SamuelBrand1/kenya-covid-three-waves

Model supporting "COVID-19 Transmission Dynamics Underlying Epidemic Waves in Kenya" # kenya-covid-three-waves Simulation and inference code for SARS-CoV-2 transmission within and between socio-economic groups in Kenya over the first three waves of COVID-19 in Kenya. This code repository contains the underlying model code for the paper *COVID-19 Transmission Dynamics Underlying Epidemic Waves in Kenya*. ## Prerequisites and recommended background knowledge: * Basic familiarity with the Julia programming language. * Solutions of the infection process are generated using the performant, and well documented, package SciML/DifferentialEquations.jl. Familiarisation with this package is desirable. * Hamiltonian MCMC (HMC) is implemented using the dynamicHMC.jl package. The log-likelihood function for parameters is directly defined in `KenyaCoVSD`, and log-likelihood gradients (necessary for HMC) are calculated using forward-mode automatic differentiation. The combination of ODE solutions and log-likelihood function gradients in code was inspired by DiffEqBayes.jl. A good conceptual introduction to HMC can be found here. * MCMC posterior draws for parameters are stored as a `Chains` struct from the `MCMCChains` package. The MCMC draws for each county are available from the `JLD2` objects stored in `\modelfits`, e.g. ```julia using JLD2,MCMCChains import KenyaCoVSD @load("modelfits/Nairobi_model.jld2") #Loads an object called model into Main scope which contains information about Nairobi county model.MCMC_results.chain #Summary information of MCMC posterior draws ``` ## Data sets in this repository `/opendatacsvs`folder contains .csv datafiles (see *supplementary information* for the main manuscript to read data file captions). This data is also present on the repository in the form of .jld2 datafiles, which are directly used in the tutorial notebooks (see below). ## Supplementary Information Please find the supplementary information containing further details on the data analysis, and mathematical/statistical reasoning and assumptions behind the model …