deposit the sampled pathogen genetic sequences into a
database, making them available for such analyses. These reporting delays
severely decrease precision of phylodynamic methods closer to present time, and
for some models can lead to extreme biases. Here we present a method that
affords reliable estimation of the effective population size trajectory closer
to the time of data collection, allowing for policy decisions to be based on
more recent data. Our work uses readily available historic times between
sampling and sequencing for a population of interest, and incorporates this
information into the sampling model to mitigate the effects of reporting delay
in real-time analyses. We illustrate our methodology on simulated data and on
SARS-CoV-2 sequences collected in the state of Washington in 2021.
