Probabilistic Inference Models To Address Bias, Validity, And Credibility Of Infectious Disease Data

Project Overview

This project addresses data management and usage for decision support related to EIDM. This will contribute to the development of reliable models of uncertainty. The chosen approach to uncertainty management is based on probabilistic causal models, such as Bayesian networks with probabilistic inference. The credibility of data can be included in these models and assessed by calculating risk and biases using various measures of uncertainty.

Co-Project Investigator

  • Dr. Svetlana Yanushkevich, University of Calgary

What is this Project’s Impact on the Emerging Infectious Disease Modelling (EIDM) Initiative?

This project has contributed to the body of research surrounding localized modelling and agent-based modelling for the purpose of providing decision support. This project’s research contributions work towards developing and deploying epidemiological decision support systems for risk mitigation at the facility level.

Implementation of the One Health Approach

The One Health approach was pivotal in the sense of looking at the causality (environments variables, seasonality, caregivers and the patients demographics, the preventive measure variety etc.) rather than simple modeling of the disease spread constrained by the building and environment and at different macro-levels.

Focus Areas and Research Achievements

Our focus now is on decision support and real-world validation. Previously, our focus was on developing a localized simulation framework for agent-based epidemiological modelling. Our focus changed once we had achieved our initial goals.

Dr. Svetlana Yanushkevich, University of Calgary

Research Team, Collaborators and HQPs

  • Svetlana Yanushkevich, University of Calgary
  • Philip Ciunkiewicz, University of Calgary