# Disclaimer

This application is fetching data from the sources described below (namely italian dpc for italian data, and Hopkins University for world data). Only a very simplistic and objective analysis is performed: see the explanations given below before giving any significance to the data plotted. You are encouraged to report any problem by opening an issue on github.

- Data source:
- data collected from here
- Data set:
- Series:

- data collected from here
- Data set:
- Series:

- data has been extracted from here aura vision lockdown tracker
- Series:

- Smoothing:
- Press button:

# Explanation

From each series *y=y(t)* we consider as many data points in the past as specified by the user
up to the date specified (or today if no date is specified in the "up to date" input field).
We compute the linear regression of the logarithms of the values: *log(y) = mt + q*.
This gives the exponential fit curve *y=exp(mt+q)*.
The *average daily increase* is the average daily percentual increase: *exp(m)-1*.
The *origin* is the time when the exponential fit has a unit value: *t=-q/m*.
The *growth rate* plot shows the percentual increase in each day with respect to the previous day.
The *growth rate smooth* smooth also adds a smoothing filter.
The *time alignment* selection translates each curve in time with respect to the first curve
plotted.
If *prediction shift* is selected, each curve is shifted by the number of days required to reach
the level of the first curve following the current exponential trend.
If *history shift* is selected, each curve is shifted by the number of days in the past
when the first curve had the same value (when the first curve is larger than the current) or
in the future when it has the same value of the first curve (when the first curve is smaller than the current).
If you press the button *set url for this chart* the url of the page is changed
so that you can save or share a link to the current chart.
To perform your own analysis you can download the data in CSV format.