In part 3 of this series I promised to get out the big spanner, this being formal intervention analysis using ARIMA. The statistical modelling strategy for this is straightforward. I select the period 2020/w50 (first week of vaccinations in the UK) to 2022/w33 (latest available data at the time of analysis). I then run ARIMA four times, thus:
Stage 1: Develop a baseline model using only the time series for excess non-COVID death for the period 2020/w50 onwards.
Stage 2: Submit the independent variable for case detection rate (CDR) to check if this is statistically significant predictor over a range of different lags, then choose the very best fitting model found.
Stage 3: Submit the independent variable for total weekly combined doses to check if this is statistically significant predictor over a range of different lags, then choose the very best fitting model found.
Stage 4: Submit both independent variables simultaneously using optimal lags for each to determine their combined impact.
Stage 5: Coffee & cogitation.
This will necessarily be a somewhat pithy newsletter to avoid extraneous statistical waffle and reams of output but I will slot in a smidgen of plain English interpretation amongst the gibberish so we all have a sense of what I’m doing and what I found. Here goes; deep breaths, deep breaths, one, two, three and…
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