COVID-19 Data Analysis

Tasso di letalità

Tasso di letalità istantaneo ponderato (wiCFR) per COVID-19 in Italia.

Max Pierini, Alessio Pamovio, Sandra Mazzoli

Case Fatality Rate (CFR) is usually estimated as total death over total cases. This estimate though does not take into account likely dynamic variations of fatality along the time line. Is here presented a dynamic estimation of instant case fatality rate based upon onset-to-death interval: weighted instant Case Fatality Rate (wiCFR).

METODO (preprint):

Massimo Pierini, Sandra Mazzoli, & Alessio Pamovio. (2020, November 3). Estimation of weighted instant Case Fatality Rate of COVID-19 in Italy. Zenodo. http://doi.org/10.5281/zenodo.4235485

Grezzo

Filtrato

OUTLIERS in col median (0< or >0.5)
========================================
              median        Q1        Q3        lo        hi
date                                                        
2020-06-24 -0.097484 -0.147619 -0.078086 -0.190184 -0.038130
2020-08-15  0.619608  0.516340  0.818653  0.286232  1.224806

Impute function `mean`, window 7
========================================
              median        Q1        Q3        lo        hi
date                                                        
2020-06-24  0.065129  0.036507  0.093421  0.004055  0.150556
2020-08-15  0.107225  0.089046  0.141138  0.051793  0.211517

Mediana settimanale

Confronto con cumulativo

CFRcum % wiCFR %
date
2022-05-06 0.982269 0.195879
2022-05-07 0.980554 0.182050
2022-05-08 0.979160 0.116060
2022-05-09 0.978645 0.136463
2022-05-10 0.976322 0.256681
2022-05-11 0.974538 0.195376
2022-05-12 0.973035 0.220859
2022-05-13 0.971477 0.204397
2022-05-14 0.969939 0.161740
2022-05-15 0.968726 0.115667
2022-05-16 0.968541 0.190291
2022-05-17 0.966865 0.288072
2022-05-18 0.965915 0.281836
2022-05-19 0.964816 0.229597

Mediana mensile

Vaccini e wiCFR

OLS Regression Results
Dep. Variable: wiCFR R-squared: 0.626
Model: OLS Adj. R-squared: 0.625
Method: Least Squares F-statistic: 789.0
Date: Thu, 19 May 2022 Prob (F-statistic): 1.05e-102
Time: 17:01:50 Log-Likelihood: -316.79
No. Observations: 473 AIC: 637.6
Df Residuals: 471 BIC: 645.9
Df Model: 1
Covariance Type: nonrobust
coef std err t P>|t| [0.025 0.975]
Intercept 2.9476 0.069 42.865 0.000 2.812 3.083
due_dosi_log -0.5238 0.019 -28.090 0.000 -0.560 -0.487
Omnibus: 48.027 Durbin-Watson: 0.749
Prob(Omnibus): 0.000 Jarque-Bera (JB): 60.724
Skew: 0.802 Prob(JB): 6.52e-14
Kurtosis: 3.714 Cond. No. 12.4


Warnings:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
© 2020 Max Pierini. Thanks to Sandra Mazzoli & Alessio Pamovio. ipynb-website © 2017 Peter Carbonetto & Gao Wang

Exported from Italia/wiCFR.ipynb committed by maxdevblock on Thu May 19 17:01:57 2022 revision 650, 5d3eeb3d