Multiple sources quote number of Covid-19 daily cases, but information on actual count of patients active at any given moment is rare. ECDC data set I use does not show number of recovered patients, so it is not possible to subtract cumulative recoveries form cumulative cases to calculate number of ill people at any given moment. However we still can estimate number of Covid-19 infected using number of cases.
Estimation method
Whoever contracts Covid1-19 will either recover or die after some time. Let us make the following simple assumption regarding actual illness duration:
Covid-19 patient is ill 14 days from infection date inclusive
Then 7 day recovery period starts and number of infected patients drops linearly
Above rule will give us a set ow weights (see figure below) to be applied to daily cases in order to calculate current number of Covid-19 patients (cases).
Estimation results
Figure below shows estimated number of active Covid-19 cases for selected countries. Only countries above 1 million population were taken into account. Next to country name, in brackets, country all time rank in number of active Covid-19 cases is shown. In order to compare countries with different populations number of Covid-19 ill people (cases) per million is plotted.
Belgium holds all time top in Covid-19 active cases. Czechia is number 2, its curve (in this scale) is similar to Belgium one, we don;t show it to avoid chart overcrowding.
Israel and Qatar had high readings but they have dropped significantly. Israeli drop may be due to lock down imposed on country. I have no details on Qatar lock down policy.
Sweden 2nd wave is much lower than Belgium and other countries, please note Sweden 1st wave was extended over time and the country had distinct lock down policy.
Belgium number of active cases may have peaked, but since Belgium data are delayed (it takes 3 days before all cases are counted) we need to wait a bit in order to confirm this conclusion.
What is happening now in Europe is very similar to Qatar experience, they have survived it so we will too. Unless health services are better organized there…
ECDC data set for Spain shows some strange patterns, on 2 days daily death count is negative. One figure recorded 25th May is huge -1918, the other form 12th Aug is just -2 but still it should not be negative. I understand there may be situations where data need correction, but in data set presenting time series it should be done by correcting data at date when it was previously overstated. Otherwise entire data collection process becomes doubtful. Figure below shows daily deaths data for Spain with potentially wrong entries marked by orange dots.
Table below lists data points which need to be corrected:
index
DateRep
GeoId
Cases
Deaths
Countries and territories
1
46429
2020-04-27
ES
1660
0
Spain
2
46428
2020-04-28
ES
1525
632
Spain
3
46404
2020-05-22
ES
1787
688
Spain
4
46401
2020-05-25
ES
-372
-1918
Spain
5
46400
2020-05-26
ES
859
283
Spain
6
46376
2020-06-19
ES
307
1179
Spain
7
46322
2020-08-12
ES
3172
-2
Spain
8
46238
2020-11-04
ES
25042
1623
Spain
Items 1,2 – Deaths from 2 days were probably recorded under one date
Item 3 unusually high figure comparing to nearby points
Item 4 negative deaths count (-1918)
Items 5, 6 unusually high figure comparing to nearby points
Item 7 negative death count (-2)
Item 8 surge in death counts, can be attributed to 2nd wave impact, but for me it look as data glitch since it stands out nearby points
Conclusions
Items 3 to 7 from above table combined (688-1918+283+1179-2) total 230. Recording this figure on 26th May and zeroing existing entries can be a quick fix to the data, but the case requires a deeper investigation how Spanish data are reported. It is especially important if we take into account deaths surge reported Nov 4th. It looks like a data collection glitch, but it may as well represent valid data resulting from 2nd wave, so it definitely need investigation.
Added 2020-11-13:
It seems Spain has own understanding of time series. November spike comes from re-stating definition of Covid-19 deaths. Why do they post +1300 deaths occurred prior to 11th May together with current data (297 deaths on 2020-11-04) in November is hard to comprehend. Such an approach clearly distorts 2nd wave statistics. https://www.aa.com.tr/en/europe/spain-s-covid-19-death-toll-surges-by-1-623/2032447
Covidmeter shows number of cumulative Covid-19 deaths per 1 million people. Figure below displays top 5 countries in this ranking along with USA, Russia and Poland which were hand picked. Country rank in cumulative deaths is shown after its name. Diamond Princess and San Marino were added as reference and future impact prediction, please go to previous posts for rationale.
No Covid-19 fatality was registered in San Marino since May.
Belgium leads ranking again.
Predictions regarding total population impact – 0.2% or 2000 fatalities per million made after Diamond Princess experience hold true. In fact no country so far exceeded San Marino figure.
Covid-19 2nd wave in Belgium takes death toll while in Sweden deaths count is very modest.
China (not shown on picture) unlike other counties it does not experience 2nd wave. They have either eliminated the virus (unlikely) or called off epidemic and focus on treating individual cases as they arise (probable).
Covid-19 deaths are usually reported as daily totals. It would be interesting to have a look into more details like deceased people age or health condition like chronic illness. Surprisingly such a data are not easily available. I’ve managed to get some detailed data on Covid-19 deaths in Poland, you’ll find them in this post.
Data set
Data presented here come form Poland Ministry of Health, I’ve got them on requests, they are not published regularly. The data represent period from 2020-10-09 to 2020-10-27. Received set has information on:
death date
death place
sex
age
The set has no information on chronic illness. Other statements from ministry show 90% of Covid-19 positive deceases had preexisting illness. Chart below shows age distribution for entire set.
Very few people under 40 die with Covid-19
It would be super interesting to compare above distribution with remaining deaths distribution. Around 1200 die in Poland on daily basis, max Covid-19 positive deaths are 179 in the set. Unfortunately I have no access to the latter distribution. I believe both will be similar. For avoidance of doubts: It does not mean Covid-19 is not dangerous for individuals who contract it.
I believe epidemic dangerous for population should take its toll regardless of age, Covid-19 seems to focus on older ones
Males and females
Charts below show age distribution split by gender.
Men have significantly higher death count till 80, after 80 both distributions are almost identical
Men represent 58% of all deaths, male deaths are (1025-722)/722=42% higher than female
Table below compares age distribution in numbers. Age 25% means 25% of deaths in group are under this age. Please note average lifespan in Poland is 78 years.
all
male
female
total deaths
1747.0
1025.0
722.0
average age
75.8
74.2
78.1
most common age
82.0
82.0
87.0
age 25%
69.0
67.0
72.0
age 50%
78.0
76.0
80.5
age 75%
85.0
83.0
87.0
Conclusions
There are little fatalities under 40, just 27 out of 1747 or 1.5%
Covid-19 fatalities are much higher among men than women
Death age distribution shows Covid-19 does not represent a significant threat for population since it spares younger part if it.
Better quantitative analysis of Coviid-19 cases is needed. Number of cases and deaths are floated everywhere. Age of deceased was hard to get. I’ve seen no data on recovered ones like full/partial recovery, time between infection and recovery, hospitalization rate, intensive care time etc.
Lock down or quarantine will not save us, working economy allowing better planning and execution of medical response will.
Comparing Covid-19 cases and deaths during first and second wave of epidemic reveals interesting facts. Daily cases during second wave exceed 1st wave figures, while for some countries daily deaths stay significantly lower. One country – Sweden seems to have Covid-19 resilience, people get ill but fatalities are rare.
Method
In order to compare 1st and 2nd wave we make charts for selected countries following steps below:
Second wave data are shown from 1st Oct
First wave data are taken form 15th Mar to 1st June and moved forward by 200 days so they start at 1st Oct after the shift
Both 1st and 2nd wave data are presented on same chard for visual comparison
Deaths per million people are shown on left axis, cases on right one
Belgium
We see daily cases during 2nd wave (peak 1600) significantly surpass 1st wave (peak 200).
Daily deaths are lower during 2nd wave (peak 5) than 1st (peak 25). Of course 2nd wave figures may go up, but so far cart clearly shows daily deaths dynamics is significantly lower during 2nd wave.
Please note recent drop of cases and deaths figure is due to data collection method. Initial data are not accurate and take 3 days to be refined, so both numbers are likely to be revised up.
Netherlands
We see pattern similar to Belgium one: cases 100 vs 600, deaths 14 vs 3. Second wave brings more cases but less deaths
Please note 2nd wave cases for Belgium (1600) more than double Netherlands (600). Belgium mandated face masks in public spaces much earlier than Netherlands, so mask magic should be treated with a grain of salt. Cases detected may be correlated with tests performed, unfortunately ECDC data set I use has no information on tests.
Poland
Both cases and deaths during 2nd wave significantly exceed 1st wave figures.
Lock down during 1st phase delayed Covid-19 impact but failed to eliminate it
Czechia
Czechia shows similar pattern to Poland, unfortunately its cases and deaths figures are much higher
Czechia 2nd wave daily cases are similar to Belgium, but daily deaths are much higher (12 vs 5 for Belgium)
Sweden
Sweden daily cases 2nd wave exceed 1st wave figures, but difference is not as high as Belgium one (2x vs 8x)
There is no growth in daily deaths, figure stays around 0. In other words people get sick but fatalities are rare. It looks as if Sweden population built Covid-19 resilience.
Conclusions
Sweden approach to epidemic worked best so far. First wave may have taken longer, but apparently country population build Covid-19 resilience. This is not immunity, people still get ill but fatalities are rare. I would not call it herd immunity since nobody knows how herd immunity is defined.
Countries with high daily deaths rate during 1st wave have significantly lower rates now. Cumulative deaths estimates as presented in Covidmeter remain valid.
Countries with low death rates during 1st wave are catching up now. For them lock down measures of 1st wave were sort of futile. Of course situation would be different had Covid-19 medication had bee discovered by now.
Panic can increase death toll. Patient with brain stroke or heart attack kept at hospital driveway for several hours due to unclear Covid-19 status is likely to die. Medical personnel adequately equipped with protective gear should be able to treat urgent cases accepting reasonable infection risk.
Sweden got a lot of bad press for adopting somehow relaxed approach to Covid-19 lock down. There were voices it caused a carnage especially among elderly people. Figures below show so far Sweden escapes 2nd wave of Covid-19, so maybe what they do is worth investigating.
ECDC data set used it this analysis gives number of deaths on daily basis. Since the number fluctuates quite a lot we calculate a moving average (MA) to eliminate some noise, I use 7 days MA. Figure below shows daily deaths 7 days MA for selected EU countries. We can see 2 peak structure corresponding to 1st and 2nd wave of disease.
Once we have MA we can find a maximum for value for each country and sort it top down thus creating a country ranking. On chart legend ranking is shown in brackets next to country name.
No 2nd wave in Sweden?
Figure below shows same countries from Sep 10th. We see daily deaths are gradually growing for all countries but Sweden. Worst situation is in Czechia, where daily deaths growth is accelerating quite rapidly. Please note Czechia daily deaths now significantly exceed its first wave values, same applies for Poland. Both countries implemented lock down during 1st wave, it seems it only delayed outbreak.
Comparing 1st and 2nd wave
In order to visually compare 1st and 2nd wave 1st wave data for reference country are moved forward along time axis by number of days indicated in chart legend.
So far no country is close to the dynamics of 1st wave of Covid-19 in Belgium. Even Czechia development much less aggressive than it used to be in Belgium.
Current Czechia situation looks similar to 1st wave development in Netherlands.
Conclusions
Swedish approach to Covid-19 resulted in slower drop of daily deaths but no 2nd wave pick up is visible so far.
Belgium mandated face masks in all public spaces while Netherlands not, yet the former has currently higher Covid-19 death rate than the latter. Masks may have some impact on the virus transmission reduction, but they don’t do magic to stop it.
In terms of daily deaths growth 2nd wave is less violent than 1st one.
Countries with low death rates during 1st wave (Czechia, Poland) set new highs during 2nd wave. First lock down did not prevent outbreak, just delayed it.
Swedish approach to epidemic – no lock down but elevated hygiene standards and limits on mass gatherings seem to work better.
Lock downs have a bang in media, but results achieved so far do not justify significant economic burden incurred.
In EU normal daily death rate is around 30 per 1 million. Covid-19 deaths in none of investigated countries are close to that figure.
Covid-19 is likely to stay with us like many other diseases. Subsequent outbreaks are likely to be less severe. it will not kill all, just some. Bad response plans with expedited mass implementation and huge cost may do more harm to everyone than the virus, so watch your politicians.
Last Covidmeter update was issued and of May. Unfortunately the disease is still around. Conclusions made a couple of months ago still hold true: Covid-19 takes toll on some individuals, but it does not have significant impact on entire population.
I have decided to restart coverage with just one picture showing number of cumulative Covid-19 deaths per 1 million people. Figure below shows top 5 countries in this ranking along with USA, Russia and Poland which were hand picked. Country rank in cumulative deaths is shown after its name. Diamond Princess and San Marino were added as reference and future impact prediction, please go to previous posts for rationale.
No Covid-19 fatality was registered in San Marino since May.
Peru now leads in Covid-19 deaths, South America was hit hit in summer like Europe in spring.
Predictions regarding total population impact – 0.2% or 2000 fatalities per million made after Diamond Princess experience hold true. In fact no country so far exceeded San Marino figure.
So far Covid-19 2nd wave in hard hit European countries like Belgium and Spain does not result in steep growth of fatalities comparable to the one in spring
China (not shown on picture) ranking is 161. Unlike other counties it does not experience 2nd wave. They have either eliminated the virus (unlikely) or called off epidemic and focus on treating individual cases as they arise (probable).
DAX (Deutscher Aktienindex) jest najważniejszym niemieckim indeksem akcji, na który składa się 30 spółek. Indeks jest popularnym instrumentem do spekulacji, na którym wielu chce zarobić, ale większość traci. Podstawowy handel akcjami DAX odbywa się od 9:00 do 17:30. Obrót kontraktami na DAX trwa dłużej. Pewien zasłużony spekulant zadał pytanie o skuteczność następujących strategii inwestycyjnych:
Wchodze w DAX L codzienie o 9 wychodze o 17:30
Wchodze w DAX L o 17:30 wychodze nastepnego dnia o 9:00
Wchodze w DAX S o 17:30 S i wychodze nastepnego dnia o 9:00
Wchodze w DAX L o 15:30 i wychodze o 17:30
Dla nie spekulantów wyjaśnienie: Wchodzę L oznacza kupuje, S sprzedaję. Wyjście polega na wykonaniu operacji odwrotnej. Zysk z transakcji L to różnica wartości DAX na wyjściu i wejściu (sprzedałem drożej niż kupiłem), dla transakcji S odwrotnie.
Żeby odpowiedzieć na pytanie spekulanta przeanalizowałem dane transakcyjne z systemu pewnego brokera regulowanego i sporządziłem kilka wykresów:
Wykresy na podstawie historii notowań DAX zapisanych w plikach .hst terminala mql4.
Wykorzystane dane 30 minutowe. Dysponowałem danymi od listopada 2018.
Wynik obejmuje około 410 transakcji i nie uwzględnia spreadu
Wyniki poszczególnych strategii na wykresach niżej.
Wnioski
Najlepszy wynik uzyskała strategia “2. Wchodze w DAX L o 17:30 wychodze nastepnego dnia o 9:00”
DAX najbardziej zmienia się w czasie, kiedy jego podstawowy rynek jest zamknięty. Kiedy spekulant śpi rynek rośnie?
Wielu spekulantów zamyka pozycje DAX wraz na koniec pracy rynku dla ograniczenia ryzyka. Warto przeanalizować wpływ takiej strategii na zyskowność handlu.
Niniejsze opracowanie służy wyłącznie intelektualnej zabawie z danymi rynkowymi. Autor nie odpowiada za starty spekulantów, nie ma też prawa do udziału w zyskach.
Lock down easing does not result in Covid-19 surge, it was not preventing spread neither. Thus we can call the virus lock down neutral
All quiet in China, reporting is key success factor I presume
Covid-19 worldwide deaths toll scary in absolute numbers pales compared with human population size
Both common cold and Covid-19 are here to stay, they will not take us all from here to eternity, just some individuals
Picture below shows China and San Marino cases over last 6 weeks, we show absolute number. China success is hard to believe if you think about difference i population size.
World summary
Table below shows key numbers on Covid-19 development. ECDC data sets provide information on cases and deaths, so we can find some top scores. Metric come in some variations, they can be combined:
Suffix 1M – per 1 million inhabitants data.
Suffix 7d MA – 7 days moving average
Prefix population > – values calculated for countries above population threshold.
date
country
value
population
max cumulative cases
2020-05-31
United States of America
1770384
327167434
max cumulative deaths
2020-05-31
United States of America
103781
327167434
max cumulative cases 1M
2020-05-31
Qatar
19866
2781677
max cumulative deaths 1M
2020-05-24
San Marino
1243
33785
max daily deaths 1M 7d MA
2020-03-22
San Marino
63
33785
population > 100000, max cumulative cases 1M
2020-05-31
Qatar
19866
2781677
population > 100000, max cumulative deaths 1M
2020-05-31
Belgium
827
11422068
population > 100000, max daily deaths 1M 7d MA
2020-04-17
Belgium
29
11422068
population > 100000, max daily deaths 1M
2020-04-26
Ireland
48
4853506
recent cases max
2020-05-31
Brazil
33274
209469333
recent deaths max
2020-05-31
Brazil
956
209469333
recent cases max 1M
2020-05-31
Qatar
846
2781677
recent deaths max 1M
2020-05-31
Brazil
4
209469333
worldldwide Covid1-19 cases
2020-05-31
6027546
7550105433
worldldwide Covid1-19 deaths
2020-05-31
370564
7550105433
USA leads in absolute numbers, it has big population but China one is even bigger and pandemic started there. China either quenched Covid-19 or implemented creative reporting, the former is easier.
San Marino had one Covid-19 death on May 24th, previous one happened 27th April and they report cases on daily basis. There is a good chance the virus penetrated entire population, killed 0.12%, This hardly qualifies as carnage.
Daily deaths maximum straight number (Ireland) and moving average (Belgium) were scored back in April
Absolute number of deaths is impressive but it represents around 1% of all deaths on Earth in Feb – mid May time frame. Human population is huge, we are mortal, 7.5 billion population produces in average over 200 thousands deaths per day.
Top countries
Figures below show top 30 countries selected by cumulative number of cases. The group represents 89% of total cases and 94% of total deaths. In top 30 group lowest cumulative cases figure is 30.7 thousand cases.
Countries are sorted by cumulative cases per 1 million people
China (CN) is unique in its ability to stop Covid-19. No other country poor nor rich small nor big was able to repeat its success. China culture is not about social distancing. Is superb quarantine or rather creative reporting behind China success?
Countries are sorted by cumulative deaths per 1 million people
Again China is at the end. Normally country first hit by epidemic will suffer most, others can learn from its experience and prepare a better response. Maybe they are stubborn to learn and inept to implement solutions instantly developed in China?
Countries are sorted by cases mortality
Please note wide range of cases mortality rate, from 0.1% Singapore to 20% France. This indicates wide difference in both death and case definition.
Covid-19 cases and deaths evolution
Figure below compare recent and reference values. We show Covid-19 cases and deaths. Countries are selected in the following way:
Dashed lines represent reference countries, we are looking for historical maximum in the following categories:
cumulative cases (cc),
cumulative cases per 1 million people (cc_1M),
cases per 1 million people 7 days moving average (cma_1M)
Solid lines represent 3 countries with top recent cma_1M.
Country once selected is excluded from subsequent selections.
Rule for deaths comparison is similar to cases one.
Quatar reports significantly more cases per million people than any country, while its death toll is low (see earlier charts). We see historical maximum in cumulative cases per million and cases per million moving average now, so Quatar line is dashed. This can be explained either by superb testing coverage or long lag between infection (case) and death. Please note cases surge took place in April. Lock down measures did not help to prevent it.
South America and Persian Gulf is Covid-19 are hot spots, again lock down does not prevent Covid-19 spread.
No country is close to make new record in this category
Cases mortality comparison
We calculate cases mortality dividing cumulative deaths by cumulative cases. High and low cases mortality countries are selected in the following way:
More than 20 cumulative deaths and 10000 cumulative cases
Sort by cases mortality rate
Take top 5 and bottom 5 from above list
Add Germany to result
Top mortality countries is stable
Russia left low mortality group
Chart below shows countries with lowest mortality rate with Russia added.
Russia has very high number of cases, almost 400 thousands
Singapore has around 34 thousands cases and very low mortality rate, total deaths as of 31.05 are 23.
Cumulative deaths per 1 million people
Leaders group is constant
Covidmeter
Covidmeter compares cumulative deaths per million people for top countries with reference. Reference countries are:
Diamond Princess cruise ship
San Marino – country, enclave in Italy
DP development is closed, crew and passengers disembarked the ship. San Marino is a live group, the country has tiny population, over 30 thousands, but it was hard hit my Covid-19.
No country came close to Diamond Princess death toll declared limit at the beginning of April
San Marino still reports Covid-19 cases on daily basis, last deaths were reported 27th April and 24th May. Neither Covid-19 nor common cold will be fully eliminated there.
China reporting standards are different than other countries. You can call it cheating if you like. It is just a tag, however Chinese propaganda will not like it
Covid-19 deaths toll scary in absolute numbers pales compared with population size
Both common cold and Covid-19 are here to stay, they will not take us all from here to eternity, just some individuals
World summary
Table below shows key numbers on Covid-19 development. ECDC data sets provide information on cases and deaths, so we can find some top scores. Metric come in some variations, they can be combined:
Suffix 1M – per 1 million inhabitants data.
Suffix 7d MA – 7 days moving average
Prefix population > – values calculated for countries above population threshold.
date
country
value
population
max cumulative cases
2020-05-22
United States of America
1577287
327167434
max cumulative deaths
2020-05-22
United States of America
94702
327167434
max cumulative cases 1M
2020-05-22
San Marino
19476
33785
max cumulative deaths 1M
2020-04-27
San Marino
1213
33785
max daily deaths 1M 7d MA
2020-03-22
San Marino
63
33785
population > 100000, max cumulative cases 1M
2020-05-22
Qatar
13894
2781677
population > 100000, max cumulative deaths 1M
2020-05-22
Belgium
804
11422068
population > 100000, max daily deaths 1M 7d MA
2020-04-17
Belgium
29
11422068
population > 100000, max daily deaths 1M
2020-04-26
Ireland
48
4853506
recent cases max
2020-05-22
United States of America
25434
327167434
recent deaths max
2020-05-22
United States of America
1263
327167434
recent cases max 1M
2020-05-22
Qatar
558
2781677
recent deaths max 1M
2020-05-22
Brazil
5
209469333
worldldwide Covid1-19 cases
2020-05-22
5067087
7550105433
worldldwide Covid1-19 deaths
2020-05-22
332704
7550105433
USA leads in absolute numbers, it has big population but China one is even bigger and pandemic started there. China either quenched Covid-19 or implemented creative reporting, the former is easier.
In San Marino nobody died due to Covid-19 since 27th April and they report cases on daily basis. There is a good chance the virus penetrated entire population, killed 0.12%, This hardly qualifies as carnage.
Daily deaths maximum straight number (Ireland) and moving average (Belgium) were scored back in April
Absolute number of deaths is impressive but it represents around 1% of all deaths on Earth in Feb – mid May time frame. Human population is huge, we are mortal, 7.5 billion population produces in average over 200 thousands deaths per day.
Top countries
Figures below show top 30 countries selected by cumulative number of cases. The group represents 90% of total cases and 95% of total deaths. In top 30 group lowest cumulative cases figure is 24.3 thousands cases.
Countries are sorted by cumulative cases per 1 million people
China (CN) is unique in its ability to stop Covid-19. No other country poor nor rich small nor big was able to repeat its success. China culture is not about social distancing. Is superb quarantine or rather creative reporting behind China success?
Countries are sorted by cumulative deaths per 1 million people
Again China is at the end. Normally country first hit by epidemic will suffer most, others can learn from its experience and prepare a better response. Maybe they are stubborn to learn and inept to implement solutions instantly developed in China?
Countries are sorted by cases mortality
Please note wide range of cases mortality rate, from 1% Russia to 20% France. This indicates wide difference in both death and case definition.
Covid-19 cases and deaths evolution
Figure below compare recent and reference values. We show Covid-19 cases and deaths. Countries are selected in the following way:
Dashed lines represent reference countries, we are looking for historical maximum in the following categories:
cumulative cases (cc),
cumulative cases per 1 million people (cc_1M),
cases per 1 million people 7 days moving average (cma_1M)
Solid lines represent 3 countries with top recent cma_1M.
Country once selected is excluded from subsequent selections.
Rule for deaths comparison is similar to cases one.
Quatar reports significantly more cases per million people than any country, while its death toll is low (see earlier charts). We see historical maximum in cumulative cases per million and cases per million moving average now, so Quatar line is dashed. This can be explained either by superb testing coverage or long lag between infection (case) and death. Please note cases surge took place in April. Lock down measures did not help to prevent it.
Singapore (SG) is no longer among top cases countries.
South America and Persian Gulf is Covid-19 are hot spots, again lock down does not prevent Covid-19 spread.
Deaths per 1 million people are dropping
Brazil replaced Italy and its deaths per million are on rise.
Contrary to some media claims there is no surge in Sweden deaths.
Cases mortality comparison
We calculate cases mortality dividing cumulative deaths by cumulative cases. High and low cases mortality countries are selected in the following way:
More than 20 cumulative deaths and 10000 cumulative cases
Sort by cases mortality rate
Take top 5 and bottom 5 from above list
Add Germany to result
Top mortality countries is stable, so is low group
Mortality rate in UK drops, this may be due to increased testing capacity or Covid-19 true cases peak – everybody prone to virus already infected.
Chart below shows countries with lowest mortality rate.
Russia has very high number of cases, above 300 thousands, this is order of magnitude higher than other countries on chart. Interesting is mortality rate for Russia stays constant. For other countries it went up once number of cases soared.
Singapore has around 30 thousands cases and very low mortality rate, total deaths as of 12.05 are 23.
Cumulative deaths per 1 million people
Leaders group is constant
Covidmeter
Covidmeter compares cumulative deaths per million people for top countries with reference. Reference countries are:
Diamond Princess cruise ship
San Marino – country, enclave in Italy
DP development is closed, crew and passengers disembarked the ship. San Marino is a live group, the country has tiny population, over 30 thousands, but it was hard hit my Covid-19.
No country came close to Diamond Princess death toll declared limit at the beginning of April
San Marino still reports Covid-19 cases on daily basis, last death was reported 27th April. Neither Covid-19 nor common cold will be fully eliminated there.
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