In the above graph, we display what proportion of the population was vaccinated in countries around the world during the pandemic. We calculated our own vaccination index using weighted average of total singly and doubly vaccinated people as a proportion of the country's population to bring the values to a uniform scale.
Cross Correlation Function Plot between Vaccination Index and COVID Severity
Cross-Correlation plot between the vaccination index and covid severity is the correlation plot between both de-trended time series at different lags of covid severity. This plot helps us understand how long it takes for changes in vaccination index to take effect on covid severity or vice versa. The red lines indicates the significance interval, which means correlation values more than that threshold can be considered significant. The plot shows us that there is maximum interaction between covid severity and vaccination index around the window of 120-140 days. The plot signifies that the covid severity is affected by the vaccination index in the country and not vice versa. Thus we can say that any change in vaccination index (more people gettting vaccinated) today would cause a change in covid severity 4-5 months down the line.
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Pre Vaccination Covid Severity
Post Vaccination Covid Severity
The above graph shows the breakdown of covid severity into two timelines; Pre-vaccination and post-vaccination. These plots show the change in COVID severity before and after vaccination availability. The graph also shows a general best fit line to easily communicate if there was an upward or a downward trend in the covid severity post/pre vaccination.