Building a statistical surveillance dashboard for COVID-19 infection worldwide

Abstract

When a pandemic like the current novel coronavirus (COVID-19) breaks out, it is important that authorities, healthcare organizations and official decision makers, have in place an effective monitoring system to promptly analyze data, create new insights into problematic areas and generate actionable knowledge for fact-based decision making. The aim of this article is to describe an initial work focused on building a comprehensive statistical surveillance dashboard for the epidemic of COVID-19, which can be exploited also for future needs. We propose novel ways of exploring, analyzing and presenting data, using metrics that have not been used previously. We also show the steps necessary to build and operate such a dashboard. As a result of this this work, a set of data exploration and data visualization tools have been proposed which can be instrumental in providing information necessary to manage a crisis like COVID-19 pandemic in a systematic and effective way. The proposed statistical surveillance dashboard can provide formal authorities and other decision makers with valuable insights into problematic areas and help them make critical decisions based on facts and an in-depth data analysis. The dashboard is implemented in an online dedicated website, freely accessible to the readership of this journal.

Keywords:

• Decision analysis
• multiple attribute decision-making
• statistical decision making
• statistical process control
• process monitoring
• statistical thinking

Acknowledgments

The authors thank their own institutions for the possibility to allocate time to do research on the topics addressed in the manuscript. The international collaboration between the authors is not new. Barone and Chakhunashvili did research together and produced several publications since 2003.

Declaration of interest statement

No conflict of interest of any kind is underlying the research work and the publication of the manuscript.

Authors’ contributions

All authors contributed equally to the definition of all steps of the research and in the writing of the manuscript.

Additional information

Notes on contributors

Stefano Barone

Stefano Barone is Associate Professor of Statistics at University of Palermo, Italy. He received his PhD in Applied Statistics in 2000 from the University of Naples (Italy). After having worked for two years as a post doc researcher at ELASIS (FIAT research centre), at University of Naples and then at Chalmers University of Technology, he became Assistant Professor of Statistics at the University of Palermo, Faculty of Engineering. In 2005-2006, he served as council member and Vice President of ENBIS, the European Network for Business and Industrial Statistics of which he was one of the founders. In 2008, he was awarded a Fulbright visiting research grant and worked at the Georgia Institute of Technology (Atlanta, Georgia, USA). From 2009 to 2012, he was Associate Professor and docent of Industrial Statistics at Chalmers University of Technology where he was responsible for the Six Sigma Black Belt education at Master level.

Alexander Chakhunashvili

Alexander Chakhunashvili is a Senior Data Analyst/Statistician at Karolinska University Hospital in Stockholm, Sweden. He earned his PhD in quality sciences from Chalmers University of Technology in Gothenburg, Sweden in 2006. Since then, Dr. Chakhunashvili has worked in the hospital sector focusing on different aspects of healthcare quality improvement. He has also been involved in numerous research projects studying and analyzing healthcare data as well as teaching quality improvement methods to healthcare professionals and students at several universities and health systems both in Sweden and abroad.

Albert Comelli

Albert Comelli is a Research/Scientist in Biomedical Image Processing and Analysis at the Ri.MED Foundation. Research Affiliate Long Term at the Laboratory of Computational Computer Vision (LCCV) in the School of Electrical and Computer Engineering at Georgia Institute of Technology, Atlanta, Georgia. He received the PhD in Computer Engineering at the University of Palermo, Combined BSc/MSc degree in Computer Science at the University of Catania. His research interests include medical image processing and medical data analysis.