Pandemic Impact Analysis
CEE Recovery Index

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How is the CEE Recovery Index calculated?

The CEE Recovery Index is a weekly indicator that tracks how much of the gap caused by the pandemic outbreak in different areas of economic activity has already been closed since mid-April. The following sub-indices are included in the calculation: energy consumption (weekly annual change), air pollution (weekly mean of annual change), mobility in the categories groceries (weekly mean change to baseline), retail and recreation (weekly mean change to baseline), as well as workplace (weekly mean change to baseline), and capacity in the automotive sector (weekly value).

All the values are normalized between 0 and 1 and the CEE-weighted average for each category is calculated. In order to obtain the final number, a simple average is calculated over the included categories. 

Sources:  Mobility trends

                 Electricity consumption

                 Air pollution

CEE Countries included:

  •  Croatia
  •  Czechia
  •  Hungary
  •  Poland
  •  Romania
  •  Slovakia
CEE Region
R-Factors & New COVID-19 Cases

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How is the R-Factor estimated?

The approach is based on computations of AGES (Austrian Agency for Health and Food Safety) where the effective reproduction number is estimated via the time series of new infections and the serial interval, the average duration between the disease onset of individual i and the disease onset of other individuals, caused by individual i. The serial interval was estimated to have a mean of 4.46 and a standard deviation of 2.63. An Rt of 0.5 would mean that the number of new infections would halve within 4.46 days.

Thus, while an Rt <1 means that the number of new infections is contracting and expected to abate, an Rt >1 represents rising new infections and is an indicator for governments to tighten measures against COVID-19.

Please note that the presented estimates of Rt can deviate from numbers published by other institutions, such as figures by the  Robert Koch Institute for Germany, due to differences in the computational approach described above, the fact that R is estimated on a weekly sliding window and deviations in the data source.

Datasource New-Infections: John Hopkins University

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