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

Abstract read

In one paragraph

Article in Infectious Disease Modelling, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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1citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

The convergence epidemic volatility index (cEVI) as an alternative early warning tool for identifying waves in an epidemic.Infectious Disease Modelling · 2023
Article

4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Konstantinos PaterasDepartment of Public and One Health, School of Medicine, University of Thessaly, Karditsa, Terma Mavromichali St., 43131, Greece.

Eleftherios MeletisDepartment of Public and One Health, School of Medicine, University of Thessaly, Karditsa, Terma Mavromichali St., 43131, Greece.

Matthew DenwoodDepartment of Veterinary and Animal Sciences, University of Copenhagen, Grønnegårdsvej 8, 1870, Frederiksberg, Copenhagen, Denmark.

Paolo EusebiDepartment of Medicine and Surgery, University of Perugia, Via Gambuli, 1, 06132, Perugia, Italy.

Polychronis KostoulasDepartment of Public and One Health, School of Medicine, University of Thessaly, Karditsa, Terma Mavromichali St., 43131, Greece.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

Convergence diagnosticsEarly warningEpidemic indexSurveillance systemTime-series

Identifiers

PMID37234097

DOI10.1016/j.idm.2023.05.001

PMCPMC10206801

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