Covid19 Some Statitics

With the Covid 19 a huge lot of data are usable for some data analysis.  It is very interesting for the Datascientist that we are. It is an incredible possibility for analysis this crisis on multiple views.

For start this kind of analysis, I would take two questions in this short article about food and Covid 19.  The first question has the more simple approach and would take care about the effect of a particular alimentation and the Covid death. We hear that obesity is an important factor of death. It is an exact sentence on a short population of people gravely sad by the Covid. But in a more important population of people is it correct. My first question is : Is there a food factor favorable to catch the Covid ?

My second question will approach the necessity of truth in country declaration. The dataset used in this study is about countries. There is no more information of region , religion, etc … We hear that some countries don’t declare reality of the Covid in their country. The reasons are unclear in this dramatic situation. But we can study the effect of a country on statistical data. My second question will be more complex to study and to understand: Is china can lie on the map effect of variables on countries ?

For the food factors, the response come with the study of the variance analysis of factors against the Covid death. And the result is clear and cannot be attacked.

Factors On Covid Death F Value Pr(>F) Effect
Alcoholic.Beverages 5.707 0.0184 Medium
Animal.Products 18.474 3.4e-05 Heavy
Animal.fats 1.314 0.2538 No
Aquatic.Products..Other 0.818 0.3675 No
Cereals…Excluding.Beer 0.006 0.9408 Never
Eggs 0.341 0.5603 Never
Fish..Seafood 0.022 0.8826 Never
Fruits…Excluding.Wine 0.000 0.9983 Never
Meat 1.173 0.2809 No
Milk…Excluding.Butter 2.634 0.1070 Little
Offals 2.389 0.1247 No
Oilcrops 0.147 0.7019 Never
Pulses 0.018 0.8941 Never
Spices 2.190 0.1414 No
Starchy.Roots 0.126 0.7232 Never
Stimulants 0.086 0.7698 Never
Sugar.Crops 0.004 0.9503 Never
Sugar…Sweeteners 0.392 0.5321 Never
Treenuts 3.417 0.0668 Little
Vegetal.Products 0.336 0.5633 Never
Vegetable.Oils 1.594 0.2090 No
Vegetables 5.757 0.0179 Medium
Miscellaneous 0.353 0.5534 Never
Obesity 0.253 0.6156 Never

Yes we have a response . Obesity is not the principal factor of death in a more global population. The winning factors are Alcoholic , Animal, Milk, Treenuts and Vegetables. You should say why vegetables and not vegetal products. There is an other aspect related to the reality of a dataset ? We don’t have information on population as age, wealth,… But clearly rich countries consume a lot of Alcoholic,vegetables, animal and treenuts. Poors countries have not the same conduct with the foods. If we see Japan or south corea , we will see more fish and sea foods.

This first response is interesting. It shows that eating behavior can be an important factor during the covid crisis. The time of the second question will talk more about countries and factors effects.

In first I would introduce a map that permits to show country against their factors.

The death direction is to the right. You should see the death orientation near the Kazakhstan country. This map is extremely clear and is completely linked with effect of factors on countries. More a country is on the left more the number of deaths are important. I let you identify and understand this map. But my work is to calculate the effect of china against other countries and variables. For that we construct exactly the same map without china and we add it as a complementary variable. In clear we use exactly the points calculated without china to replace this country on the map.

China is in red. The first point is that without china the map is relatively similar. It is a good behavior. The statistical map is stable. The second point is that china is replaced near their original position in the full map.

With the data given today and without anymore related information, we can say that china has no evidence of lie in their death declarations.

Please take care of dataset reality and missed informations.