Best practices for estimating and reporting epidemiological delay distributions of infectious diseases using public health surveillance and healthcare data

TL;DR

This paper reviews challenges in estimating epidemiological delay distributions and proposes best practices and strategies to improve the accuracy and reporting of these estimates for infectious disease modeling.

Contribution

It introduces a comprehensive checklist of best practices and strategies for estimating and reporting delay distributions, addressing common biases and uncertainties.

Findings

Identified key challenges in delay estimation, including censoring and truncation.

Proposed a checklist of best practices for robust delay estimation.

Highlighted areas needing further research for improved estimates.

Abstract

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.