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:

1. Covid-19 patient is ill 14 days from infection date inclusive
2. 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.

1. 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.
2. 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.
3. 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.
4. 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.
5. 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

1. Items 1,2 – Deaths from 2 days were probably recorded under one date
2. Item 3 unusually high figure comparing to nearby points
3. Item 4 negative deaths count (-1918)
4. Items 5, 6 unusually high figure comparing to nearby points
5. Item 7 negative death count (-2)
6. 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

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:

1. death date
2. death place
3. sex
4. 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.

1. Very few people under 40 die with Covid-19
2. 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.
3. 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.

1. Men have significantly higher death count till 80, after 80 both distributions are almost identical

1. 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

1. There are little fatalities under 40, just 27 out of 1747 or 1.5%
2. Covid-19 fatalities are much higher among men than women
3. Death age distribution shows Covid-19 does not represent a significant threat for population since it spares younger part if it.
4. 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.
5. 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:

1. Second wave data are shown from 1st Oct
2. 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
3. Both 1st and 2nd wave data are presented on same chard for visual comparison
4. Deaths per million people are shown on left axis, cases on right one

Belgium

1. We see daily cases during 2nd wave (peak 1600) significantly surpass 1st wave (peak 200).
2. 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.
3. 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

1. We see pattern similar to Belgium one: cases 100 vs 600, deaths 14 vs 3. Second wave brings more cases but less deaths
2. 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

1. Both cases and deaths during 2nd wave significantly exceed 1st wave figures.
2. Lock down during 1st phase delayed Covid-19 impact but failed to eliminate it

Czechia

1. Czechia shows similar pattern to Poland, unfortunately its cases and deaths figures are much higher
2. Czechia 2nd wave daily cases are similar to Belgium, but daily deaths are much higher (12 vs 5 for Belgium)

Sweden

1. Sweden daily cases 2nd wave exceed 1st wave figures, but difference is not as high as Belgium one (2x vs 8x)
2. 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

1. 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.
2. Countries with high daily deaths rate during 1st wave have significantly lower rates now. Cumulative deaths estimates as presented in Covidmeter remain valid.
3. 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.
4. 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

1. Swedish approach to Covid-19 resulted in slower drop of daily deaths but no 2nd wave pick up is visible so far.
2. 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.
3. In terms of daily deaths growth 2nd wave is less violent than 1st one.
4. 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.
5. Swedish approach to epidemic – no lock down but elevated hygiene standards and limits on mass gatherings seem to work better.
6. Lock downs have a bang in media, but results achieved so far do not justify significant economic burden incurred.
7. 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.
8. 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.

1. No Covid-19 fatality was registered in San Marino since May.
2. Peru now leads in Covid-19 deaths, South America was hit hit in summer like Europe in spring.
3. 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.
4. 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
5. 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).

Covidmeter findings:

1. 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
2. All quiet in China, reporting is key success factor I presume
3. Covid-19 worldwide deaths toll scary in absolute numbers pales compared with human population size
4. 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:

1. Suffix 1M – per 1 million inhabitants data.
2. Suffix 7d MA – 7 days moving average
3. 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

1. 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.
2. 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.
3. Daily deaths maximum straight number (Ireland) and moving average (Belgium) were scored back in April
4. 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.

1. Countries are sorted by cumulative cases per 1 million people
2. 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?

1. Countries are sorted by cumulative deaths per 1 million people
2. 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?

1. Countries are sorted by cases mortality
2. 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:

1. Dashed lines represent reference countries, we are looking for historical maximum in the following categories:
   1. cumulative cases (cc),
   2. cumulative cases per 1 million people (cc_1M),
   3. cases per 1 million people 7 days moving average (cma_1M)
2. Solid lines represent 3 countries with top recent cma_1M.
3. Country once selected is excluded from subsequent selections.

Rule for deaths comparison is similar to cases one.

1. 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.
2. South America and Persian Gulf is Covid-19 are hot spots, again lock down does not prevent Covid-19 spread.

1. 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:

1. More than 20 cumulative deaths and 10000 cumulative cases
2. Sort by cases mortality rate
3. Take top 5 and bottom 5 from above list
4. Add Germany to result

1. Top mortality countries is stable
2. Russia left low mortality group

Chart below shows countries with lowest mortality rate with Russia added.

1. Russia has very high number of cases, almost 400 thousands
2. 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

1. Leaders group is constant

Covidmeter

Covidmeter compares cumulative deaths per million people for top countries with reference. Reference countries are:

1. Diamond Princess cruise ship
2. 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.

1. No country came close to Diamond Princess death toll declared limit at the beginning of April
2. 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.

Covidmeter findings:

1. Lock down does not prevent Covid-19 spread
2. 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
3. Covid-19 deaths toll scary in absolute numbers pales compared with population size
4. 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:

1. Suffix 1M – per 1 million inhabitants data.
2. Suffix 7d MA – 7 days moving average
3. 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

1. 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.
2. 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.
3. Daily deaths maximum straight number (Ireland) and moving average (Belgium) were scored back in April
4. 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.

1. Countries are sorted by cumulative cases per 1 million people
2. 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?

1. Countries are sorted by cumulative deaths per 1 million people
2. 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?

1. Countries are sorted by cases mortality
2. 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:

1. Dashed lines represent reference countries, we are looking for historical maximum in the following categories:
   1. cumulative cases (cc),
   2. cumulative cases per 1 million people (cc_1M),
   3. cases per 1 million people 7 days moving average (cma_1M)
2. Solid lines represent 3 countries with top recent cma_1M.
3. Country once selected is excluded from subsequent selections.

Rule for deaths comparison is similar to cases one.

1. 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.
2. Singapore (SG) is no longer among top cases countries.
3. South America and Persian Gulf is Covid-19 are hot spots, again lock down does not prevent Covid-19 spread.

1. Deaths per 1 million people are dropping
2. Brazil replaced Italy and its deaths per million are on rise.
3. 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:

1. More than 20 cumulative deaths and 10000 cumulative cases
2. Sort by cases mortality rate
3. Take top 5 and bottom 5 from above list
4. Add Germany to result

1. Top mortality countries is stable, so is low group
2. 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.

1. 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.
2. 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

1. Leaders group is constant

Covidmeter

Covidmeter compares cumulative deaths per million people for top countries with reference. Reference countries are:

1. Diamond Princess cruise ship
2. 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.

1. No country came close to Diamond Princess death toll declared limit at the beginning of April
2. 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.