Covid-19

Introduction

I have seen several analysis and simulations of the spread. In this entry I want to use the number of beds in hospitals, the disease duration, and the infection rate to find when will the healthcare system collapse in Spain. I’ll try to link to the resoures of the assuptions I take, or where I find the numeric values I use.

Important numbers

The reproductive number is the number of people that get the virus from a person with the virus. This is highly variable and is influenced by government decisons (lock down, travel bans, …) and social behavior (See slide 11 from this deck). Many dataset are linked in this thread of Civio.

• Before any major measure in Spain was ~2.6 Source
• After major measures ? (too soon to say, we’ll need to wait a week)

In order to know how many time are people ill and how much time they might need to spend at the hospital we need to know the disease duration:

• ~20 days until death (healthy <60 years woman) Source
• 37 days and still testing positive of SARS-CoV-2 Source
• Person released without symptoms and after a month tested again positive Source

The saturation of the hospitals can be measured by the capacity they have. Ventilators, oxygen, doctors those numbers affect at the peace the doctors can attend patients, generally the number of beds is used:

• 315/100000 habitants Source

The Covid severity, from a widely circulated and verifyed source:

• 20% of infected need hospitalization (mild cases)
• 5% of infected are critically ill (severe cases)

Also we can check the difference in symptoms on the population at South Corea and Italy, via this tweet that lead to this article. Also the case of Iceland

Some of them will not recover despite any treatments by the doctors, this is the letality rate. I’ve taken the numbers from a webminar of the lead epidemiologist of Hospital Clínic which was done the 5th of March.

• Globally of 2-2.5% on China 0.7% outside
• By age
  • Below 40 years old 0.2%
  • 50-60 years old 1.3%
  • 60-70 years old 3.6%
  • 70-80 years old 8%
  • Above 80 years old 14.8%

Also compare with the information here, which takes it from this document used for GB decision mak ing.

Time from infection to disease, incubation time:

• 5-7 days Source: webminar

High risk groups are people that are more exposed to get the virus or have other comorbodities such as previous respiratory diseases or other major disease afecting the immune system (like cancer, HIV…) :

• Comorbidities ?, we’ll use MorbiditySpainR to find it out.

As seen on the death rate, the age at the moment of the infection is important. Generally Italy and Spain has a population age older than China:

• ? We’ll use the data on INE thanks to INEbaseR

Modelling

One example of such analysis is this tweet:

Acabamos de evaluar la predicción del efecto de las políticas de restricción de movilidad. Equipo @jtmatamalas @SergioGomezJ @stinomat @urv y @gomezgardenes @claragranell @sorianopanos @wlcota @unizar. Restricción de movilidad total, excepto servicios esenciales, necesaria YA. pic.twitter.com/YaWqXHw7Qv

— Alex Arenas (@_AlexArenas) March 16, 2020

We could use models based on chapter 10 of the book Network Science of Albert-Lázló Barábasi, and from slide 28.

But follwing this advice I won’t:

To my colleagues, who are highly accomplished geniuses in a variety of fields: now is not the time to start an independent public health or infectious disease modeling practice if you have no expertise in these areas.

Please get an expert to check your work. Don't just post.

— Michael Hoffman (@michaelhoffman) March 18, 2020

However, here I leave a link to the UCI bed currently on use from DATADISTA.

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##  os       Ubuntu 20.04.1 LTS          
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##  language (EN)                        
##  collate  en_US.UTF-8                 
##  ctype    en_US.UTF-8                 
##  tz       Europe/Madrid               
##  date     2021-01-08                  
## 
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##  yaml          2.2.1   2020-02-01 [1] CRAN (R 4.0.1)                   
## 
## [1] /home/lluis/bin/R/4.0.1/lib/R/library