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The Epidemic Volatility Index: an early warning tool for epidemics
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  • Polychronis Kostoulas,
  • Eletherios Meletis,
  • Konstantinos Pateras,
  • Paolo Eusebi,
  • Theodoros Kostoulas,
  • Niko Speybroeck,
  • Matthew Denwood,
  • Suhail A.R. Doi,
  • Christian L. Althaus,
  • Carsten Kirkeby,
  • Pejman Rohani,
  • Navneet K. Dhand,
  • José L. Peñalvo,
  • Luis Furuya-Kanamori,
  • Lehana Thabane,
  • Slimane BenMiled,
  • Hamid Sharifi,
  • Stephen Walter
Polychronis Kostoulas
Faculty of Public Health, University of Thessaly, Greece.

Corresponding Author:polychronis.kostoulas@gmail.com

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Eletherios Meletis
Faculty of Public Health, University of Thessaly, Greece.
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Konstantinos Pateras
Faculty of Public Health, University of Thessaly, Greece.
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Paolo Eusebi
Department of Medicine and Surgery, University of Perugia.
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Theodoros Kostoulas
Department of Information and Communication Systems Engineering, University of the Aegean, Greece
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Niko Speybroeck
Research Institute of Health and Society (IRSS), Université Catholique de Louvain, 1200, Brussels, Belgium.
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Matthew Denwood
Department of Veterinary and Animal Sciences, University of Copenhagen, Denmark
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Suhail A.R. Doi
Department of Population Medicine, College of Medicine, QU Health, Qatar University, Doha, Qatar.
Christian L. Althaus
Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland.
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Carsten Kirkeby
Department of Veterinary and Animal Sciences, University of Copenhagen, Denmark
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Pejman Rohani
Center for the Ecology of Infectious Diseases, University of Georgia, Athens, GA 30602, USA.
Navneet K. Dhand
Sydney School of Veterinary Science, The University of Sydney, Camden, NSW, Australia.
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José L. Peñalvo
Unit of Noncommunicable Diseases, Department of Public Health, Institute of Tropical Medicine, Antwerp, Belgium.
Luis Furuya-Kanamori
UQ Centre for Clinical Research, Faculty of Medicine, The University of Queensland, Herston, Australia.
Lehana Thabane
Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton ON, Canada.
Slimane BenMiled
Pasteur Institute, University of Tunis el Manar, Tunis, Tunisia.
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Hamid Sharifi
HIV/STI Surveillance Research Center, and WHO Collaborating Center for HIV Surveillance, Institute for Futures Studies in Health, Kerman University of Medical Sciences, Kerman, Iran.
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Stephen Walter
Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton ON, Canada.

Abstract

Background. This paper presents, for the first time, the Epidemic Volatility Index (EVI), a conceptually simple, early warning tool for emerging epidemic waves.  
Methods. EVI is based on the volatility of the newly reported cases per unit of time, ideally per day, and issues an early warning when the rate of the volatility change exceeds a threshold.
Results. Results from the COVID-19 epidemic in Italy and New York are presented here, while daily updated predictions for all world countries and each of the United States are available online.
Interpretation. EVI's application to data from the current COVID-19 pandemic revealed a consistent and stable performance in terms of detecting oncoming waves. The application of EVI to other epidemics and syndromic surveillance tasks in combination with existing early warning systems will enhance our ability to act fast and optimize containment of outbreaks.