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Covid data is difficult to analyze because conditions are constantly changing. This is further exacerbated by badly recorded data. See, for example https://hjstein.blogspot.com/2020/05/covid-19-data-collection-garbage-in_33.html, as well as Reporting.ipynb for the data analysis. Fortunately, NYC data is not impacted by inappropriate recording. So, we keep tabs on NYC here.
Of particular interest is the current statistics on infections, hospitalizations and fatalities. This is in the Current.ipynb notebook. Similarly, to avoid hyperventilating about the steep rise in infections under omicron, take a look at the relationship between infections, hospitalizations and fatalities in Growth.ipynb. You'll see there that hospitalizations/infection and deaths/infection have been dropping.
Latest snapshot of recent history - cases/day, hospitalizations/day & deaths/day, with comparison to previous waves.
General data analysis, including:
- Bar graph of latest number of cases reported
- 7 day rolling average of above
- Historical analysis of reports
Analyzing rate of hospitalizations per infection and deaths per infection.
Additional analysis, focused on trying to estimate the peak:
- Growth over time as reports roll in
- Movement of peak
Compare impact of handling data by incident date vs reporting date.
Some of the above blog posts as LaTeX documents, including a presentation given on the subject.