The epidemic volatility index, a novel early warning tool for identifying new waves in an epidemic.
Sci Rep
; 11(1): 23775, 2021 12 10.
Article
in English
| MEDLINE | ID: covidwho-1565730
ABSTRACT
Early warning tools are crucial for the timely application of intervention strategies and the mitigation of the adverse health, social and economic effects associated with outbreaks of epidemic potential such as COVID-19. This paper introduces, the Epidemic Volatility Index (EVI), a new, conceptually simple, early warning tool for oncoming epidemic waves. EVI is based on the volatility of newly reported cases per unit of time, ideally per day, and issues an early warning when the volatility change rate exceeds a threshold. Data on the daily confirmed cases of COVID-19 are used to demonstrate the use of EVI. Results from the COVID-19 epidemic in Italy and New York State are presented here, based on the number of confirmed cases of COVID-19, from January 22, 2020, until April 13, 2021. Live daily updated predictions for all world countries and each of the United States of America are publicly available online. For Italy, the overall sensitivity for EVI was 0.82 (95% Confidence Intervals 0.75; 0.89) and the specificity was 0.91 (0.88; 0.94). For New York, the corresponding values were 0.55 (0.47; 0.64) and 0.88 (0.84; 0.91). Consecutive issuance of early warnings is a strong indicator of main epidemic waves in any country or state. EVI's application to data from the current COVID-19 pandemic revealed a consistent and stable performance in terms of detecting new 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 swiftly and thereby enhance containment of outbreaks.
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
Pandemics
/
COVID-19
Type of study:
Diagnostic study
/
Observational study
/
Prognostic study
Limits:
Humans
Country/Region as subject:
North America
/
Europa
Language:
English
Journal:
Sci Rep
Year:
2021
Document Type:
Article
Affiliation country:
S41598-021-02622-3
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