Find relationship between parameter values and OxCGRT scores

[lisphilar]

We will discussion to find the relationship between parameter values and OxCGRT scores. This idea was discussed in #3 as follows.

Because COVID-19 crisis is ongoing, we take (should take) measures and it is difficult to find y=f(t) where t is time and y is parameter value.
However, it may be possible to find y=f(measures) by

• estimation of parameters of all phases in many countries
• gathering information about measures, including lockdown, new medicines and physical distancing
• learning the relationship of measures and parameter values

For learning the relationship of measures and parameters, tslearn Python package may be useful. With tslearn, we can make clusters of time series data (Rt/parameters of SIR-like models of each countries) and find patterns of time evolution.

For each pattern, we will try to create y=f(effective date of measures).

[lisphilar]

OxCGRT scres:
Government responses are tracked with Oxford Covid-19 Government Response Tracker (OxCGRT). Because government responses and activities of persons change the parameter values of SIR-derived models, this dataset is significant when we try to forcast the number of cases.
(This is documented in https://lisphilar.github.io/covid19-sir/usage_dataset.html#Government-Response-Tracker-(OxCGRT))

[lisphilar]

PolicyMeasures class was created for this purpose as documented in https://lisphilar.github.io/covid19-sir/usage_policy.html

As discussed in #204 and #205, the following methods will be useful.

• Perform time-series clustering of history of parameter values to find patterns.
• Calculate the delay to see the effect of policies on parameter values.