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Job request: 2101

Organisation:
The London School of Hygiene & Tropical Medicine
Workspace:
carehomes
ID:
4dysh2a36tlrlv5c

This page shows the technical details of what happened when the authorised researcher Emily Nightingale requested one or more actions to be run against real patient data within a secure environment.

By cross-referencing the list of jobs with the pipeline section below, you can infer what security level the outputs were written to.

The output security levels are:

  • highly_sensitive
    • Researchers can never directly view these outputs
    • Researchers can only request code is run against them
  • moderately_sensitive
    • Can be viewed by an approved researcher by logging into a highly secure environment
    • These are the only outputs that can be requested for public release via a controlled output review service.

Jobs

  • Action:
    run_models
    Status:
    Status: Succeeded
    Job identifier:
    niek2jiyocmqnyyj

Pipeline

Show project.yaml
version: '3.0'

expectations:
  population_size: 1000000

actions:
  generate_study_population:
    run: cohortextractor:latest generate_cohort --study-definition study_definition 
    outputs:
      highly_sensitive:
        cohort: input.csv

  calc_coverage:
    needs: [generate_study_population]
    run: r:latest analysis/calculate_tpp_coverage.R input.csv data/SAPE22DT15_mid_2019_msoa.csv
    outputs:
      moderately_sensitive:
        log: coverage_log.txt
        rds: tpp_msoa_coverage.rds
        csv: tpp_msoa_coverage.csv
        csv2: msoas_in_tpp.csv
        csv3: msoa_gt_100_cov.csv
        figure: total_vs_tpp_pop.png
        
  prelim:
    needs: [generate_study_population, calc_coverage]
    # last argument relates to MSOA TPP coverage >= X%
    run: r:latest analysis/prelim.R input.csv tpp_msoa_coverage.rds 80
    outputs:
      moderately_sensitive:
        log: prelim_check_log.txt

  data_clean:
    needs: [generate_study_population, calc_coverage]
    # last argument relates to MSOA TPP coverage >= X%
    run: r:latest analysis/data_clean.R input.csv tpp_msoa_coverage.rds 80
    outputs:
      moderately_sensitive:
        log: data_clean_log.txt
      highly_sensitive:
        input_clean: input_clean.rds
        
  data_check_figs:
    needs: [data_clean]
    run: r:latest analysis/data_check_figs.R input_clean.rds data/msoa_shp.rds
    outputs:
      moderately_sensitive:
        figure1: tpp_coverage_msoa.png
        figure2: tpp_coverage_carehomes.png
        figure3: tpp_coverage_map.pdf
        figure4: age_dist.png
        figure5: infection_death_delays.png
        figure6: hh_size_dist.png

  data_setup:
    needs: [data_clean]
    # last argument relates to carehome TPP coverage >= X%
    run: r:latest analysis/data_setup.R input_clean.rds data/cases_rolling_nation.csv 90
    outputs:
      moderately_sensitive:
        log: data_setup_log.txt
      highly_sensitive:
        comm_prev: community_incidence.rds
        analysisdata: analysisdata.rds
        ch_linelist: ch_linelist.rds
        ch_agg_long: ch_agg_long.rds

  descriptive:
    needs: [data_clean, data_setup]
    run: r:latest analysis/descriptive.R 
    outputs:
      moderately_sensitive:
        report: descriptive.pdf
        log: log_descriptive.txt
        data: ch_gp_permsoa.csv

  run_models:
    needs: [data_setup]
    # 
    run: r:latest analysis/run_models.R analysisdata.rds community_incidence.rds data/msoa_shp.rds 0.0
    outputs:
      moderately_sensitive:
        output: output_model_run.txt
        log: log_model_run.txt
        figure: model_coeffs.pdf
      highly_sensitive:
        fit: fits.rds
        data: testdata.rds

  validate_models:
    needs: [run_models]
    run: r:latest analysis/validate_models.R fits.rds testdata.rds
    outputs:
      moderately_sensitive:
        output: output_model_val.txt
        report: test_pred_figs.pdf
        
  run_all:
    needs: [validate_models, descriptive]
    # In order to be valid this action needs to define a run commmand and
    # some output. We don't really care what these are but the below seems to
    # do the trick.
    run: cohortextractor:latest --version
    outputs:
      moderately_sensitive:
        whatever: project.yaml

Timeline

  • Created:

  • Started:

  • Finished:

  • Runtime: 00:19:42

These timestamps are generated and stored using the UTC timezone on the TPP backend.

Job request

Status
Succeeded
Backend
TPP
Workspace
carehomes
Requested by
Emily Nightingale
Branch
master
Force run dependencies
No
Git commit hash
0d93a19
Requested actions
  • run_models

Code comparison

Compare the code used in this job request