mishra-lab / imbalanced-contact-matrices

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

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

Contributors: Mackenzie A. Hamilton¹, Jesse Knight^1,2, Sharmistha Mishra^1,2,3,4

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: [e-mail] and/or [e-mail]

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