The SIR Model is used to simulate the spread of the disease in the population, and the MCMC method is used to estimate the parameters.
Data visualization to plot confirmed cases, deaths, and recoveries in Germany over time.
1.SIR Model definition, using Python's Theano library to implement SIR Model.
2.Prediction creation to predict future disease spread using the defined SIR Model.
3.Probabilistic modeling was performed using PyMC3, and the probabilistic model was set using the data to estimate the parameters of the SIR Model.
4.Simulation and forecasting, running simulations to predict where the epidemic will go under different scenarios using the estimated parameters.
5.The results are visualized by plotting the effective reproduction number and the effective growth rate in different scenarios to assess the impact of the intervention.
python 3.9
jupyter 1.0
pandas 1.4
numpy 1.21
matplotlib 3.5
scipy 1.9
pymc3 3.11
theano 1.0
arviz 0.12
seaborn 0.11
https://github.com/Priesemann-Group/covid19_research
https://github.com/Priesemann-Group/covid_bayesian_mcmc
https://github.com/ada-k/ChangePointAnalysis_Covid19Interventions
https://github.com/Jessie1024/COVID-19-U.S.-pandemic-prediction