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.
Bayesian, diagnosis, latent class, split component synthesis Meta-analyses of diagnostic accuracy studies are a fundamental component of evidence-based medicine, and they are extensively used in medical imaging and the clinical laboratory. Techniques specifically developed to combine independent studies of diagnostic accuracy and provide pooled estimates for sensitivity (Se), specificity (Sp), positive (pLR) and negative (nLR) likelihood ratios are relatively new. In 2001, Rutter and Gatsonis proposed the hierarchical summary receiver operating characteristic (HSROC) model, 1 and in 2004 Macaskill described an empirical Bayes approach. 2 Soon after, in 2005, Reitsma et al. pro-
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.
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