end erat. Pellentesque suscipit risus massa, non vestibulum libero euismod feugiat. In hac habitasse platea dictumst. Maecenas rutrum lobortis lobortis. Vestibulum convallis porttitor sem ac ultricies. Mauris volutpat fringilla nisl blandit semper. Proin nec iaculis sem. Aenean neque ipsum, pretium a faucibus non, tincidunt ut sapien \cite{Zhou_1988,Boyer_1998} .
The objective of this to present the Epidemiologic Volatility Index (EVI) and demonstrate its use as an early warning indicator for upcoming surge in the number of cases of ongoing epidemics. Its application in the case of (i) the COVID-19 pandemic, (ii) the yearly influenza epidemics and (iii) the Dengue epidemic in Brazil revealed the universal applicability and efficiency of EVI.

The Epidemiologic Volatility Index

The Epidemiologic Volatility Index (EVI) has been inspired by the use of volatility indices in the stock markets where a mainly negative association has been observed between volatility indices and stocks or stocks' future prices \cite{Fernandes_2014,Brenner_1989} . Stock price volatility indices are based on the standard deviation of historical price data sets. EVI uses the standard deviation \(\left(\sigma_t\right)\) of the observed number of newly reported cases for each of the time units \(i\) within a prespecified fixed time interval \(\left(t\right)\):
\[\sigma_t=\sqrt{\frac{1}{N_t}\sum_{i_t=1}^{N_t}\left(x_{i_t}-\mu_t\right)}\]
Susbsequently the relative change \(\left(RC_{t-1,t}\right)\) of \(\left(\sigma_t\right)\) between two subsequent time intervals \(\left(t-1,t\right)\) is calculated:
\[EVI_{t-1,t}=\frac{\sigma_t-\sigma_{t-1}}{\sigma_t}\]
At each time point of the time series of the recorded number of new cases, if \(EVI_{t-1,t}\) exceeds a prespecified threshold \(\left(c\right)\) it indicates an upcoming surge in the future number of new recorded cases:
\[EVI_{t-1,t} = \begin{cases} EVI_{t-1,t}>=c & \text{expected rise in cases}\\ EVI_{t-1,t}<c & \text{steady number of cases or decline} \end{cases} \]