TY - JOUR
T1 - Best practices for estimating and reporting epidemiological delay distributions of infectious diseases
AU - Charniga, Kelly
AU - Park, Sang Woo
AU - Akhmetzhanov, Andrei R.
AU - Cori, Anne
AU - Dushoff, Jonathan
AU - Funk, Sebastian
AU - Gostic, Katelyn M.
AU - Linton, Natalie M.
AU - Lison, Adrian
AU - Overton, Christopher E.
AU - Pulliam, Juliet R.C.
AU - Ward, Thomas
AU - Cauchemez, Simon
AU - Abbott, Sam
N1 - Publisher Copyright:
This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.
PY - 2024/10
Y1 - 2024/10
N2 - AU Epidemiological: Pleaseconfirmthatallheadinglevelsarerepresentedcorrectly delays are key quantities that inform public:health policy and clinical practice. They are used as inputs for mathematical and statistical models, which in turn can guide control strategies. In recent work, we found that censoring, right truncation, and dynamical bias were rarely addressed correctly when estimating delays and that these biases were large enough to have knock-on impacts across a large number of use cases. Here, we formulate a checklist of best practices for estimating and reporting epidemiological delays. We also provide a flowchart to guide practitioners based on their data. Our examples are focused on the incubation period and serial interval due to their importance in outbreak response and modeling, but our recommendations are applicable to other delays. The recommendations, which are based on the literature and our experience estimating epidemiological delay distributions during outbreak responses, 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.
AB - AU Epidemiological: Pleaseconfirmthatallheadinglevelsarerepresentedcorrectly delays are key quantities that inform public:health policy and clinical practice. They are used as inputs for mathematical and statistical models, which in turn can guide control strategies. In recent work, we found that censoring, right truncation, and dynamical bias were rarely addressed correctly when estimating delays and that these biases were large enough to have knock-on impacts across a large number of use cases. Here, we formulate a checklist of best practices for estimating and reporting epidemiological delays. We also provide a flowchart to guide practitioners based on their data. Our examples are focused on the incubation period and serial interval due to their importance in outbreak response and modeling, but our recommendations are applicable to other delays. The recommendations, which are based on the literature and our experience estimating epidemiological delay distributions during outbreak responses, 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.
UR - http://www.scopus.com/inward/record.url?scp=85207563407&partnerID=8YFLogxK
U2 - 10.1371/journal.pcbi.1012520
DO - 10.1371/journal.pcbi.1012520
M3 - Article
C2 - 39466727
AN - SCOPUS:85207563407
SN - 1553-734X
VL - 20
JO - PLoS Computational Biology
JF - PLoS Computational Biology
IS - 10
M1 - e1012520
ER -