I recently posted this article which highlighted the remarkable divergence between people reporting COVID symptoms through the NHS and the 'official' number of cases. Specifically, I was talking about the difference between the daily data reported at https://digital.nhs.uk/dashboards/nhs-pathways (which catalogues all NHS COVID triage pathways through 999 calls, 111 calls and 111-online accesses) and the daily case data reported at https://coronavirus.data.gov.uk/details/cases.
Several people provided reasonable explanations for the divergence, including the fact that whereas in March and April lots of people were using 111 online to report symptoms, the widespread introduction of track and trace apps in June meant there was no longer much need to use 111 online. However, the most pertinent explanation was that the 999 calls data does not include all 999 ambulance service calls related to COVID. This is certainly true of London.
But it turns out that, for certain areas including the North East and West Midlands, the 999 calls data does include the 999 ambulance service calls (see screenshot below). So I used the filtering option to display only the plot of 999 calls for one of these areas (West Midlands) and compared it to the plot of cases for the same area.
As you can see, we still have the same divergence. As the 999 COVID triage data for the West Midlands includes all 999 ambulance calls it should be a very reliable indicator of patients ill with COVID symptoms. This provides further evidence that many of the COVID 'cases' (and similarly 'hospital admissions' and 'deaths') are being misclassified.
COVID false positives and theimpact of confirmatory testing
Pooled COVID19 testing makes the data on 'cases' even more dubious
Remarkable relationship between number of tests and positivity rate when we drill down into regions
COVID-19 in the UK: the remarkable divergence between number of 'cases' and number of people reporting symptoms
- How to explain an increase in proportion testing positive if there is no increase in infection rate
- We are still not getting the basic testing data we need
- Why we know so little about COVID19 from the data provided
- Impact of false positives in Covid testing
- Covid19 hospital admissions data: evidence of exponential increase?
- Don't panic: limits to what we know about Covid-19 PC testing, inferred infection rates and alse positive rates
- A privacy-preserving Bayesian network model for personalised COVID19 risk assessment and contact tracing
Covid-19: Infection rates are higher, fatality rates lower than widely reported
- Coronavirus: country comparisons are pointless unless we account for these biases in testing
- Why most studies into COVID19 risk factors may be producing flawed conclusions - and how to fix the problem
- Causal explanations, error rates, and human judgment biases missing from the COVID-19 narrative and statistics
- Covid-19 risk for the black and minority ethnic community: why reports are misleading and create unjustified fear and anxiety
- UK Covid19 death rates by religion: Jews by far the highest and atheists by far the lowest 'overall' - but what does it mean?
- All COVID articles on this blog
But it turns