  Epidemiological delays, such as incubation periods, serial intervals, and
hospital lengths of stay, are among key quantities in infectious disease
epidemiology that inform public health policy and clinical practice. This
information is used to inform mathematical and statistical models, which in
turn can inform control strategies. There are three main challenges that make
delay distributions difficult to estimate. First, the data are commonly
censored (e.g., symptom onset may only be reported by date instead of the exact
time of day). Second, delays are often right truncated when being estimated in
real time (not all events that have occurred have been observed yet). Third,
during a rapidly growing or declining outbreak, overrepresentation or
underrepresentation, respectively, of recently infected cases in the data can
lead to bias in estimates. Studies that estimate delays rarely address all
these factors and sometimes report several estimates using different
combinations of adjustments, which can lead to conflicting answers and
confusion about which estimates are most accurate. In this work, we formulate a
checklist of best practices for estimating and reporting epidemiological delays
with a focus on the incubation period and serial interval. We also propose
strategies for handling common biases and identify areas where more work is
needed. Our recommendations can help improve the robustness and utility of
reported estimates and provide guidance for the evaluation of estimates for
downstream use in transmission models or other analyses.
