This project aims to incorporate SIRD dynamics with machine learning techniques to make long term predictions of the spread of COVID-19.
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Updated
Aug 28, 2020 - Python
This project aims to incorporate SIRD dynamics with machine learning techniques to make long term predictions of the spread of COVID-19.
Provides classes to simulate epidemics on (potentially time-varying) networks using a Gillespie stochastic simulation algorithm or the classic agent based method.
a Solver and visualizer for SIR Model a Mathematical Model for Infectious Disease
A report and relevant scripts detailing how quarantining has affected the spread of COVID-19 using ECDC data and an SIR model.
Use ChatGpt (openAi) by Voice i.e. using text to speech and speech to text. Voice Agent , Voice Assistant.
A model for studying competing rumor epidemics
Implementation of the Leap-Frog Method for solving ordinary differential equations describing the epidemic SIR Model for a population of N individuals, considering the variables of individuals infected I(t), the suceptible to be infeted S(t) and the individuals who have recovered from the infection R(t).
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.
Stochastic Cellular Automata epidemic models in Python with 2D simulations
Python SIR-x model implementation
COVID-19 SIR model estimation
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