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2019-ncov

COVID-19: Knowledge, Research, Software

biomass

A Python Framework for Modeling and Analysis of Signaling Systems

covid-19

covid19

Codes for paper in Epidemics 2020

covid19-sir

CovsirPhy: Python library for COVID-19 analysis with phase-dependent SIR-derived ODE models.

dinn

Disease Informed Neural Networks (DINNs) — neural networks capable of learning how diseases spread, forecasting their progression, and finding their unique parameters (e.g. death rate).

einns

[AAAI-23] Epidemiologically-informed Neural Networks

fractional-order-seiqrdp-model-for-simulating-the-dynamics-of-covid-19-epidemic

Fractional-order SEIQRDP Model for Simulating the Dynamics of COVID-19 Epidemic

infectious_disease_modelling

neuralnets-in-raw-python

Sources for book "Explaining neural networks in raw Python: lectures in Jupyter"

neuraluq

pesto

PESTO: Parameter EStimation TOolbox, Bioinformatics, btx676, 2017.

pinn-covid

PINN-COVID analyzes a plurality of epidemiological models through the lens of physics-informed neural networks (PINNs).

pinn-for-dynamic-system-

pydaddy

Python package to discover stochastic differential equations from time series data

pygom

ODE modelling in Python

pymcmcstat_examples

python-programming-for-data-science

Content from the University of British Columbia's Master of Data Science course DSCI 511.

shiksmen

smoothsde

Fit stochastic differential equation models with time-varying parameters

tensir

SIR model parameter estimation using a novel algorithm for differentiated uniformization.

thesis-code

Code used for the masters thesis "Autophagy in yeast Saccharomyces cerevisiae: single-cell and multi-cell model approaches"

time-dependent-seird-model

A Time-Dependent SEIRD Model for Forecasting the COVID-19 Transmission Dynamics

xpinns

when using, please cite "Bayesian Physics-Informed Neural Networks for real-world nonlinear dynamical systems", CMAME, https://doi.org/10.1016/j.cma.2022.115346