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31 December 2020
Data Science for COVID-19
The COVID-19 pandemic is affecting the lives of people all around the world. At the same time, understanding the evolution and other features of this disease is encouraging and pushing many scientic communities to focus their research along these lines. Mathematicians, statisticians and data scientists can play a prominent role regarding the comprehension of this global threat, by contributing their analytical and modeling skills to describe possible scenarios, make inferences and design strategies that may be helpful, for the present and future situations, to control the disease spread, avoid health system breakdowns and, eventually, save lives.
With these goals in mind, the Journal of Nonparametric Statistics calls for papers for a special issue on Data Science for COVID-19, to gather together the efforts of experts on the topic and oer their results to enlighten us to find a way out of this world crisis. A list of subjects of interest include prediction
of pandemic evolution, case tracking and forecasting techniques, risk analysis, data quality and under-reporting effects, spatiotemporal disease dynamics, peak and length estimation, analysis of transmission reduction covariates, data-supported eficacy of isolation and lockdown strategies, and any other problem related to COVID-19 that has been addressed with a Data Science approach.
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All papers will be refereed and subject to the same high-quality standards as regular submissions to the Journal of Nonparametric Statistics. This special issue will be published upon completion of the required number of papers, following a fast track process and avoiding any publication queue. Those authors interested in submitting an article for possible publication in this special issue, please visit the webpage https://mc.manuscriptcentral.com/gnst and selecte "Data Science for COVID-19" when submitting your paper.
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