/imbalanced-contact-matrices

Study to assess the effect of imbalanced social contact matrices on infectious disease dynamics

Primary LanguageR

Failure to balance social contact matrices can bias models of infectious disease transmission

Contributors: Mackenzie A. Hamilton1, Jesse Knight1,2, Sharmistha Mishra1,2,3,4

1MAP Centre for Urban Health Solutions, Unity Health Toronto; Toronto, Canada
2Institute of Medical Science, University of Toronto; Toronto, Canada
3Dalla Lana School of Public Health, University of Toronto; Toronto, Canada
4Division of Infectious Diseases, Department of Medicine, University of Toronto; Toronto, Canada

Correspondene to: mackenzie.hamilton@mail.utoronto.ca and/or sharmistha.mishra@utoronto.ca

Descsription of Study

Research Question: How do imbalanced contact matrices from age-stratified populations bias tranmsission dynamics of infectious diseases?

Research Aims:

  1. Assess the effect of imbalanced contact matrices on the basic reproduction number of an infectious disease across 177 demographic settings
  2. Construct a theoretical susceptible exposed infected recovered tranmission model of SARS-CoV-2 stratified by age, to assess the effect of imbalanced contact matrices on infection transmission dynamics
  3. Simulate age-specific vaccination strategies within the SEIR model to assess the effect of imbalanced contact matrices on impact of targeted public health interventions

Description of Repository

  1. Code: Code to clean raw Prem 2021 data and derive balanced contact matrices (Contacts.R), model code (Model.R), code to obtain results (Main.R), and code to plot results (Results.R)
  2. Data: Data used to run code
  3. Figures: Figures output from Results.R
  4. Output: Data output from Main.R