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COVID-19 SIR model estimation

Epidemic simulation models of complex network.

Stochastic Cellular Automata epidemic models in Python with 2D simulations

Python code to analyze data and predict Covid-19 infection

A general framework for quick epidemiological ABM models

Official PyTorch implementation of Neural Enhanced Dynamic Message Passing in AISTATS 2022

Provides classes to simulate epidemics on (potentially time-varying) networks using a Gillespie stochastic simulation algorithm or the classic agent based method.

Some remarks on prior modelling for the basic reproductive number in the Susceptible-Infected-Recovered (SIR) epidemic model

Python SIR-x model implementation

Agent-based modelling of pandemics using the Susceptible, Infected, Recovered (SIR) framework.

This repository is meant as a facilitator for all those who want to learn agent based disease modelling using the mesa framework in python. When I started out, there were too few examples for this on the net. Thankful to the community helping me learn, here I will share some of my insights.

Uses Markov chain Monte Carlo to estimate the parameters of an SIR model with COVID-19 data

Julia code to evaluate SIR models

I noticed that traditional methods to predict a disease outbreak was by performing sentiment analysis on Twitter posts and Google Search terms. Unfortunately, these methods were inadequate, as Twitter and Google is not popular in all countries. So, I created a system to model and predict outbreaks without the need for social media. The system wa…

PyCoMod is a Python package for building and running compartment models derived from systems of differential equations such as the Susceptible-Infectious-Recovered (SIR) model of infectious diseases.

Explore strategies of social distancing on epidemic w/ SIR model and R - from Gandon, Lion, Day

Contains functions to simulate data from and fit SIR multievent models

The purpose of this GitHub repository is to furnish the source code corresponding to the research paper authored by me. The repository is designed to facilitate the verification of results by users who wish to apply their own datasets.

Graph based epidemic simulations.