Issue #408: Update to PLOS CB version of Charniga et al. (#409)

README.Rmd

@@ -145,7 +145,7 @@ If using our methodology, or the methodology on which ours is based, please cite
 This may include:
 
 * [Estimating epidemiological delay distributions for infectious diseases](https://www.medrxiv.org/content/10.1101/2024.01.12.24301247v1) by Park *et al.* (2024)
-* [Best practices for estimating and reporting epidemiological delay distributions of infectious diseases using public health surveillance and healthcare data](https://arxiv.org/abs/2405.08841) by Charniga *et al.* (2024)
+* [Best practices for estimating and reporting epidemiological delay distributions of infectious diseases using public health surveillance and healthcare data](https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1012520) by Charniga *et al.* (2024)
 
 ## Contributors
 

README.md

@@ -164,13 +164,13 @@ citation("epidist")
 To cite package 'epidist' in publications use:
 
   Adam Howes, Park S, Sam Abbott (NULL). _epidist: Estimate
-  Epidemiological Delay Distributions With brms_.
+  epidemiological delay distributions for infectious diseases_.
   doi:10.5281/zenodo.5637165 <https://doi.org/10.5281/zenodo.5637165>.
 
 A BibTeX entry for LaTeX users is
 
   @Manual{,
-    title = {epidist: Estimate Epidemiological Delay Distributions With brms},
+    title = {epidist: Estimate epidemiological delay distributions for infectious diseases},
     author = {{Adam Howes} and Sang Woo Park and {Sam Abbott}},
     year = {NULL},
     doi = {10.5281/zenodo.5637165},
@@ -187,8 +187,9 @@ please cite the relevant papers. This may include:
   by Park *et al.* (2024)
 - [Best practices for estimating and reporting epidemiological delay
   distributions of infectious diseases using public health surveillance
-  and healthcare data](https://arxiv.org/abs/2405.08841) by Charniga *et
-  al.* (2024)
+  and healthcare
+  data](https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1012520)
+  by Charniga *et al.* (2024)
 
 ## Contributors
 

vignettes/references.bib

@@ -11,12 +11,18 @@ @article {park2024estimating
 }
 
 @article{charniga2024best,
-      title={Best practices for estimating and reporting epidemiological delay distributions of infectious diseases using public health surveillance and healthcare data}, 
-      author={Kelly Charniga and Sang Woo Park and Andrei R Akhmetzhanov and Anne Cori and Jonathan Dushoff and Sebastian Funk and Katelyn M Gostic and Natalie M Linton and Adrian Lison and Christopher E Overton and Juliet R C Pulliam and Thomas Ward and Simon Cauchemez and Sam Abbott},
-      year={2024},
-      eprint={2405.08841},
-      archivePrefix={arXiv},
-      primaryClass={stat.ME}
+    doi = {10.1371/journal.pcbi.1012520},
+    author = {Charniga, Kelly and Park, Sang Woo and Akhmetzhanov, Andrei R. and Cori, Anne and Dushoff, Jonathan and Funk, Sebastian and Gostic, Katelyn M. and Linton, Natalie M. and Lison, Adrian and Overton, Christopher E. and Pulliam, Juliet R. C. and Ward, Thomas and Cauchemez, Simon and Abbott, Sam},
+    journal = {PLOS Computational Biology},
+    publisher = {Public Library of Science},
+    title = {Best practices for estimating and reporting epidemiological delay distributions of infectious diseases},
+    year = {2024},
+    month = {10},
+    volume = {20},
+    url = {https://doi.org/10.1371/journal.pcbi.1012520},
+    pages = {1-21},
+    abstract = {Epidemiological 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.},
+    number = {10},
 }
 
 @Article{brms,