Italy Story Model

This Page shows how according to this model the Italy story would be described.              

It is only an attempt to describe subject to corrections, improvements, suggestions.

Many models may provide different conclusions, this one shows some consistency and is the reason why we are sharing for better research for all.

You can see our analysis on the data and cross check to see how accurate the analysis is. You can see the dates, as per our analysis, at which the curve changes and things start becoming better by clicking here.  

This country has a population of   60 Millions  
This model describes best if situation started January 12th 2020  
Each individual was infecting at a rate of 0.3 per day during 9 to 10 days   
Government proceeded to progressive lockdowns March 10th


(2,7 to 3,0)

Fits best ran models – except for South Korea that seems to capture clusters regulary

(2,7 to 3,0) Fits best ran models – except for South Korea that seems to capture clusters regulary Model may work if its started a few days later but entered 2 infected people navigating in different spheres or a superspreader appeared early on 
On April 10th  2% (Infected People)  
Expected peak infection date

April 4th
Population Immuned
Expected peak lossed date

April 15th
Population Immuned then
57.00% These numbers maybe altered by lockdown/unlock dates and policy changes to unlock
Expected herd immunity (natural R0 on population <1)

April 18th
Population Immuned
Expected losses <100/day

May 31st
Poupulation Immuned

Cloudy Sky

The lockdown does seem to have flattenned the curve

Cloudy Sky

In all cases, these numbers are death with virus and not Over Mortality caused by Corona in a valid statiscal model

Such model would require

1) Available reliable data

2) Having details of mortality by category and after the fact calculating statistically what is the real over-mortality (this is how it is done with influenza in many countries)

Remember these are numbers that reflect dead with Corona as overmortality due to Corona can be calculated after the epidemic ends having all data (which cannot happen given the overwhelming circumstances and that not all can be tested)

We know the overmortality due to virus is lower, possibly significantly lower, than that of dead with virus, and communicated numbers will end up being a guess influenced by communication needs

An interesting figure would be changes in death rate in in January, February, March, April as they become available Such statistic would result in a lower number as unfortunately some people die with or without Corona

They would reflect impact of epidemic situation, the disease itself, anxiety, lockdown, trafic reduction...

Cloudy Sky


Life finds a way and most will recover and let’s remember all those who may have left us

Cloudy Sky

Unfortunately sometimes life ends, we all try our best to have long nice best moments.

Some go with Corona (Maybe it shortenned their life, maybe not)

Some just go every day Let’s honor them all Let’s take care of a larger picture (obsessions restrict our thoughts)

A thought for healthcared professionals who have left because they were overworked

A thought for the elder who have left us with or without Corona without being able to say goodbye because of confinement

Infected in blue recovered in red

Cloudy Sky

Infections and recoveries

All are stronger and a little more adapted

Much of the Population has immunity

Time to reconstruct

How does the model test against reality

Hypothesis confinement peak minimized if until April 12th. At this stage 2/3 of population would have been infected

Our analysis and its corelation to real available data is underneath:   

Date Real Losses Modeled Losses
09/03/20 467 448
10/03/20 631 570
11/03/20 827 725
12/03/20 1016 923
13/03/20 1266 1173
14/03/20 1441 1491
15/03/20 1809 1761
16/03/20 2158 2077
17/03/20 2503 2446
18/03/20 2978 2875
19/03/20 3405 3373
20/03/20 4032 3947
21/03/20 4825 4609
22/03/20 5476 5366
23/03/20 6077 6228
24/03/20 6820 6959
25/03/20 7503 7747
26/03/20 8215 8604
27/03/20 9,134 9533
28/03/20 10,023 10535