Skip to content

pedrohgp02/SIRVD-COVID-Simulation

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

7 Commits
 
 
 
 
 
 

Repository files navigation

SIRVD-COVID-Simulation

Overview

This repository contains the code and documentation for modeling the spread of COVID-19 in Brazil during 2021 using the SIRVD (Susceptible, Infected, Recovered, Vaccinated, Deceased) model. The project demonstrates numerical simulation through Euler's method and includes both deterministic and agent-based approaches to capture disease dynamics.

Introduction

The primary objective of this project is to simulate the spread of COVID-19 using a modified SIR model (SIRVD) and to compare the results with an agent-based model (ABM) implemented in Python. The SIRVD model includes compartments for vaccinated and deceased individuals, providing a more detailed representation of disease dynamics.

SIRVD Model

The SIRVD model is an extension of the classic SIR model, incorporating additional compartments for vaccinated and deceased individuals. The model uses differential equations to describe the flow of individuals between compartments.

Numerical Simulation

The numerical simulation is performed using Euler's method, a straightforward technique for solving differential equations. The Python implementation provides a clear visualization of the disease dynamics over time.

Agent-Based Modeling

The agent-based model (ABM) simulates individual interactions within a population. Each agent represents an individual with specific attributes and behaviors, allowing for a more granular and realistic simulation of disease spread.

Contact

For any questions or further information, please contact pedro@uni.minerva.edu.

About

This assignment demonstrates modeling and coding through a numerical simulation of COVID-19 in Brazil using the SIRVD model. It includes variables like infection, recovery, and vaccinations. The simulation employs Euler's method for solving differential equations, offering insights into disease dynamics and public health interventions.

Topics

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

 
 
 

Contributors