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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'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

p-j-r avatar

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