2/8 Rather than make plots of one measure against another, we get the correlation coefficient of all pairs of measures.
Correlation coefficient, CC, of A to B is same as CC of B to A so table is symmetric. Correlation coefficient of A to A is always 1; it is whited out here.
1/7 Excess death (E) in any period is the difference between the actual all-cause deaths and those that are expected. Expected deaths in the current year, c, can be calculated in many ways. Easiest is to use the data from a few recent years as a reference (we use, 2017 to 2019).
2/7 Data can be used in 3 ways to calculate expected deaths. (1) as average death in the reference years. (2) as average corrected for the change in total population. (3) as average for each age band corrected for its population, what we call age-adjusted.
We use 5 age bands.
3/7 (1) If D(i) is death in reference years i, then expected death in year c is E(c)=average[D(i)]. (2) If P(i) is population; E(c)=P(c)*average[D(i)/P(i)]. (3) If (P(i,j) is population of age band j in year i, D(i,j) the corresponding death; E(c,j)=P(c,j)*average[D(i,j)/P(i,j)]
Excess deaths are related to total all-cause deaths. While subject to recording delays, this number avoids all uncertainty about what caused death. Often reported each week, total death can be converted to excess by subtracting a baseline of the total number of deaths expected.
The baseline can be calculated as an average over earlier years or even simply be the totals for a different year. Deaths can be reported anywhere but a larger population makes numbers more certain. The total death is shown here for Europe (350 million) euromomo.eu/graphs-and-maps.
Notice the winter flu peaks that are large in 2016/17 & 2017/18 but smaller in the next two years. There is also a large peak centered at Week 14 in 2020. This is due to COVID19. The excess death from COVID19 is the total number of deaths above the baseline for the entire period.
Here is my clearer analysis of the Population Fatality Rate (PFR) related to influential predictions by Ferguson et al. 2020. It use data released by the Chinese CDC on 14Apr20 @ChinaCDCWeekly, not full-text indexed by Google @Google but released in The @guardian on 1Mar20.
A perceptive reader will ask for the Verity et al., 2020 IFR. My 25Mar report to UK scientific leaders used that data. After normalization to percent, Verity IFR data is identical (0.6% RMSD) to deaths/Chinese_population in Col. F on Tweet1 Excel. My numbers are unchanged.
EuroMOMO euromomo.eu/graphs-and-maps Excess Deaths from 2020 Week 8 now match reported COVID Deaths @JHUSystems perfectly (better than 2%). In earlier weeks the reported deaths were lower. Not sure why? It allows me to do this in depth analysis & comparison with EuroMOMO influenza.
Analysis of Europe's Excess Deaths is hard: EuroMOMO provides beautiful plots; data requires hand-recorded mouse-overs. COVID19 2020, Weeks 08-19 & flu 2018, Weeks 01-16 is relatively easy for all age ranges (totals 153,006 & 111,226). Getting Dec. 2017 flu peak is very tricky.