SARS-CoV-2 genomic surveillance in Germany

This repository contains a join of the metadata and pango lineage tables of all German SARS-CoV-2 sequences published by the Robert-Koch-Institut on Github.

The resulting dataset can be downloaded here, beware it's currently around 50MB in size: https://raw.githubusercontent.com/corneliusroemer/desh-data/main/data/meta_lineages.csv

The analysis uses the genomicsurveillance python package. The main file is genomicsurveillance.ipynb.

Current share (nowcast)

Share by state over time and extrapolation

Estimated growth advantage

This shows the growth advantage over BA.5. It is only related to the relative share of variants and assumed to be fixed over time. Variation between states (dots) is typically low.

Estimated growth rate

This shows the growth rate of observed cases by lineage and by state. It varies over time as the overall growth rate changes in each state.

Absolute cases by state

Description of data

Column description:

• IMS_ID: Unique identifier of the sequence
• DATE_DRAW: Date the sample was taken from the patient
• SEQ_REASON: Reason for sequencing, one of:
  • X: Unknown
  • N: Random sampling
  • Y: Targeted sequencing (exact reason unknown)
  • A[<reason>]: Targeted sequencing because variant PCR indicated VOC
• PROCESSING_DATE: Date the sample was processed by the RKI and added to Github repo
• SENDING_LAB_PC: Postcode (PLZ) of lab that did the initial PCR
• SEQUENCING_LAB_PC: Postcode (PLZ) of lab that did the sequencing
• lineage: Pango lineage as reported by pangolin
• scorpio_call: Alternative, rough, variant as determined by scorpio (part of pangolin), this is less precise but a bit more robust than pangolin.

Excerpt

Here are the first 10 lines of the dataset.

IMS_ID,DATE_DRAW,SEQ_REASON,PROCESSING_DATE,SENDING_LAB_PC,SEQUENCING_LAB_PC,lineage,scorpio_call
IMS-10294-CVDP-00001,2021-01-14,X,2021-01-25,40225,40225,B.1.1.297,
IMS-10025-CVDP-00001,2021-01-17,N,2021-01-26,10409,10409,B.1.389,
IMS-10025-CVDP-00002,2021-01-17,N,2021-01-26,10409,10409,B.1.258,
IMS-10025-CVDP-00003,2021-01-17,N,2021-01-26,10409,10409,B.1.177.86,
IMS-10025-CVDP-00004,2021-01-17,N,2021-01-26,10409,10409,B.1.389,
IMS-10025-CVDP-00005,2021-01-18,N,2021-01-26,10409,10409,B.1.160,
IMS-10025-CVDP-00006,2021-01-17,N,2021-01-26,10409,10409,B.1.1.297,
IMS-10025-CVDP-00007,2021-01-18,N,2021-01-26,10409,10409,B.1.177.81,
IMS-10025-CVDP-00008,2021-01-18,N,2021-01-26,10409,10409,B.1.177,
IMS-10025-CVDP-00009,2021-01-18,N,2021-01-26,10409,10409,B.1.1.7,Alpha (B.1.1.7-like)
IMS-10025-CVDP-00010,2021-01-17,N,2021-01-26,10409,10409,B.1.1.7,Alpha (B.1.1.7-like)
IMS-10025-CVDP-00011,2021-01-17,N,2021-01-26,10409,10409,B.1.389,

Suggested import into pandas

You can import the data into pandas as follows:

#%%
import pandas as pd

#%%
df = pd.read_csv(
    'https://raw.githubusercontent.com/corneliusroemer/desh-data/main/data/meta_lineages.csv',
    index_col=0,
    parse_dates=[1,3],
    infer_datetime_format=True,
    cache_dates=True,
    dtype = {'SEQ_REASON': 'category',
             'SENDING_LAB_PC': 'category',
             'SEQUENCING_LAB_PC': 'category',
             'lineage': 'category',
             'scorpio_call': 'category'
             }
)
#%%
df.rename(columns={
    'DATE_DRAW': 'date',
    'PROCESSING_DATE': 'processing_date',
    'SEQ_REASON': 'reason',
    'SENDING_LAB_PC': 'sending_pc',
    'SEQUENCING_LAB_PC': 'sequencing_pc',
    'lineage': 'lineage',
    'scorpio_call': 'scorpio'
    },
    inplace=True
)
df

License

The underlying files that I use as input are licensed by RKI under CC-BY 4.0, see more details here: https://github.com/robert-koch-institut/SARS-CoV-2-Sequenzdaten_aus_Deutschland#lizenz.

The software here is licensed under the "Unlicense". You can do with it whatever you want.

For the data, just cite the original source, no need to cite this repo since it's just a trivial join.