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COVIDsaturates

Project description: code to accompany 'The importance of saturating density dependence for predicting SARS-CoV-2 resurgence' authored by Emily Nightingale, Oliver Brady & Laith Yakob

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General info

Contact rates between people within a population increase with human density, up until a saturating level. Using COVID-19 associated mortality data from England, we inferred the shape of this saturating contact-rate curve. We then included this saturating contact rate in a mathematical model of SARS-CoV-2 transmission, and compared projections with standard density-independent and density-dependent models.

Technologies

  • Python - version 3.8
  • R - version 4.0.2

Setup

For math model, following modules must be installed: matplotlib.pyplot ; numpy ; datetime ; lmfit For statistical analysis, following environmental requirements:

Features

  • Standard frequency-dependent COVID-19 model: 'covid_mathmodel_fd-Copy1.ipynb'
  • Standard density-dependent (LINEAR density dependence) COVID-19 model: 'covid_mathmodel_linearDD-Copy1.ipynb'
  • Saturating density-dependent COVID-19 model for a super-high-density (like London): 'covid_mathmodel_saturating_London-Copy1.ipynb'
  • Saturating density-dependent COVID-19 model for an average-England-density: 'covid_mathmodel_saturating_Average-Copy1.ipynb'
  • Saturating density-dependent COVID-19 model for a low-density (like Cornwall): 'covid_mathmodel_saturating_Cornwall-Copy1.ipynb'
  • England's COVID-19 associated death data up until August 1st 2020: 'cov_data_Aug.txt'
  • R script needed to perform glm to show mortality is a function of population density: 'glm_deaths_density.R'

Status

Project is: finished

Contact

Laith Yakob (https://www.lshtm.ac.uk/aboutus/people/yakob.laith) - feel free to contact me!

About

code to accompany Nightingale, Brady & Yakob (2020)

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