Generalized SEIR Epidemic Model (fitting and computation)

Simulators for Compartmental Models in Epidemiology

A tool for modeling infectious diseases.

A free, open-source tool for modeling chemical reaction networks in Python

A simple Python library for compartment models

T2N is an extension of the TREES toolbox providing an interface between Matlab and the compartmental modeling environment NEURON. It was published in (and should be cited this way): Beining M, Mongiat, LA, Schwarzacher SW, Cuntz H, Jedlicka P: T2N as a new tool for robust electrophysiological modeling demonstrated for mature and adult-born denta…

Python SIR-x model implementation

Open source epidemiological modeling in Python

The Flexible Epidemic Modeling Pipeline

CE-ABC is a code to simulate the epidemic outbreaks with mechanistic models through a cross-entropy approximate Bayesian framework.

Tools for building and calibrating compartmental models of infectious disease.

Code for modeling RSV resurgence following contact restrictions

Simulation code for Hashemi, S., and Shafiee, S., and Tetzlaff, C. (2025)."Robust Input Disentanglement Through Dendritic Calcium-Mediated Action Potentials"

Python toolkit for epidemiological modelling and reporting. SIR/SEIR/SEIRD compartmental models, sensitivity analysis, interactive visualisations, and automated HTML/Markdown/JSON reports, built for low-resource health contexts.

Master thesis for the MSc in Data Science and Economics at Università degli Studi di Milano

Repository listing all the measles resources and tools developed by ForeSITE

Open Source Perfusion Analysis Plugin for OsiriX and Horos

Epidemiologic Compartmental Modeling Monad in R

Implementation of a SIRVSD model with different age groups and heterogeneous contacts in Python for Computational Models for Complex Systems Course

Comparative study of the main techniques used for COVID modeling where the information available is infected curve. The objective is to identify those univariate techniques that produce the best results, analyzing whether the more complex models are really able to provide better predictions.