Research
Theme 1: Data Management
Why One Health Infectious Disease Data Management?
-> Environmental (physical and social), ecological, animal, and human data are key in estimating the risk that an infectious agent becomes established in an animal or human population. Animal and human mobility data allow better understanding of disease translocation.
-> Multi-host surveillance and health systems data are key to identifying emergence of new agents and in planning to mitigate spread, how to control them once established and the consequences of these control measures.
-> Data also inform mechanistic, mathematical, and statistical models used to understand infection spread which, in turn, generate additional data that have to be understood by knowledge users and stakeholders who then decide on the most promising interventions to block entry of a threat, or control its spread once established.
To achieve OMNI-RÉUNIS’ goals, our data research projects focus on data procurement, curation, validation and dissemination.
Systematic review and repository of available models
Co-Project Investigators: Hélène Carabin (Université de Montréal), Julien Arino (University of Manitoba), Bouchra Nasri (Université de Montréal),and Jane Parmley (University of Guelph)
This project aims to conduct a systematic review of modelling approaches and parameter values in published and grey literature. The team will review mathematical and statistical models applicable to the monitoring of IDs (e.g., human/animal/environmental pathogen surveillance, human behavioral, animal management systems, and syndromic surveillance).
Inventory of data sources
Co-Project Investigators: Bouchra Nasri (Université de Montréal), Hélène Carabin (Université de Montréal), Julien Arino (University of Manitoba) and Jane Parmley (University of Guelph)
This project aims to identify the types of data typically needed to build models of ID spillover, spread and control. These data can be divided into several categories: animal health; human health; environmental contamination and hydro-climatic; human demographic and socio-economic; animal management; and financial.
Credibility of various sources of data for use in models
Co-Project Investigators: Bouchra Nasri (Université de Montréal), Hélène Carabin (Université de Montréal), Julien Arino (University of Manitoba) and Jane Parmley (University of Guelph)
The development of reliable models requires eliciting, calibrating, and adjusting for bias in event probabilities that could impact decisions for early warning systems (EWS). This shall be addressed in this project via probabilistic models of uncertainty, specifically, Bayesian networks with probabilistic inference mechanisms. Data credibility can be included in these models and assessed by calculating risk and biases using various measures of uncertainty.
Visualization Techniques
Co-Project Investigators: Usman Alim (University of Calgary) and Mea Wang (University of Calgary)
This project will aggregate health data into multiple models, which primarily aim to detect EIDs but are also capable of predicting the effects of policy interventions. The main objective is to develop innovative computational approaches for 1) fitting multiple models to large quantities of health data, and 2) evaluating these models for a variety of scenarios.
Probabilistic inference models to address bias, validity and credibility of infectious disease data
Co-Project Investigators: Svetlana Yanushkevich (University of Calgary)
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 via calculating risk and biases using various measures of uncertainty..