in french
Published on May 13, 2020
Here is the English epidemiological study on COVID-19, concerning 17 million medical files.For the first time, we have access to a strong estimation of the relative weights of different risk factors for death
The incredible finding of this study is that the weight of comorbidities is very low compared to age
A 45-year-old diabetic has 3 times less risk than a heathly 55-year-old
Age is by far the largest factor.
To simplify, we could almost say that it's the only factor that counts.
Our data sources:
https://www.atoute.org/n/IMG/pdf/fdrcoviduk.pdf?fbclid=IwAR0lhxtZFuQtlGcAW78fG8cq3gUjwg3RU4BjTrOXTDeno-ct84MpoCxaJSI
Published on May 08, 2020
With a delay of 16 to 20 days, there is a correlation between the number of people who look up information on Google about the loss of taste and the number of people who are in intensive care.It's true in the USA with the English keywords "loss taste"
It's true in France with the French keywords "perte gout"
It's true in Italy with the Italien keywords "perdita gusto"
We will continue to follow the frequency of searches for these keywords because they could allow us to predict by several days in avance whether a second wave is likely, and if so, in what state
I would like to remind you that Google Trends is a website by Google that analyzes the popularity of top search queries in Google Search across various regions and languages. The website uses graphs to compare the search volume of different queries over time.
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Published on May 05, 2020
Countries are not infected in the same way by COVID-19Some countries have 0 deaths declared; in some other countries there are tens of thousands of deaths
Why such differences between countries ?
For each country, we have collected these DATA:
- number of inhabitants
- population density
- wealth by inhabitant
- average life span (from 52 years old in Angola to 84 years old in Japan)
- average age (from 16 years old in Chad to 43 years old in Italy or Japan)
- quality of health system
- freedom of the press (from 0 in North Korean to 77 in Sweden)
- temperature on average in 2020 April, in the biggest city of the country
- number of deaths (declared !), by million of inhabitants, from COVID-19
Let's analyze the correlation matrix
(A correlation matrix is a table showing correlation coefficients between variables. Each cell in the table shows the correlation between two variables. A correlation matrix is used to summarize data, as an input into a more advanced analysis, and as a diagnostic for advanced analyses. The value correlation ranges from -1 to +1. +1 describes a perfect positive correlation -1 describes a perfect negative correlation 0 means no linear correlation)
4 features are highly correlated: wealth by inhabitant, average life span, average age and quality of health system.
Indeed, in a prosperous country, people live a long time, the average age is high and the quality of the health system is better
The number of deaths declared by million of inhabitants is very correlated with these 4 features.
That may sound contradictory, but the better the quality of the health care system, the more deaths from Covid-19. It's a side effect.
A good health care system has a consequence a long life span and an high percentage of older population, who themselves have the consequence of an increased death rate from COVID-19
The freedom of press and the number of deaths from COVID-19 are positively correlated: the less freedom of the press, the lower the death rate declared
Temperatures and the wealth by inhabitant are negatively correlated: the poorer a country, the hotter its weather
Temperatures are also negatively correlated with the number of deaths
But beware: an effect is correlated to its cause but two effects will also be correlated between them
1) Does an increase in temperature reduce the number of deaths ?
2) Or are there fewer deaths in countries where it is hot because they are poor countries with a young population and a low life span ?
We cannot answer that question by only analysing our matrix of correlations.
We need to deepen our study: we have to train a machine learning algorithm
The gradient boosting algorithm is considered to be the most reliable machine learning algorithm with the best results
We have trained the algorithm with our dataset
The algorithm has given us the features it considered important to explain the differences in the number of COVID-19 deaths between countries.
The most important of our variables is the average age of the population.
The temperature has virtually zero impact, and is not retained by the algorithm.
Conclusion: President Trump was wrong when he said that COVID-19 would disappear in the US with the rise in the temperatures and the arrival of spring and summer.
The temperature has no significant impact on the evolution of the pandemic. What a pity!
Our algorithm can be improved by adding other features to our dataset: we are going to do that to the next few days
We are interested in your comments (via Facebook or via Twitter)
Stay safe !
God bless America
, and God bless the world 
!
Our data sources::
data about COVID-19: https://www.jhu.edu
freedom of the press : https://rsf.org/fr/classement
quality of health system: https://fr.april-international.com/fr/sante-des-expatries/quels-sont-les-pays-avec-les-meilleurs-systemes-de-sante
average life span: https://fr.wikipedia.org/wiki/Liste_des_pays_par_esp%C3%A9rance_de_vie
average age, by country: https://fr.wikipedia.org/wiki/Liste_des_pays_par_%C3%A2ge_m%C3%A9dian
wealth by inhabitant, by country: https://fr.wikipedia.org/wiki/Liste_des_pays_par_PIB_(PPA)_par_habitant
population density: https://fr.wikipedia.org/wiki/Liste_des_pays_par_densit%C3%A9_de_population
number of inhabitants: https://fr.wikipedia.org/wiki/Liste_des_pays_par_population
Temperature on 2020 April: https://fr.tutiempo.net/
Published on April 21, 2020
"Now this is not the end.It is not even the beginning of the end.
But it is, perhaps,the end of the beginning.", Churchill
The impact of containment is appearing now: we have started the beginning of the decline. Good news !
There is a lot of inertia: so it's easy to predict the next few days
Published on April 14, 2020
How many people have been infected by Covid-19 in the United States ?It's difficult to evaluate this because we don't know the relationship between the number of people detected with COVID-19 versus the number of people infected with COVID-19: double, triple, ten times ?
For the moment, there is only one place where the whole population has been tested: it's on the Diamond Princess cruise ship.
On this ship, out of 3.711 passengers and crew members, 634 have been tested positive (half showed no symptoms).
With 7 deaths, the death rate would be 1.2%.
Problem: on this ship, the repartition by age is different than the repartition in the United States. The average age on this ship is older.
We took this into account in our calculations: after correction, we get a rate of 0.7%.
With 24.000 deaths in the United States, the number of people infected would be approximately 3 million, or 1% of the population of the United States
Unfortunately, we are very far from herd immunity
It's the same thing in the UK: with 12.000 deaths, the number of people infected would be approximately 1.7 million, or 2% of the population of the UK
Published on April 09, 2020
Who have the highest probability to be detected COVID-19 and to die of COVID-19 ?African American people are the most affected
Next come Asian
Then people of mixed raced
Then Hispanic people
Then Native Americans
Caucasian people are the less affected
We have built a correlation matrix heat map.
A correlation matrix is a table showing correlation coefficients between variables. Each cell in the table shows the correlation between two variables. A correlation matrix is used to summarize data, as an input into a more advanced analysis, and as a diagnostic for advanced analyses.
The value correlation ranges from -1 to +1.
+1 describes a perfect positive correlation
-1 describes a perfect negative correlation
0 means no correlation
Our data sources:
https://www.census.gov/library/publications/2011/compendia/usa-counties-2011.html?fbclid=IwAR298kYur4jeYdw4qmWgU0vfqq1AiOUCeh1t5f06kh2Gh-XJY36WsMV3nM0
https://docs.google.com/spreadsheets/d/1pxuTu10uO7MsBaKA554XSuCpnF--FTqwdnl_sUHfWro/edit?fbclid=IwAR0gfCEBDjKAQbi5ejlTU-tHsfpxdGIbnFZlFF9gTfDNvFYgUkGRmTmFUPU#gid=289496465
https://raw.githubusercontent.com/nytimes/covid-19-data/master/us-counties.csv?fbclid=IwAR3PUqAOnSRnKOrgBgwY3cNH8yQYWP64v7SoPVWvlL_FKFVbwe10rTiEhxw
Published on April 08, 2020
What is the weather's impact on the evolution of the COVID-19 ? The temperatures will be higher in May: will this reduce the epidemic ? At what temperature ?Does the percentage of humidity have an impact ?
Now, we can give you the results of our study about the 3242 US-counties (see our post from yesterday)
Bad news: there is no correlation between the temperature and the speed of evolution of the COVID-19.
The United States is a very large country. Currently a vast range of temperatures can be found there: betwwen 20 F and 90 F
It's the same thing for the humidity and atmospheric pressure: there is no correlation
We have built a correlation matrix heat map: the correlation coefficients between weather and evolution of the epidemic are insignificant
Published on April 07, 2020
What is the weather's impact on the evolution of the COVID-19 ? The temperatures will be higher in May: will this reduce the epidemic ? At what temperature ? Does the percentage of humidity have an impact ?We are going to try to answer these questions in the next few days.
For each of the 3242 counties in the US, we have collected data on:
- number of inhabitants
- area
- density of population
- distribution by age group
- percentage of graduates
- containment index through Google Mobility
- mean of temperature in March 2020
- mean of percentage of humidity in March 2020
- evolution of epidemic: number of cases and number of deaths
We have just finished the step of data collection and data cleaning.
Next step: to train our algorithms on our data sets.
The objective is to identify the features which are important and which have an impact on the evolution of the epidemic.
We will be careful of biases.
Our data sources:
https://www.census.gov/library/publications/2011/compendia/usa-counties-2011.html?fbclid=IwAR298kYur4jeYdw4qmWgU0vfqq1AiOUCeh1t5f06kh2Gh-XJY36WsMV3nM0
https://docs.google.com/spreadsheets/d/1pxuTu10uO7MsBaKA554XSuCpnF--FTqwdnl_sUHfWro/edit?fbclid=IwAR0gfCEBDjKAQbi5ejlTU-tHsfpxdGIbnFZlFF9gTfDNvFYgUkGRmTmFUPU#gid=289496465
https://www.timeanddate.com/weather/@5075315/historic?month=3&year=2020&fbclid=IwAR2kI5HJbghOtpvafcpl9FCE430_nLc_aWQYVjNyWrIPESaFSucU8yA3UXo
https://raw.githubusercontent.com/nytimes/covid-19-data/master/us-counties.csv?fbclid=IwAR3PUqAOnSRnKOrgBgwY3cNH8yQYWP64v7SoPVWvlL_FKFVbwe10rTiEhxw
Published on April 04, 2020
Google is giving the world a clearer glimpse of exactly how much it knows about people everywhere:using the coronavirus crisis as an opportunity to repackage its persistent tracking of where users go and what they do as a public good in the midst of a pandemic.
The reports consist of per country, or per state, further broken down into regions/counties: with Google offering an analysis of how community mobility has changed vs a baseline average before COVID-19 arrived to change everything.
Google location mobility report for Italy, which remains the European country hardest hit by the virus, illustrates the extent of the change from lockdown measures applied to the population — with retail & recreation dropping 94% vs Google’s baseline; grocery & pharmacy down 85%; and a 90% drop in trips to parks and beaches.
In our graph, we have analyzed the evolution of trips to parks and beaches.
google data: https://www.google.com/covid19/mobility/
Published on April 02, 2020
Evolution of number of deaths per million inhabitants, and by major countries.We have removed some countries (such as China) whose data are not considered reliable
Italy and Spain are the 2 countries the most affected.
The progession in Spain is even faster than in Italy
Our data source: Engineering (CSSE) at Johns Hopkins University
Published on March 30, 2020
Number of cases COVID-19 confirmed by state and per million of inhabitants.New York is the state the most affected, per million of inhabitants
A good news: Washington state has managed to slow down the epidemic
Our data source: Engineering (CSSE) at Johns Hopkins University
Published on March 29, 2020
We calculated some predictions 9 days ago (see our post from March 20)The comparison between the reality and our prediction
Published on 2020 March 28, 08.00 am EST
FLORIDA. We have calculated the evolution of the number of cases of COVID-19 confirmed per 10,000 inhabitants et by Florida's major counties.Only one county has a higher number of cases than the mean of the USA: Broward
Our data source: Engineering (CSSE) at Johns Hopkins University
Published on March 25, 2020
The evolution in New York is now very rapid and worrying.1,200 cases per million inhabitants (25,681 cases in total) have been now confirmed in New York
In the other states (like Washington, Florida or California), the number of confirmed cases is not very high and the evolution is not rapid.
Please note: we have calculated the number of confirmed cases per million inhabitants. Each state is of a different size and we can't compare apples to oranges so we needed to calculate cases per population size
Our data source: Engineering (CSSE) at Johns Hopkins University
Published on March 23, 2020
The increase in numbers of confirmed cases in the USA is very rapid.Nonetheless, when you analyse the situation state by state, you can see that only New York City is very affected.
In the other states, the number of confirmed cases is not very high and the evolution is not rapid for the moment.
Please note: we have calculated the number of confirmed cases per million inhabitants
Our data source: Engineering (CSSE) at Johns Hopkins University
Published on March 20, 2020
The evolution in the USA will be very bad.It will be worst than in Italy
Numbers of confirmed cases are increasing faster than in Italy: case count is now tripling every 3 days :-(
Stay at home ! Take care !
We have analyzed the spead of evolution, the acceleration (second derivative) and the evolution of the acceleration (third derivative).
Here our prediction for the next few days.
There is a lot of inertia. So the impact of some containment measures will not be seen before 8 days
Our data source: Engineering (CSSE) at Johns Hopkins University
Published on March 18, 2020
Evolution of cases of Coronavirus in the United StatesThe USA followed the same evolution as Italy with a delay of 10 days.
The USA followed the same evolution as France with a delay of 2 days.
But now, it's finished: numbers of cases in the USA are increasing faster than in Italy 10 days ago and faster than in France 2 days ago
In the USA, case count is tripling every 4 days
We don't undestand when we see a lot of people on the beach. Stop Spring Break parties ! Fast ! Stay at home ! To save thousands of lives
Here our prediction for the next few days
Our data source: Engineering (CSSE) at Johns Hopkins University
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