Welcome to the Python Outbreak.info package docs!

Here you can find information on the functions you will use to collect and analyze SARS-COV-2 data from the Outbreak.info API. Our package pulls data from the Outbreak.info API and is reflected on our Outbreak.info web interface

Installation

We recommend installing the package via pip using:

pip install python-outbreak-info

Alternatively, the package can be directly installed from source via pip:

pip install git+https://github.com/outbreak-info/python-outbreak-info.git

Getting Started

The Python Outbreak.info package contains key functions for accessing genomic and epidemiological data for SARS-CoV-2. Access to genomic data requires logging in using GISAID credentials to generate an API key, using the authenticate_new_user() function. To perform authentication, you’ll need to first run

from outbreak_data import authenticate_user
authenticate_user.authenticate_new_user()

and then you should be able access all of the functionality of the package. Most of the rest of the tools are available within the outbreak_data component of the package. For example:

from outbreak_data import outbreak_data
lin_list = ['B.1.1.7','B.1.351','B.1.617.2']
# request lineages occurring with minimum frequency of 0.05 (5%)
df = outbreak_data.lineage_mutations(lin_list,freq=0.05)
# filter mutations and sort by codon number
df = df[df['gene']=='S'].sort_values(by='codon_num')

For location-specific analyses, users will need to supply the appropriate location code corresponding to their location of interest. To do this, we provide an ID lookup tool kit via the outbreak_tools part of the package. An example lookup should look like:

from outbreak_tools import outbreak_tools
location_list = outbreak_tools.id_lookup(['Illinois','South Africa','Chile'])
# which returns ['USA_US-IL', 'ZAF', 'CHL']

Note

This project is under active development.

Core Outbreak Data Tools

• authenticate_new_user()
  • authenticate_new_user()
• cases_by_location(location)
  • cases_by_location()
• lineage_mutations(pango_lin, mutation, freq)
  • lineage_mutations()
• all_lineage_prevalences(location, startswith)
  • all_lineage_prevalences()
• global_prevalence(pango_lin, mutations, cumulative, lineage_crumbs=False)
  • global_prevalence()
• sequence_counts(location, cumulative, sub_admin)
  • sequence_counts()
• mutations_by_lineage(mutation, location, pango_lin)
  • mutations_by_lineage()
• prevalence_by_location(pango_lin, location, mutations=None, datemin=None, datemax=None, cumulative=None, lineage_crumbs=False)
  • prevalence_by_location()
• lineage_by_sub_admin(pango_lin, mutations, location, ndays=0, detected)
  • lineage_by_sub_admin()
• collection_date(pango_lin, mutations=None, location=None)
  • collection_date()
• submission_date(pango_lin, mutations=None, location)
  • submission_date()
• mutation_details(mutations)
  • mutation_details()
• daily_lag(location=None)
  • daily_lag()
• wildcard_lineage(name)
  • wildcard_lineage()
• wildcard_location(name)
  • wildcard_location()
• wildcard_mutations(name)
  • wildcard_mutations()
• location_details(location)
  • location_details()
• get_outbreak_data()
  • get_outbreak_data()
• growth_rates(lineage, location)
  • growth_rates()

Example analyses:

• Epidemiological data analyses
• Lineage Prevalence Analyses
• Mutation Data Analyses
• Dealing with Cryptic Variants