Oregon – 7 day moving average of total daily tests versus positive test results. Data for most recent two weeks or so is preliminary/provisional and is routinely updated as more data comes in. Hence the decline in the curve at right is an artifact of the delay in receiving data.

Just guessing that the recent upwards trend at right (days 209-225) is likely to be over 7,000 daily tests as recent positive test results have gone from the 400s up to 550 today and the two lines do correlate.

Update – yep, a staggering 9,189 test results came in yesterday – about 50% above the normal run rate. After correcting for that, the number of positive results is exactly in line with the numbers found on recent days this week. Positive results re 5.7% – down from 7.7% last week and 6.0% the week before.

The state also announced a week ago that it would soon be able to double and triple the number of tests being done through use of the BinaxNOW rapid test, which may have a false positive rate of 3-6% (rates of other tests in the BinaxNOW product line).

We will know in a couple of weeks when the data up through this week is brought up to date.

Data from https://public.tableau.com/profile/oregon.health.authority.covid.19#!/vizhome/OregonHealthAuthorityCOVID-19SummaryTable_15889676399110/TotalPersonsTestedSummaryTable

The magenta dots at bottom indicate when various parts of the UK made face masks mandatory, initially only on public transport. But then masks were made mandatory in retail shops and other locations. Rules came into place at varying times depending on location: N Ireland, Wales, Scotland, England and so on.

The more that were required to wear face masks, the worse it became – in fact, many times worse. This pattern has gone on around much of the world and there is no unified explanation as to why face masks have failed to curtail the outbreaks.

Source

Fascinating insights in this video. Well worth watching, maybe even important to watch. You can fast forward through it, if you wish, to get the gist of the message.

Several studies have published estimated IFRs for various regions around the world. For those below age 70, the IFRs are much lower than the overall/all age population IFR. Another study Dr Campbell shows us finds a strong correlation with age demographics as the factor that distinguishes a low IFR in one region with a high IFR in another. 90% of the difference can be explained by population age differences.

He then shows population pyramids of different countries including a couple in Africa, where disease prevalence is low. The implication is that its low there because overall life expectancy was already low (unfortunately) – and hence, their population is massively skewed towards young people with very few older people. Cases in Kenya have been mild and asymptomatic with few deaths (less than 600 for a population of over 50 million). By comparison, California has had about 17,000 deaths with a population of almost 40 million.

The take aways from this video are many, but the interpretation is left up to you.

Do not confuse case fatality rate (CFR) with infection fatality rate (IFR).

He is Director of the UCL Genetics Institute, University College London and this is his tweet.