Comments (6)
thanks for the reference. we'll try to make these numbers available through the app asap
from covid19_scenarios.
We have added fields now. Thanks for the pointers. We'll clean up the input data and hopefully have a table that can be readily extended. Making the fatality rate a function of load is on the list!
from covid19_scenarios.
Great work,
I would second this request (actually the big question now). Adding total beds or beds per 100.000 and add a transition rate between C and D with ICU and C and D' for critical cases above number of available beds.
For default values per country this sources can be used:
https://link.springer.com/article/10.1007/s00134-012-2627-8
https://gateway.euro.who.int/en/indicators/hfa_479-5061-number-of-acute-care-hospital-beds/
(Europe only)
Thank you! Great work
from covid19_scenarios.
'ICM/ICU beds' overstates capacity. There are generally less ventilated beds than physical beds in an Intensive Care Unit. Given the numbers of critically ill with SARS/ARDS COVID-19 produces and their subsequent long ICU stay, the important number is really ventilated beds rather than ICM/ICU beds. Once ventilator capacity is occupied, it is the critically ill in excess of that number (that need ventilation but can't be accommodated) who will become inevitable mortalities.
2010 numbers for Australia and New Zealand are here: https://journals.sagepub.com/doi/pdf/10.1177/0310057X1003800124
Given the overstatement of ICM/ICU capacity provided by official numbers in this context, the ability to directly enter known ventilated bed capacity for the simulated area, rather than pull down from an ICU/ICM number list is more helpful. Perhaps even more so if it could be changed to reflect increasing capacity during the selected term of the simulation (that's only a 'nice to have' though).
from covid19_scenarios.
I must agree with @aeon-lakes. Maybe is better to have it as a value or even as a time-dependent value as used on mitigation.
from covid19_scenarios.
This paper: https://www.nejm.org/doi/full/10.1056/NEJMoa2002032
Suggests 2.3% of treated COVID-19 patients or around half of ICU patients were invasively ventilated. It doesn't state whether ventilation was indicated for more, but that's a ballpark figure to calculate ventilator demand? Cases above that calculated number would become deaths.
from covid19_scenarios.
Related Issues (20)
- Option for adding Google Mobility data as an additive mitigation NPI HOT 3
- Option for correcting observed case counts using test positivity data and/or recorded deaths HOT 3
- Simulation plot changes every time refresh is pressed. HOT 1
- 🇧🇷 Brazil case data is incorrect HOT 7
- Step-by-step guide for parameter adjustment HOT 5
- Port to Next.js HOT 1
- Split app data per region and load on demand
- Don't bunde the data HOT 7
- weekly cases(data) vs weekly death (model) HOT 7
- Document, improve schema modification workflow
- Second wave
- Seroprevalence
- I want to contribute for Spanish translation HOT 4
- Missing Patients in hospital (data) in the results HOT 5
- Not all strings are translated
- API HOT 4
- Include effect of vaccination HOT 3
- Outcomes Summary Table
- Missing (And wrong) data shown before 08-16-2020 HOT 3
- [Security] Workflow eslint.yml is using vulnerable action reviewdog/action-eslint
Recommend Projects
-
React
A declarative, efficient, and flexible JavaScript library for building user interfaces.
-
Vue.js
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
-
Typescript
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
-
TensorFlow
An Open Source Machine Learning Framework for Everyone
-
Django
The Web framework for perfectionists with deadlines.
-
Laravel
A PHP framework for web artisans
-
D3
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
-
Recommend Topics
-
javascript
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
-
web
Some thing interesting about web. New door for the world.
-
server
A server is a program made to process requests and deliver data to clients.
-
Machine learning
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
-
Visualization
Some thing interesting about visualization, use data art
-
Game
Some thing interesting about game, make everyone happy.
Recommend Org
-
Facebook
We are working to build community through open source technology. NB: members must have two-factor auth.
-
Microsoft
Open source projects and samples from Microsoft.
-
Google
Google ❤️ Open Source for everyone.
-
Alibaba
Alibaba Open Source for everyone
-
D3
Data-Driven Documents codes.
-
Tencent
China tencent open source team.
from covid19_scenarios.