Skip to content

Latest commit

 

History

History
103 lines (87 loc) · 7.9 KB

README.md

File metadata and controls

103 lines (87 loc) · 7.9 KB
Raw

Build Status Build Status

covate

Covate uses the COG-UK metadata to examine the relationship of SARS-CoV-2 lineage growth between a specified list of UK regions. It can forecast the time series for lineages of SARS-CoV-2 that are common to all the specified regions. It can also be used to investigate the likelihood of lineages being imported between the regions. When provided with the relevant metadata, it can also produce an analysis of the lineages observed in the Welsh health boards.

Covate consists of four analyses:

  1. PREDICT, --predict ,-p
    Covate can forecast the time series of sequenced cases for lineages that are common to all the regions. The selection of either a VAR or VECM model is automated on a per lineage basis from the results of a cointegration test. The selection of parameters for the chosen model is also automated.

  2. VALIDATE, --validate, -v
    Covate can also build validation forecasts for existing metadata. For example, the validation forecast from 30/8/2021 would be a replicate of the prediction forecast from 16/8/2021 (when running with default parameters). The validation forecast is plotted against the actual time series.

  3. CROSS-CORRELATION, --cross-correlation, -c
    Covate can run a cross-correlation analysis that investigates the likelihood of lineages of SARS-CoV-2 being imported between the regions.

  4. HEALTHBOARD, --health-board, -b
    When provided with a csv for the Welsh health boards containing the following columns: cog_id, HB; covate runs an analysis for lineages observed in the Welsh health boards, including a plot of the number of individual lineages observed on a given day in each health board.

Install

The recommended Python versions for running covate are 3.7.x - 3.9.x (other versions may work but are untested).

For stability, it is recommended you download the latest release and install using python setup.py install

To install the very latest updates (as on main branch) you can use pip with git+:

 pip install git+https://github.com/Pathogen-Genomics-Cymru/covate.git

Usage

To run all four analyses with default arguments:

covate -i cog-metadata.csv -o output_dir -p -v -c -b -s welshHB-metadata.csv

A full description of the available arguments and their default values can be found below.

Help message:

usage: covate [-h] -i METADATA -o OUTPUT [-r REGIONS] [-a ADM]
              [-l LINEAGETYPE] [-t TIMEPERIOD] [-e ENDDATE] [-p] [-v] [-c]
              [-b] [-s HBCSV] [-f PRIMARYREGION] [-m MAXLAGS] [-n NSTEPS]

optional arguments:
  -h, --help            show this help message and exit
  -i METADATA, --input-csv METADATA
                        Input metadata csv, expects columns: cog_id,
                        adm1/adm2, sample_date, lineage/uk_lineage
  -o OUTPUT, --output-dir OUTPUT
                        Output directory for the results
  -r REGIONS, --region-list REGIONS
                        Input list of regions to compare, e.g. Wales, England
  -a ADM, --adm ADM     Select either adm1 or adm2
  -l LINEAGETYPE, --lineage-type LINEAGETYPE
                        Select either lineage or uk_lineage
  -t TIMEPERIOD, --time-period TIMEPERIOD
                        Select time period in weeks to take from metadata
  -e ENDDATE, --end-date ENDDATE
                        Select end date to take from metadata. Format: d/m/Y
  -p, --predict         Run prediction forecast
  -v, --validate        Run validation forecast
  -c, --cross-correlation
                        Run cross-correlation analysis
  -b, --health-board    Run health-board analysis
  -s HBCSV, --hb-csv HBCSV
                        Input healthboard csv, expects columns: cog_id, HB
  -f PRIMARYREGION, --primary-region PRIMARYREGION
                        Region of primary interest for cross-correlation
  -m MAXLAGS, --max-lags MAXLAGS
                        Maximum number of lags to investigate
  -n NSTEPS, --n-steps NSTEPS
                        Number of days to predict

Arguments

  • --input-csv
    Input metadata csv. The following columns are required: cog_id, adm1/adm2, sample_date, lineage/uk_lineage
  • --output-dir
    Output directory for results
  • --region-list
    Input list of regions to compare. Default Wales, England
  • --adm
    Select adm the regions belong to (adm1 or adm2). Default adm1
  • --lineage-type
    Select whether to compare global or uk lineages (lineage or uk_lineage). Default uk_lineage
  • --time-period
    Select time period in weeks to take from the input metadata csv. Default 12
  • --end-date
    The end date of the time period to take from the input metadata csv. Expected format is d/m/Y, e.g. 31/7/2021. Default latest date in the metadata -7 days (to account for lag in data)
  • --predict
    If specified, prediction forecasts will be created
  • --validate
    If specified, validation forecasts will be created
  • --cross-correlation
    If specifed, cross-correlation analysis will be run
  • --health-board
    If specifed, health board analysis will be run
  • --hb-csv
    Input Welsh health board csv. The following columns are required: cog_id, HB
  • --primary-region
    Primary region for cross-correlation and health board analysis. Default Wales
  • --max-lags
    Select maximum number of lags to investigate. Default 14
  • --n-steps
    Number of days to predict. Default 14

Workflow

Output

A date-stamped output directory is created with sub-directories for each common lineage, and cross-correlation and healthboard sub-directories. At the top level you will find a csv of the timeseries and summary error log file(s) for prediction and validation (provided --predict and --validate). The cross-correlation sub-directory contains multiple plots and csvs from the cross-correlation analysis (provided --cross-correlation). Similarly, the healthboard sub-directory contains the plot and csvs from the healthboard analysis (provided --health-board). In a lineage sub-directory you should find the following directories and plots:

  • prediction The forecasted time series for each region. This directory will be empty if --predict is not specified. If --predict has been specified and directory is empty then the forecast has failed to run (check logs/prediction for the error log).
  • validation A validation forecast for each region (plots the time series for the last nsteps prior to the set end date with a forecast). This directory will be empty if --validate is not specified. If --validate has been specified and the directory is empty then the forecast has failed to run (check logs/validation for the error log).
  • logs There are separate log files for prediction and validation. Log files $lineage_model.txt contain information on the built models. If there are any errors raised for the lineage then an error log $lineage_error.txt will also be generated. There are also csvs of the forecasted time series values.
  • additional-plots Time series for the lineage and ACF plots for each region. There may be additional VAR plots if relevant.

Error Log

There are separate error log files for prediction and validation. The error logs will likely contain ERROR and WARN messages for some lineages. ERROR messages indicate a fatal error where the code was unable to build a model for a lineage due to poor quality data. WARN messages indicate a non-fatal error, in this case the model should build for a lineage, but the message may indicate that the model might not be accurate (e.g. A WARN message is recorded if causality is not found).