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slidingsir

Sliding SIR Model for Epidemic Modelling

https://github.com/shwars/slidingsir

Science Score: 67.0%

This score indicates how likely this project is to be science-related based on various indicators:

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    CITATION.cff file
    Found CITATION.cff file
  • ✓
    codemeta.json file
    Found codemeta.json file
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    .zenodo.json file
    Found .zenodo.json file
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    DOI references
    Found 2 DOI reference(s) in README
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    Academic publication links
    Links to: preprints.org, zenodo.org
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    Institutional organization owner
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    Scientific vocabulary similarity
    Low similarity (11.4%) to scientific vocabulary
Last synced: 10 months ago · JSON representation ·

Repository

Sliding SIR Model for Epidemic Modelling

Basic Info
  • Host: GitHub
  • Owner: shwars
  • License: mit
  • Language: Jupyter Notebook
  • Default Branch: master
  • Size: 2.7 MB
Statistics
  • Stars: 7
  • Watchers: 2
  • Forks: 8
  • Open Issues: 8
  • Releases: 1
Created about 6 years ago · Last pushed almost 5 years ago
Metadata Files
Readme License Citation

README.md

Sliding SIR Model for Epidemic Modelling

This repository contains code to perform estimation of effective reproductive number Rt of COVID epidemic based on public data. This code accompanies the following publications:

Citation

If you use any portion of this code in your research, please cite either this repository, or the following paper:

Petrova, T.; Soshnikov, D.; Grunin, A. Estimation of Time-Dependent Reproduction Number for Global COVID-19 Outbreak. Preprints 2020, 2020060289 (doi: 10.20944/preprints202006.0289.v1).

DOI

The code is distributed under MIT License

Running the Code

The easiest way to run the code is to clone the repository in [Azure Notebooks])https://soshnikov.com/azure/8-reasons-why-you-absolutely-need-azure-notebooks/), or use Visual Studio Codespaces to open the code.

<!-- ?filepath=notebooks%2FSlidingSIR.ipynb -->

Provided Files

  • notebooks/EpiModelling.ipynb is the notebook that describes the basics of SIR epidemic modelling, and applies our Sliding SIR idea to the epidemic in Moscow
  • notebooks/SlidingSIR.ipynb contains the code that applies Sliding SIR to many countries based on publicly available data, and ties this to Apple Mobility Index
  • data directory contains the snapshot of datasets used in our modelling. The code uses online publicly available datasets, but should they become unavailable - you would still be able to test the code with the data provided there

Some Results

The methodology is described in this blog post or in the paper. Here are a few obtained results:

Rt Dynamics in Russia

Rt Dynamics vs. Daily Infected Population Worldwide

Rt Dynamics for Different Countries

Owner

  • Name: Dmitri Soshnikov
  • Login: shwars
  • Kind: user
  • Company: Microsoft

Cloud Developer Advocate, formerly SE/Technical Evangelist at Microsoft Russia, Associate Professor at MIPT, HSE and MAI, Co-founder of SHWARSICO and MAILabs

Citation (CITATION.cff) 

# YAML 1.2
---
authors: 
  -
    family-names: Soshnikov
    given-names: Dmitri
    orcid: "https://orcid.org/0000-0003-1021-091X"
  -
    family-names: Petrova
    given-names: Tatiana
    orcid: "https://orcid.org/0000-0001-5569-145X"
cff-version: "1.1.0"
doi: "10.5281/zenodo.5245225"
license: MIT
message: "If you use the code from this repository, please cite it."
title: "Sliding SIR Model for Epidemic Modelling"
version: "1.0"
...

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Dependencies

requirements.txt pypi
  • matplotlib *
  • numpy *
  • pandas >=1.0.3
  • scipy *