The convergence epidemic volatility index (cEVI) as an alternative early warning tool for identifying waves in an epidemic

Konstantinos Pateras*, Eleftherios Meletis, Matthew Denwood, Paolo Eusebi, Polychronis Kostoulas

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

1 Citation (Scopus)
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Abstract

This manuscript introduces the convergence Epidemic Volatility Index (cEVI), a modification of the recently introduced Epidemic Volatility Index (EVI), as an early warning tool for emerging epidemic waves. cEVI has a similar architectural structure as EVI, but with an optimization process inspired by a Geweke diagnostic-type test. Our approach triggers an early warning based on a comparison of the most recently available window of data samples and a window based on the previous time frame. Application of cEVI to data from the COVID-19 pandemic data revealed steady performance in predicting early, intermediate epidemic waves and retaining a warning during an epidemic wave. Furthermore, we present two basic combinations of EVI and cEVI: (1) their disjunction cEVI + that respectively identifies waves earlier than the original index, (2) their conjunction cEVI- that results in higher accuracy. Combination of multiple warning systems could potentially create a surveillance umbrella that would result in early implementation of optimal outbreak interventions.

Original languageEnglish
JournalInfectious Disease Modelling
Volume8
Issue number2
Pages (from-to)484-490
Number of pages7
ISSN2468-0427
DOIs
Publication statusPublished - 2023

Bibliographical note

Publisher Copyright:
© 2023 The Authors

Keywords

  • Convergence diagnostics
  • Early warning
  • Epidemic index
  • Surveillance system
  • Time-series

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