help evi

Title

evi -- Epidemic Volatility Index (EVI) for detecting epidemic waves

Syntax

evi varlist [if] [in] [, options]

varlist should contain two variables and be entered in the following order: cases and time

Description

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 ('c').
Issuance of consecutive early warnings is a strong indication of an upcoming epidemic wave.
EVI is calculated for a rolling window of time series epidemic data ('lag'). At each step, the observations within the window are obtained by shifting the window forward over the time series data one observation at a time.
The user should provide the minimum rise in mean cases between two consecutive weeks ('r') that, if present, should be detected.

Modules required

Users need to install diagt and rangestat

Options

lag(#1 #2) specifies the range of 'lag'. It is not recommended to use values ≤ 6 (by default the range is 7 to 10).

c(#1 #2) specifies the range of 'c' (by default the range is 0.01 to 0.05).

NOTE: It is recommended when running EVI in a server to specify the range for 'lag' from 7 to 28 and for 'c' from 0.01 to 0.5.

cumulative specifies that cases contain cumulative number of cases, rather than new cases per unit of time.

r(#) specifies the cut-off value for 'r' (by default it is 1.2, indicating an increase in the mean of cases of ≥ 20%).

mov(#) specifies the size of the moving average of cases (by default it is 7, to estimate weekly moving average of cases).

Selection of best lag and c to estimate evi

youden specifies that the selection is done based on the highest Youden's J statistic (default).

sensitivity(#) specifies the maximum sensitivity desired to select 'lag' and 'c'.

specificity(#) specifies the maximum specificity desired to select 'lag' and 'c'.

Time series graph and saved data

logarithmic displays the number of cases (x-axis) in the time series in the log10 scale.

nograph suppresses the time series graph.

norsample do not add new variables (_status _lag _c _sens _spec _youden _evi) to the dataset.

Examples

The data for the example is taken from the COVID-19 Data Repository maintained by the Center for Systems Science and Engineering at the Johns Hopkins University (https://github.com/CSSEGISandData/COVID-19).

. use http://fmwww.bc.edu/repec/bocode/e/evi_example_data.dta

EVI for the first 150 days of cumulative COVID-19 data from Italy.

Note: It takes approximately 5 minutes to complete the command

. evi cases day, cumulative log

Authors

Luis Furuya-Kanamori, UQ Centre for Clinical Research, The University of Queensland, Australia

l.furuya@uq.edu.au

Polychronis Kostoulas, Faculty of Public Health, University of Thessaly, Greece

pkost@uth.gr

References

Kostoulas P et al. The Epidemic Volatility Index: an early warning tool for epidemics. DOI:10.22541/au.161918947.77588494/v2

The Epidemic Volatility Index: Predictions for COVID-19. http://83.212.174.99:3838/

Funding

LFK was supported by an Australian National Health and Medical Research Council Fellowship (APP1158469).