Since Mid-March I have been posting various short snippets of my analysis of statistics for Corona /COVID-19 to my Facebook page (see following example).
March 15 ·
RE Corona Virus situation Canada versus USA
Currently the Corona cases per 1000 people look similar in Canada and US; however, deaths and testing tell a different story.
The USA currently has ~3000 cases, 60 deaths and ~ 15K people tested (as of 2 days ago).
Canada currently has ~300 cases, 1 death and ~ 15K people tested as of 2 days ago.
Based on cases the rate per 1000 look similar so we are no better than US.
However, If we estimate cases based on deaths adjusted for pop X10 then US has 6 X more cases or a true number of around 18,000 cases.
If we adjust for cases versus test done then the US really has 10 X our cases per 1000 people or 30,000 cases
This explains why Trump was holding back on testing. My estimate is that when the US really starts testing at the same rate as Canada per 1000 pop then US cases will skyrocket. Assuming Canada is at 600 cases next week then US if they do more test might hit 50, 000 cases??
However, on April 15 I did a comparison of the relationship between Corona virus deaths and number of reported cases for major urban areas in Europe and the USA, tat was too big for a Facebook posting so I moved it here (see April 15 report further down page). I have subsequently added more updates.
The following is a continuation of the work posted on April 27 with updates and a reanalysis of the relationship between COVID-19 (Corona) deaths per million in selected geographic areas and population of those areas. In the April 27 posting I was focusing on the how the number of deaths per million increases as you move towards the epicenters of the outbreak. After further research I have prepared the following graph that demonstrates that the upper limit of the cases per million follows an inverse power relation with the population of the region considered (remember that stats are adjusted per million pop). The data points are based on the data presented in Table 1 and Table 2 presented in the April 27 report (data for Bergamo have been updated based on a recent press report).
This Graph suggest that the upper limit to COVID deaths per million is partially determined by population density since in general population density increases as you zoom in on urban areas. However, this is not just density since many urban areas with much lower COVID death rates have population densities similar to the Bronx and Bergamo. My interpretation is that within a particular population range the death rate increases as the social, economic, ethnic and demographic profile of the community converges (becomes more uniform) on the factors that increase both infection rate and mortality rate.
Basically, the Bronx and Bergamo both have populations that represent the perfect storm of factors for promoting COVID infections and deaths.
Factors that promote high infection rates:
an early start to the infection
overall population density in area,
percent of population living in multi family buildings,
average number of people in an household,
degree (number) of social interactions,
type of interactions (different forms of greeting),
use of mass transit,
frequency of different types of employment in area (e.g. public facing versus, office vs factory).
Factors that limit transmission:
wearing masks,
social distancing,
closing venues that promote social interactions.
Factors that promote high mortality:
demographic profile of area (% seniors in population)
overall health of population
availability of health care especially to low income individuals,
living conditions of seniors (with family, independent of family, in group homes).
This is not implying that both of these areas have the same combination of factors but both likely share factors like: early initial start, population density, family size and makeup.
This report is intended to show how unrepresentative average COVID statistics are for large geo-political units like Canada, the USA, Europe and the UK. Basically, COVID is not evenly distributed across countries but occurs in clusters. These clusters start as pseudo random outbreaks in different locations. The initial spatial distribution looks random (see map below) but was determined by travel patterns of infected individuals.
Once established in an area it can grow rapidly if conditions are right or linger as a virtually invisible background level of infection. Some of the factors that determine the spread of the infection in any area include:
any steps in place to mitigate transmission (e.g. wearing masks, social distancing).
The upper limit to the infection (# of cases) in an area is determined by the total population in an area and any subsequent measures to limit spread.
The death rate from the infection will be determined by a separate set of factors including:
living conditions of seniors (with family, independent of family, in group homes),
any unknown variations in genetic susceptibility.
Table 1 (see below in this section) summarizes reported cases and deaths from COVID-19 Corona Virus in various regions and sub regions. It was compiled on April 25 and 27 from a number of sources. The analysis focuses on the worse case situation in four large geo-political areas: the USA, the European Union and the UK. It shows how the situation changes as you zero in on the epicenter of the infection hot spots. I have attempted to go to the finest detail possible by identifying the areas within cities with the highest rates of infections (statistics on Corona deaths are not readily available on Internet for sub City regions). Within a city/region there may be multiple hot spots and I have just identified the one with the highest rate of infection.
As you zero in on the epicenter in each region the number of cases and deaths per million population increases. The increase in the absolute number of cases is partially due to the fact that population in urban areas is generally higher than in surrounding areas but there is also an increase in cases per million (which adjust for population). This likely occurs since population density is also greater which promotes the spread of the virus. This is not that different from a colony of bacteria in a Petri Dish where the density of bacteria decreases as you go away from the center of the infection. What this demonstrates is that once the virus becomes established in an area where the the factors, outlined previously, are conducive to its spread it will multiply until it either runs out of victims (herd immunity) or some factor changes (e.g. introduction of mitigating factors like social distancing).
I have included data on cases and deaths but since statistics on Corona cases are very much a function of testing rates and testing strategies which vary widely I focus on deaths which, while still subject to problems, are much more reliable.
The data in Table 1 below shows that the deaths/ million from Corona in New York State are 5X the rate for the USA (New York State being more densely populated than the rest of the US with roughly 64% of the state's population in the New York City metropolitan area and 40% in New York City alone), the rate in New York City is 1.6 X the rate for the State and the rate for the Bronx is 2.2 X that for New York City as a whole. Overall, the Bronx has a death rate 19.5X that for the US as a whole. A similar pattern occurs in Europe and the UK with the areas at the epicenter having rates considerably higher than the national average. However, the degree of difference appears to be differences between regions. To investigate these differences I compiled statistics on other urban areas in all three regions plus Canada (see Table 2).
The following Graph Shows the relationship between number of Corona Virus deaths per million people versus the population of selected urban areas. Note the data for Bergamo Italy, the hardest hit area in Lombardy, is for April 6th which was the latest data I could find so likely significantly underestimates the true fig for April 26th. I have added a line (red) that might represent a trend in the upper limit of deaths/ million vs Population. Remember, these figures are adjusted for population so the relationship does not reflect the number of people but is likely a proxy for other factors like population density. Also the smaller urban areas are likely to have more uniform social, economic, ethnic and demographic profiles than larger units (The Bronx vs New York City). As a result it is more likely that the perfect storm of factors leading to higher infection rates are more likely to be found in areas with smaller populations. The ultimate example of this would be a large nursing home for seniors which would contain a large proportion of the most vulnerable people and which in many cases have have very high death rates from Corona.
Cases Deaths Ratio Population Deaths /Mill Cases /Mill
USA 1,000,000 56308 5.6% 331,000,000 170 3021
New York State 282,000 16600 5.9% 19,500,000 851 14462
New York City 155,000 11817 7.6% 8,400,000 1407 18452
Bronx 35,450 4700 13.3% 1,418,000 3315 25000
EU 930,123 99,052 10.6% 446,000,00 222 2085
Italy 199,000 26,977 13.6% 60,360,000 447 3297
Lombardy It 71,256 13106 18.4% 10,060,000 1303 7083
Bergamo (see notes) 9,712 2,245 23.1% 1,100,000 2041 8829
UK 157,149 21092 13.4% 68,000,000 310 2311
England 130,000 18748 14.4% 56,000,000 335 2321
London 23,833 4693 19.7% 8,900,000 527 2678
Crydon 1,325 385346 3442
Table I April 27, 2020
Urban Area Corona Deaths Population Deaths/Mill
Montreal 808 1,800,000 449
Toronto 253 2,930,000 86
Vancouver Coastal 64 1,250,000 51
Calgary 45 1,635,000 28
New York City 11817 8,400,000 1407
Bronx 4700 1,418,000 3315
Wayne Co MI 1580 1,749,000 903
Mass 2730 6,893,000 396
New Orleans 1644 4,650,000 354
Miami Dade 287 2,717,000 106
LA County 894 10,040,000 89
San Francisco 22 882,000 25
Lombardy It 13106 10,060,000 1303
Bergamo Lombardy 2245 1,100,000 2041
Madrid 7922 6,642,000 1193
Belgium 7207 11,590,000 622
Greater London 4693 9,300,000 505
Stockholm County 1128 2,344,000 481
Ile de France 5578 12,210,000 457
Netherlands 4518 17, 135,000 264
Bavaria 1596 13,300,000 120
Baden-Württemberg 1237 11,191,000 111
North Rhine-Westphalia 1096 17,839,000 61
Table 2 April 27.
Update April 17, 2020.
The following Graph is the latest update to my analysis of Corona Virus statistics. It looks at the relationship between reported cases and deaths for 49 selected urban areas (counties, regions, cities, small countries) in Europe, the UK, the USA and Korea. The data was captured on April 15 using the latest data available on the Internet (see Table at end of this Section). All of these areas are in developed countries with relatively reliable statistical data on Corona deaths (though not perfect). The objective was to examine variations in the relationship in different regions.
In earlier analysis I noted regional variations in the relationship and the data points have been labeled to reflect these: (A) Europe & UK minus Germany and Switzerland; (B) the USA and (C) Germany & Switzerland plus Korea. In area (A) deaths were approximately 14.8% of Reported Cases in (B) 7% and in (C) 3%.
One factor that might explain this relationship is the level of testing in each region. The countries in (C), Korea, Germany and Switzerland are noted for high rates of testing. The USA (B) was initially slow in ramping up testing but since the start of April the rates of testing have increased significantly, especially in the urban areas plotted in this graph. Europe and the UK started mass testing earlier than the USA but the rapid growth in the number of cases overwhelmed the systems.
What this graph indicates that if we use the number of deaths as the most reliable (but not perfect) indicator and we assume the death rate from the virus is relatively constant then the reported cases in Europe and the UK underestimate the true case count when compared to the USA. If we use London as an example the statistic used for April 15 was 17,479 cases with 3265 deaths. If London was following the USA trend line then this would equate to approximately 46,000 cases or a case count 2.6 X higher. This would mean that London's Case Count is not 16.4% of the case count for the City of New York but more like 43%.
Putting this in the US context it means that many European regions have much higher case counts compared to USA cities than the raw published statistics would suggest. Some reports compare make projections for US Cities like New York based on the situation in European regions like Lombardy but based on this analysis the situation in Lombardy is much worse than in New York and there is potential for the situation to get much worse in the US. It should also be noted that based on area (C) it is likely that the reported statistics for the USA significantly underestimate the true case count.
Posting April 15, 2020
Background and Methodology
I have been attempting to understand the relationship between the number of reported cases of Coronavirus (i.e. COVID-19) and the number of deaths resulting from the Virus. The true relationship between these factors is masked by a number of factors related to how the data is collected; these include:
The rate of testing for Corona in different countries and regions,
How different regions compile death statistics,
Geographic variability in occurrence of Corona virus in Countries,
Variations in the Health and Demographics (%>60) by Country,
Variations in Population density and culture between countries,
Possible political interference in data collection and reporting.
To overcome some of these problems I focused on data from large urban clusters (major cities, urban regions/counties and small high density countries) in Europe, the UK and the USA. The measurement unit depended on what statistics were available (e.g. Urban Counties in US, Regions or countries in Europe). By focusing on these smaller geographic areas I reduced problems resulting from geographic variations in occurrence and testing in large geographic areas like the USA and Italy. The analysis focused on those regions with the highest rates of Corona. The data was compiled on April 14th from sources like Google/Wikipedia. A list of the data used is provided in a Table at the end of this Report.
The Corona Virus Paradox
The following Graph presents a scatter chart for the data contained in the Table. Each dot represents the relationship between reported cases and deaths for that region. The chart excludes New York City and Lombardy Region in Italy. These were exclude since they are such extreme outliers (high values) they would skew the trend lines (more discussion on these later). I also added Labels on each data point to indicate whether the data point was in: Europe/UK (E), The USA (A), Germany (G) or Switzerland (S). The latter two markers (G & S) were identified separately from the rest of Europe since they fit a different pattern.
I fitted separate linear regression line to the data for Europe (including Germany and Switzerland) and the USA. The data for the USA (excluding New York) cluster around a regression line suggesting a death rate of approximately 3.4 deaths per 100 reported cases (3.4%). The European data (Including UK, Germany and Switzerland) show much more variability and suggest a much higher death rate of ~10.5%. However,the chart shows that Germany and Switzerland are a better fit for the USA trend line.
The following Graph shows the data for Europe and the UK including Lombardy but minus Germany and Switzerland. By excluding the latter two countries there is a much better fit for the new trend line and Lombardy now fits the data. However, this new trend line suggest a death rate of 14.7%!
Possible Explanation
Ignoring possible genetic or cultural differences between the two groups the obvious explanation is differences in how the data is collected. Death rates are perhaps the most reliable of the two factors but recent articles have pointed out that death statistics in the US and Germany might be underestimating Corona deaths due to failure to run test and not recording deaths in senior home. This might increase US and German death rates somewhat but can only explain a small part of the difference. The most likely cause of the differences is that most European Countries (excluding Germany and Switzerland) are significantly underestimating the number of cases versus the data plotted for the USA (by a factor of 4 X). We know that reported cases in the USA underestimate the actual number of cases but the European data is even further off the mark.
Germany was noted as having the highest rate of testing in Europe. The USA started testing later than most European countries and initially was testing at a lower rate but the rate of testing accelerated in April. In addition the data I show is for major urban areas with high number of cases and these are the regions of the USA that have the highest rates of testing. By contrast many European countries started testing early but the rate of infections overwhelmed their systems.
Conclusion April 15th section
The true number of Corona Virus cases in most European countries (Italy, Spain, France, UK) are likely 4 times higher than reported rates (using USA reported rates as baseline). What this suggest is that while European rates of new infections may be leveling off somewhat they are doing so at much higher levels than the USA is experiencing. This may mean that Urban areas in the USA may not start leveling off until much late
Bottom line is that the true number of cases in Europe (excluding Germany and Switzerland) may be 4 X greater than those in USA. Good news is that USA is catching up on testing but bad news is that the true number of cases per Mill pop in Italy is not 1.4 X that in US but 7 X i.e. US has a lot further to go before reaching where Italy is.
Update April 16: Refining the Results for April 15
This project is a work in progress and I have updated the report to reflect the new research. The following Graph shows how the number of cases in London would change using American trend line from the previous graph:
At the time I compiled the chart the City of London had 17,479 cases and 3265 deaths. If we extend the American trend line (Red) then London with 3265 deaths should have approximately 90,000 cases (nearing the situation in New York which had 106,000 reported cases at the time the data was compiled).
This figure of 90,000 cases for London does appear high when compared to reports of the situation in London compared to New York City. To investigate this I redid the USA graph to include data for New York City which was excluded from the earlier analysis (see below).
Deaths versus Reported Cases US Urban Areas
Doing this changed the slope of the line and left three outliers which are areas outside New York City (two of which are Long Island and Hudson River valley). These latter two are low density high income suburban areas so the new curve likely is a better fit for more Urban areas.
Using this new American trendline the estimate number of cases for London (Pop 9 mill) would be around 50,000 or about half of those in New York City (Pop 8.4 Mill) which is a better fit to the reported situation. Applying the same trend to the data for Lombardy Italy (Pop 10 Mill) would increase the number of cases from 60314 (see table) to around 160,000 (2.7 X increase).
It should be remembered that the American reported cases are themselves an underestimate of the true cases since they miss many asymptomatic cases. As a result the actual number of cases in places like London and Lombardy will be considerably higher than my estimates.
Data Table April 15
Region Cases Deaths
Lombardy Italy 60314 10901
Emilia-Romagna 20,440 2,615
Piedmont 17,134 1,826
Veneto 14,251 882
Tuscany 7,390 518
London 17479 3265
Switzerland 25,936 1,174
Belgium 31,119 4,157
Île-de-France 7,660 3,416
Grand Est 5,479 2,008
Auvergne-Rhône-Alpes 2,093 755
Provence-Alpes-Côte d'Azur 1,924 317
Bavaria 33,569 872
North Rhine-Westphalia 25,300 545
Baden-Württemberg 25,040 711
Netherlands 27,419 2,945
Community of Madrid 48,048 6,568
Catalonia 35,197 3,666
Castile-La Mancha 14,329 1,714
Castile and León 13,180 1,299
Basque Country 11,226 859
Los Angeles County 9,420 320
San Francisco County 957 15
King County Seattle 4,517 295
Miami-Dade County 7,459 109
Orleans Parish LA 5,651 244
Jefferson Parish LA 5,088 186
Denver County 1,346 51
Fairfield County Conn 6,004 262
Wayne County MI 11,648 760
Oakland County MI 5,073 347
Macomb County MI 3,418 240
Marion County IA 3,012 123
Fulton County GE 1,635 52
Harris County TX 3,261 40
Dallas County TX 1,537 25
Bergen County NJ 9,784 453
Hudson County NJ 7,469 226
Essex County NJ 7,410 428
Union County NJ 6,180 209
Middlesex County NJ 5,693 193
Passaic County NJ 5,590 131
Nassau County NY 24,358 910
Suffolk County NY 21,643 568
Westchester County NY 19,786 557
Rockland County NY 7,965 200
Orange County NY 5,598 171
New York City 106,863 7,349