Modelling COVID-19 transmission in a hemodialysis centre using simulation generated contacts matrices

Area of Science:

• Epidemiology
• Computational Biology
• Public Health

Background:

• Patients with end-stage kidney disease (ESKD) undergoing hemodialysis face heightened COVID-19 risks due to frequent healthcare facility visits.
• Limited research exists on modeling SARS-CoV-2 transmission dynamics within dialysis units, despite their critical nature.

Purpose of the Study:

• To develop and validate a simulation approach for generating micro-scale contact matrices within dialysis settings.
• To utilize these matrices for predicting disease transmission scenarios in hemodialysis units.

Main Methods:

• Combined discrete event and agent-based simulation models to represent a typical large dialysis unit's operations.
• Generated micro-scale contact matrices detailing contact number, duration, and timing.
• Employed agent-based modeling with generated matrices to simulate disease spread under various conditions.

Main Results:

• Successfully generated detailed micro-scale contact matrices reflecting interactions within dialysis units.
• Demonstrated the utility of micro-simulation in estimating contact patterns relevant to disease transmission.
• Predicted disease transmission dynamics using agent-based models informed by simulated contact data.

Conclusions:

• Micro-simulation is a viable method for creating contact matrices in specialized healthcare environments like dialysis units.
• The generated contact matrices can be effectively applied to disease modeling, aiding in outbreak prevention and control strategies.
• This approach offers valuable insights for managing infectious disease risks in hemodialysis centers and similar settings.