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modeling-epidemics-covid-19's Introduction

Demystifying modelling of epidemics like COVID-19, demonstrated with Jupyter notebooks

COVID-19 has triggered creation of this repository. This repository would contain notebooks that analyze epidemic from different viewpoints. This is open for contribution and to build upon ideas.

Warning : I'm not an epidemiologist, infectious disease expert and would have made errors. Please do let me know of errors if you find. These are right now of "play" quality. Also, i have heavily borrowed ideas.

These terms have now gotten into our vocabulary,

  • Flatten the Curve
  • Lockdown
  • Social Distancing
  • Quarantine (self, medical, apartment)
  • Aggressive Test strategy

For Math lovers,

  • R0
  • BRN
  • SEIR
  • Exponential

These notebooks will help you understand the impact better.

Contents

Jupyter notebooks are self-documenting.

Basic Reproduction Number

notebook

SEIR model

notebook

If you can't see images, use this pdf

Visualizing and analyzing current situation

notebook

If you can't see images, use this pdf

What's coming?

Compute probability of having disease based on interview

Compute probability of responding to treatment based on interview

Links

About

Email me: [email protected]

Thanks to wonderful teachers of Data Science and Engineering, Computer Science Department, BITS Pilani, especially,

  • Prof Shan - S. Balasubramaniam
  • Prof Anita - Anita Ramachandran

modeling-epidemics-covid-19's People

Contributors

baladutt avatar charugarg93 avatar charu-garg avatar

Stargazers

 avatar taniya-1207 avatar Manas Malhotra avatar  avatar 易家言 avatar  avatar daybreak avatar Johann Ahlers avatar YC avatar  avatar Chandramouli Shama Sastry avatar Soumendra Daas avatar  avatar Rachit Jain avatar  avatar raghusc avatar  avatar Mandar Kale avatar  avatar

Watchers

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modeling-epidemics-covid-19's Issues

extension of model

Hi

Thanks for your nice and very useful code.
May I ask you to help me to extend this code to SEIRD model? D is the number of dead peoples and and 'mu': death rate, for example, mu is the rate which will be added as : (mu*I) to dI/dt

I don't know in this case, how to estimate the additional parameter "mu". I am a mathematician and not good enough in coding, but I found your current results very interesting.
Thanks
Nik

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