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Stochastic SIR models; adding age-structures and social contact data for the spread of covid-19. Lattice model for identifying and isolating hotspots. This has been further developed into a network(graph) of multiple clusters(lattices) and tracing the infection in such a population.

Home Page: https://p-j-r.github.io/covid-19/

License: Apache License 2.0

Python 1.30% Jupyter Notebook 98.70%
covid-19 coronavirus epidemiology-analysis stochastic-models coronavirus-analysis epidemiology sars-cov-2 epidemic-simulations sir-model disease-spread

covid-19's Introduction

Models

The following epidemiological models for the spread of SARS-CoV-2 (novel coronavirus) have been implemented:

Agent based network random walks

PDF: pjr.pdf

Network-G Network-G_

Rate of adding of clusters:(It's random!) Looks like a linear fit though! Rate

SIR- Stochastic

Using Gillespie algorithm a basic SIR model is generated.
Differential equation solution: corona_diff
Stochastic simulation: corona_stochastic
stochastic

SIR- Age Structure & Social contact based

I- Both symptomatic & asymptomatic
The age and social contact data for India that is needed to construct structured compartment models can be found at the following source:

Age Structure & Contact Matrices: data
Research Paper: https://arxiv.org/pdf/2003.12055.pdf

Source-code

SIaIsR

Hotspots & Contact Tracing model

Uses networkX package and modelling the disease spread as a lattice, providing a way to identify and isolate the Hotspots.

Sick people Averaged

covid-19's People

Contributors

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Stargazers

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Watchers

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Forkers

soumiksamanta007 ayush-ar wilsongis lucascbarbosa ligege12 wstoloah sunsummoner alysiaxu

covid-19's Issues

Divide changes data array

In line 86 of the stochastic file,
data[i]/=n
changes the shape of data. Changing it to
data[i]=data[i]/n
fixes this.

Share load age structure data?

I am looking at your model. I would like to see if I can reproduce. Can you share your "load age structure data"?

Thanks,
Mike

ValueError: empty range for randrange()

In function first_case()
corona_walk

cx,cy= nodes[randrange(len(nodes))] 
temp=Lat.nodes[(cx,cy)]['walkers']

This may potentially return an empty attribute as no walker may be at that node!

first_case=temp[randrange(len(temp))]   
infect[first_case]=1
Lat.nodes[(cx,cy)]['infected']+=1
global sick
sick=1

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