Simulate a pandemic with artificial life objects.

A simple SIR model in Python

EpiMoRPH spatial disease modeling

Novel Coronavirus (COVID-19) Cases, provided by JHU CSSE

A toolset for analyzing and creating epidemiological models.

Detecção de COVID-19 a partir de imagens de raios X de tórax utilizando uma Deep Convolutional Neural Network otimizada.

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.

Simulating the effect of social distancing on disease spread through a population graph.

Comparing four modeling approaches: System Dynamics, Agent-based Modeling, Cellular Automata, and Discrete Event Simulation using a SIR model as an example

Scripts and data from "Towards a global investigation of transcriptomic signatures through co-expression networks and pathway knowledge for the identification of disease mechanisms"

Multi-stage hierarchical estimation of disease processes on demographic observations

Explore our open-source repository focused on healthcare machine learning. We've developed predictive models for cardiovascular disease, diabetes, breast cancer, and more. Our projects employ diverse machine learning algorithms and data science techniques, enhancing early detection, diagnosis, and patient outcomes.

We are the Department of Ecological Dynamics of the Leibniz IZW! This repository stores the distill webpage of the department.

Deep Learning and AI Enthusiasts to contribute to improving COVID-19 detection using just Chest X-rays.

Based of of the 3b1b video, Simulating and Epidemic, this simulation simulates a disease outbreak in a community.

Multi-Agent Based Disease Dynamics

Program to find number of differential equations needed to express an SIR(P) network exactly.

Predictive modeling for disease outbreak prediction.

Code and Model Information for to support the paper: Yuan, X. and Crooks, A.T. (2017), From Cyber Space Opinion Leaders and the Spread of Anti-Vaccine Extremism to Physical Space Disease Outbreaks, SBP-BRIMS, pp. 114-119.

An interactive visualization of the SEIR (Susceptible-Exposed-Infectious-Recovered) model using Matplotlib. The simulation allows real-time parameter adjustments via sliders and text boxes, making it easy to explore epidemic dynamics. The model is implemented with the explicit Euler method for numerical integration.