covid19-public
covid19-severity-prediction
| covid19-public | covid19-severity-prediction | |
|---|---|---|
| 19 | 1 | |
| 959 | 227 | |
| - | 0.4% | |
| 9.7 | 0.0 | |
| about 19 hours ago | 6 months ago | |
| Jupyter Notebook | Jupyter Notebook | |
| GNU General Public License v3.0 or later | MIT License |
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covid19-public
- ε ³δΊζ₯η§δΈιη§ε ΄η«θηζζδΊθ―θ―΄....
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[OC] COVID-19 in Malaysia: Deaths, active cases and vaccination percentage
Source: https://github.com/MoH-Malaysia/covid19-public
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Request total or Cumulative people infected vs vaccination brands in Malaysia.
Raw data is here: https://github.com/MoH-Malaysia/covid19-public/tree/main/epidemic/linelist
- 95.9 per cent of adult population in Malaysia fully vaccinated
- Dear KKM dearest ,what is happening?
- Singapore needs to change the way COVID numbers are reported in an endemic world: Prof Teo Yik Ying
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8,817 new cases today
GitHub
- 10,959 new cases today
- Malaysia Covid19 data in github
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19,730 new cases today
I've happen to stumble onto it while looking around, as KJ said that they are including new data, but it is in raw CSV file format, https://github.com/MoH-Malaysia/covid19-public/blob/main/epidemic/linelist/linelist_deaths.csv
covid19-severity-prediction
What are some alternatives?
Face-Mask-Detection - Face Mask Detection system based on computer vision and deep learning using OpenCV and Tensorflow/Keras
california-coronavirus-scrapers - The open-source web scrapers that feed the Los Angeles Times California coronavirus tracker.
Open-Risk-Manual-PdfBooks - Collection of PdfBooks extracted from the Open Risk Manual
covid19za - Coronavirus COVID-19 (2019-nCoV) Data Repository and Dashboard for South Africa
finite-element-networks - Reference implementation of Finite Element Networks as proposed in "Learning the Dynamics of Physical Systems from Sparse Observations with Finite Element Networks" at ICLR 2022
hyperlearn - 2-2000x faster ML algos, 50% less memory usage, works on all hardware - new and old.
Digital-Learning-During-COVID19-EDA - In this project, we will be using data analysis tools to figure out trends in digital learning and how it is effective towards improvised communities. We will be comparing districts and states on factors like demography, internet access, learning product access, and finance.