life-ops-starter-obsidian-vault
Epidemiology101
| life-ops-starter-obsidian-vault | Epidemiology101 | |
|---|---|---|
| 1 | 3 | |
| 3 | 278 | |
| - | 0.4% | |
| 4.1 | 5.6 | |
| about 2 months ago | about 2 months ago | |
| Jupyter Notebook | ||
| - | MIT License |
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life-ops-starter-obsidian-vault
Epidemiology101
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Show HN: Epidemik a Python package for epidemic simulation
Hi everyone,
I'm happy to share `epidemik`, a Python package for epidemic modeling.
Epidemik, currently supports:
- Homogeneously mixed Model: Easily define compartmental models by just specifying the transitions and rates
- Age Structure: Convert any compartmental model to an age-structured model by simply adding an age contact matrix and a the population of each age group
- Network Model: Easily define epidemic models running over an arbitrary network structure
- Meta-Population Models: Define metapopulation model
In a previous life (circa 2008-2012), I worked as the leading developer for a large-scale epidemic model so when CoVID hit I wrote a series of blog posts (see https://github.com/DataForScience/Epidemiology101) working through the mechanics of how this kind of model works. Epidemik is the result of all the code that got written during the lockdowns, and in the years since that, I finally cleaned up and decided to publish it in case others find it useful (perhaps as a teaching tool?)
This is my first package, so feedback, comments and suggestions are definitely welcome.
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Show HN: What Are You Working On?
Trying to put the finishing touches on a Python package [1] for epidemic modeling using Compartmental models. This grew out of a series of blog posts I started writing during the pandemic [2] based on my professional experience in epidemic modeling in a previous life where I was the lead developer for a state of the art global epidemic model [3].
[1] http://github.com/DataForScience/epidemik
[2] https://github.com/DataForScience/Epidemiology101
[3] https://www.gleamviz.org/explore.html
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AI outperforms conventional weather forecasting for the first time: Google study
No worries, Google does tend to do a good job of monopolizing attention in whatever they do and Epidemic Modeling is... complicated. Probably much more complicated than pretty much any other kind of modeling since people have the bad habit of thinking and acting in whatever way they want (sometimes with the explicit purpose of breaking your model :).
Now, if you want to see the real-world state-of-the-art epidemic modeling on a global scale, checkout GLEaM/GLEaMViz https://www.gleamviz.org/ (full disclaimer, in a previous life I was the lead developer).
And if you're interested in a basic intro, you can also checkout my (somewhat neglected) series of blog posts from the pandemic days: https://github.com/DataForScience/Epidemiology101
What are some alternatives?
covid-19 - Coronavirus COVID-19 Dashboard - Global Kaggle Data
california-coronavirus-scrapers - The open-source web scrapers that feed the Los Angeles Times California coronavirus tracker.
Face-Mask-Detection - Face Mask Detection system based on computer vision and deep learning using OpenCV and Tensorflow/Keras
covid_project - Data analysis project on a COVID-19 data set provided by Our World in Data
lockdowndates - Retrieve the dates of the restrictions imposed by governments in countries around the world during the covid-19 pandemic.
covid19-sir - CovsirPhy: Python library for COVID-19 analysis with phase-dependent SIR-derived ODE models.
CSGO-Pro-Gear-Performance-and-EDA - Modeling Professional (CS:GO) Gamer's Accuracy Performance Based on Gear and Settings, and Exploratory Data Analysis.