Like this

sbatch -p ckpt -A csde-ckpt --array=1-72 --job-name=wave0 --ntasks-per-node=28 --mem=100G --time=1:00:00 --export=ALL,wave=0 runsim.sh
sbatch -p ckpt -A csde-ckpt --array=1-58 --depend=afterany:582211 --job-name=wave1 --ntasks-per-node=28 --mem=100G --time=1:00:00 --export=ALL,wave=1 runsim.sh
sbatch -p ckpt -A csde-ckpt --array=1-58 --depend=afterany:582283 --job-name=wave2 --ntasks-per-node=28 --mem=100G --time=1:00:00 --export=ALL,wave=2 runsim.sh
sbatch -p ckpt -A csde-ckpt --array=1-58 --depend=afterany:582295 --job-name=wave3 --ntasks-per-node=28 --mem=100G --time=1:00:00 --export=ALL,wave=3 runsim.sh
sbatch -p ckpt -A csde-ckpt --array=1-58 --depend=afterany:582306 --job-name=wave4 --ntasks-per-node=28 --mem=100G --time=1:00:00 --export=ALL,wave=4 runsim.sh
sbatch -p ckpt -A csde-ckpt --array=1-58 --depend=afterany:582345 --job-name=wave5 --ntasks-per-node=28 --mem=100G --time=1:00:00 --export=ALL,wave=5 runsim.sh

Can do lookup of depend based on jobname with this apparently:

sbatch --dependency=$(squeue --noheader --format %i --name <JOB_NAME>)

Please move these over from your fx.R file to here, and the update the calibration workflow to use the package.

if no files found then specify

should leave the .out file blank if sucess, currently has two nulls if done

When running EpiABC to estimate EpiModel parameters, EpiABC produce sample posteriors whose mean accurately hits target statistics. However, when using estimated model parameters, EpiModel has not been able to replicate this performance in practice, e.g. fails to achieve equilibrium within a prespecified time frame.

I used EpiABC to calibrate my model to the prevalence of diagnosed infection (scripts here), but when I simulated using the means of the posterior distribution, I found that true prevalence (i.prev, in blue) instead of diagnosed prevalence (in red) is matching the target value. I calibrated to prevalence in years 70-79 (prevalence is still fluctuating at this point, which is related to a separate issue that I'm addressing now, but the mean true prevalence in these years is pretty closet to the target value).

I couldn't find anything in my scripts that would explain this, so on a whim, I tried defining i.prev in my model to be the same as diagnosed prevalence, and I defined a new variable for true prevalence. With this change, diagnosed prevalence (in red) is close(ish) my calibration target value of 0.0819 in years 70-79.

This suggests to me that there may be something hard-coded in the EpiABC functions to fit to "i.prev" by default.