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1. Utilization of COVID-19 Treatments and Clinical Outcomes among Patients with Cancer: A COVID-19 and Cancer Consortium (CCC19) Cohort Study

   https://doi.org/10.1158/2159-8290.CD-20-0941  

   Rivera, Donna R. ; Peters, Solange ; Panagiotou, Orestis A. ; Shah, Dimpy P. ; Kuderer, Nicole M. ; Hsu, Chih-Yuan ; Rubinstein, Samuel M. ; Lee, Brendan J. ; Choueiri, Toni K. ; de Lima Lopes, Gilberto ; et al ( October 2020 , Cancer Discovery)

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2. How do we share data in COVID-19 research? A systematic review of COVID-19 datasets in PubMed Central Articles

   https://doi.org/10.1093/bib/bbaa331  

   Zuo, Xu ; Chen, Yong ; Ohno-Machado, Lucila ; Xu, Hua ( December 2020 , Briefings in Bioinformatics)

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   Abstract Objective This study aims at reviewing novel coronavirus disease (COVID-19) datasets extracted from PubMed Central articles, thus providing quantitative analysis to answer questions related to dataset contents, accessibility and citations. Methods We downloaded COVID-19-related full-text articles published until 31 May 2020 from PubMed Central. Dataset URL links mentioned in full-text articles were extracted, and each dataset was manually reviewed to provide information on 10 variables: (1) type of the dataset, (2) geographic region where the data were collected, (3) whether the dataset was immediately downloadable, (4) format of the dataset files, (5) where the dataset was hosted, (6) whether the dataset was updated regularly, (7) the type of license used, (8) whether the metadata were explicitly provided, (9) whether there was a PubMed Central paper describing the dataset and (10) the number of times the dataset was cited by PubMed Central articles. Descriptive statistics about these seven variables were reported for all extracted datasets. Results We found that 28.5% of 12 324 COVID-19 full-text articles in PubMed Central provided at least one dataset link. In total, 128 unique dataset links were mentioned in 12 324 COVID-19 full text articles in PubMed Central. Further analysis showed that epidemiological datasets accounted for the largest portion (53.9%) in the dataset collection, and most datasets (84.4%) were available for immediate download. GitHub was the most popular repository for hosting COVID-19 datasets. CSV, XLSX and JSON were the most popular data formats. Additionally, citation patterns of COVID-19 datasets varied depending on specific datasets. Conclusion PubMed Central articles are an important source of COVID-19 datasets, but there is significant heterogeneity in the way these datasets are mentioned, shared, updated and cited. 

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3. Effect of Physician-Delivered COVID-19 Public Health Messages and Messages Acknowledging Racial Inequity on Black and White Adults’ Knowledge, Beliefs, and Practices Related to COVID-19: A Randomized Clinical Trial

   https://doi.org/10.1001/jamanetworkopen.2021.17115  

   Torres, Carlos ; Ogbu-Nwobodo, Lucy ; Alsan, Marcella ; Stanford, Fatima Cody ; Banerjee, Abhijit ; Breza, Emily ; Chandrasekhar, Arun G. ; Eichmeyer, Sarah ; Karnani, Mohit ; Loisel, Tristan ; et al ( July 2021 , JAMA Network Open)
4. PIN68 COVID-19 Simulator: An Interactive Tool to Inform COVID-19 Intervention Policy Decisions in the United States

   https://doi.org/10.1016/j.jval.2020.08.909  

   Chhatwal, J. ; Dalgic, O. ; Mueller, P. ; Adee, M. ; Xiao, Y. ; Ladd, M.A. ; Linas, B.P. ; Ayer, T. ( December 2020 , Value in Health)
5. The Association Between COVID-19 Mortality And The County-Level Partisan Divide In The United States: Study examines the association between COVID-19 mortality and county-level political party affiliation.

   https://doi.org/10.1377/hlthaff.2022.00085  

   Sehgal, Neil Jay ; Yue, Dahai ; Pope, Elle ; Wang, Ren Hao ; Roby, Dylan H. ( June 2022 , Health Affairs)