ABC-SMC fit of the MFED of HIV

Files in this archive

• analyze_SMC.py - plots and calculates model probabilities and parameter estimates
• getSummaryStats.py - calculates summary statistics on the output of a simulation
• initial_smc.py - generates random parameter sets according to prior distribution
• next_par.py - generates new parameter sets based on accepted parameter sets from the previous iteration
• patients - text file containing the patient characteristics to match in the simulations. These were created from sequence sets (.fasta) available from Keele et al (2008) and Li et al (2010).
• simulate_all.py - created simulated dataset for a large number of parameter sets
• simulate.py - simulate a single single patient
• simulation_functions.py - all functions needed for the simulations

Running ABC-SMC fit of MFED in early HIV infection

1. generate initial parameters: python initial_smc.py [parameter_filename] this will generate 700 parameter sets. Call the script multiple times for more parameter sets (that can be run in parallel)

2. perform simulation for all parameter sets: python simulate_all.py [parameter_filename] [simulation_directory] [outfile]

In order for the next script to work, outfile should be in the format stats_set_[iter]/stats_[nr].npy

NOTE: these simulations are expensive. The simulation for a single parameter set takes ~25 seconds on a regular computer, so a parameter file as generated in step one will take ~5 hours to complete. It is however safe to stop the script while it is running, since it will save to [outfile] after every simulated parameter set and restart with the right parameter set.

3. generate new parameter sets based on the results of the simulations: python next_par.py [max_dist] [stats_dir] [new_par_dir] [name]

We did 7 iterations, with max_dist equal to 5, 2.2, 1.3, 0.8, 0.7, 0.6, 0.5

4. repeat steps 2 & 3, lowering [max_dist] as required.
5. analyze the final iteration: analyze_SMC.py this will generate a plot with the model probabilities per iteration, and plots of the posterior density of all the parameters for those models with at least 100 accepted parameter sets