I – Increase in hospital bed requirements linked to COVID18, from 03/25 to 03/31
This analysis confirms the rough evaluation from the cumulative data (see part II of this post). Density appears not to be significant at this momentum. It has nevertheless been kept in the model.
This is an analysis based on public data, and subject to revisions or errors including the processing.
Data sources: Géodes – données en Santé Publique, INSEE.
Analysis
Multiple Regression – COVID19 in hospitals 200331-COVID19 In hospitals 200325
Dependent variable: COVID19 in hospitals 200331-COVID19 In hospitals 200325
Independent variables:
Density
Inhabitants per km²
. Standard T
Parameter Estimate Error Statistic P-Value
CONSTANT 73.014 10.4148 7.01062 0.0000
Density -0.168036 0.425494 -0.394921 0.6938
Inhabitants per km² 0.226678 0.425421 0.532832 0.5954
Analysis of Variance
Source Sum of Squares Df Mean Square F-Ratio P-Value
Model 1.96388E6 2 981941. 95.05 0.0000
Residual 1.01239E6 98 10330.5
Total (Corr.) 2.97627E6 100
R-squared = 65.9846 percent
R-squared (adjusted for d.f.) = 65.2904 percent
Standard Error of Est. = 101.639
Mean absolute error = 70.5319
Durbin-Watson statistic = 1.69812 (P=0.0650)
Lag 1 residual autocorrelation = 0.143963
Chart : increase in bed requirements from March 25 to March 31.
II – Analysis on cumulative data to 03/31
Both population and density of population are significant factors in explaining the COVID-29 outbreak in France. With an adjusted R squared of 76.7% , to be compared to 74.4% two days ago the regression continues to gain in signification (see also F and T statistics). Indeed, the role of the Est Région continues to be reduced in the model, meaning that the entire French territory is following a trajectory closer to that of the Est.
The slopes of the least squares adjusted lines continue rising, especially concerning population density, from 0.076 on the 29th of March (cumulative data) to 0.084 on the 31st. In other words the concentration of population is the major influence behind the expected increases. From this it is easy to understand that the main needs in hospitalisations will be concentrated in the largest cities : Paris, Marseilles, Lyon, Nice… The needs for Paris (and Ile-de-France) for example will exceed 7500 beds with ventilators in the coming days.
This is an analysis based on public data, and subject to revisions or errors including the processing.
Data sources: Géodes – données en Santé Publique, INSEE.
Analysis
Multiple Regression – COVID19 in hospitals 200331
Dependent variable: COVID19 in hospitals 200331
Independent variables:
Inhabitants per km²
Population
. Standard T
Parameter Estimate Error Statistic P-Value
CONSTANT -39.4282 30.1763 -1.30659 0.1944
Inhabitants per km² 0.0898564 0.00839416 10.7046 0.0000
Population 0.000325191 0.0000396694 8.19752 0.0000
Analysis of Variance
Source Sum of Squares Df Mean Square F-Ratio P-Value
Model 1.05145E7 2 5.25727E6 165.32 0.0000
Residual 3.11647E6 98 31800.7
Total (Corr.) 1.3631E7 100
R-squared = 77.1369 percent
R-squared (adjusted for d.f.) = 76.6703 percent
Standard Error of Est. = 178.328
Mean absolute error = 112.192
Durbin-Watson statistic = 1.36107 (P=0.0005)
Lag 1 residual autocorrelation = 0.315876
Charts
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