In response to the COVID-19 pandemic, The Canadian Journal of Statistics (CJS) is encouraging submissions on statistical methods and theory, with applications to aspects of COVID-19. This includes research on the design and analysis of COVID-19 studies and databases, and challenges posed by COVID-19 data.
We invite the submission of research articles, reviews, or focussed discussions on a broad range of topics, including the design and analysis of specific types of studies, the analysis of disease surveillance data, and statistical issues related to demographic, epidemiological and genetic studies of the disease, and to the impact of the pandemic on public health and the economy. In addition to novel statistical ideas, submissions should contain insightful analysis of some aspect of COVID-19. Papers that arise from collaborations with public health researchers and other scientists are especially encouraged.
We had originally called for submissions before December 31, 2020, with a view to a special issue on COVID-19. Given the time needed for studies to reach maturity and statistical ideas to be developed, we are modifying these plans. Papers may be submitted for the foreseeable future, and as papers are accepted CJS plans to highlight submissions in special sections on COVID-19 in subsequent issues. A speedy review will be given to submissions on COVID-19, according to the usual CJS criteria. Please submit your paper through the CJS submission website, specifying that the submission is for the special issue “COVID-19 Section”:
http://mc.manuscriptcentral.com/cjs-wiley
The lead Guest Editor for papers on COVID-19 is Jerry Lawless (University of Waterloo), and co-Guest Editors are Don Estep (Simon Fraser University), Nancy Heckman (University of British Columbia), Eleanor Pullenayegum (University of Toronto), Lei Sun (University of Toronto) and Denis Talbot (Université Laval).