Research

Theme 3: Early Warning Systems of Infectious Diseases

Analysis of early warning signals is a crucial component of a coordinated response to emerging infectious diseases (EIDs). The goal of OMNI-RÉUNIs’ projects is to provide both an analysis of these signals and of the possibility of disease establishment, and collectively these could be used to inform public health regarding the level of disease threat.

Signal Detection from Social Media

Co-Project Investigators: Aijun An, Jude Dzevela Kong and Manos Papagelis (York University)

This project aims to extract features or signals from social media text that are relevant to disease outbreak and at the same time identifying fake signals. We will apply natural language processing techniques, such as linguistic feature extraction, event detection, and deep neural network-based feature extraction, to identify potential signals for disease outbreak. We will investigate machine learning methods for predicting disease outbreak given the identified signals or features.

Digital disease surveillance for Emerging Infectious Diseases

Co-Project Investigator: Zahid Butt (University of Waterloo)

This project aims to analyze people’s internet search behaviour regarding health-related information and social media use to guide real-time surveillance of emerging diseases and help predict epidemics. A digital surveillance system would act as an early warning system and help public health authorities and hospitals to plan and respond to emerging infectious disease threats.

From process to structure of early warning signals

Co-Project Investigators: Frithjof Lutscher (University of Ottawa), Iain Moyles (York University), and Bouchra Nasri (Université de Montréal)

This project aims to study the structure of early warning signals (EWS); it is based on mechanistic models for epidemic and social processes (e.g., disease dynamics, pathogen shedding, doctor’s visits, use of social media, contact network). Model simulations will allow us to study the expected structure of EWS and to quantify the contribution of various processes and network topology to this structure.

Mobility-based models (spatiotemporal) for epidemic spreading, targeted interventions, and predictions

Co-Project Investigator: Zahid Butt (University of Waterloo)

This project aims to analyze people’s internet search behaviour regarding health-related information and social media use to guide real-time surveillance of emerging diseases and help predict epidemics. A digital surveillance system would act as an early warning system and help public health authorities and hospitals to plan and respond to emerging infectious disease threats.

Mining and Summarization of Early Warning Pandemic Signals for vector-borne diseases (Lyme and Chikungunya, etc.)

Co-Project Investigators: Gias Uddin (University of Calgary), Bouchra Nasri (Université de Montréal), Jude Dzevela Kong (York University), and Mark Lewis (University of Alberta)

This project aims to develop algorithms and tools to automatically detect early warning signals (EWS) for a pandemic in multiple available data sources like internet activity (e.g., Twitter, Facebook, etick.ca). Social media data and web-scraping are especially effective to detect and understand public sentiment for some infectious diseases (ID).We will investigate the design algorithms to detect signals from social media texts, e.g., detection of pandemic-related entities and events, tracing opinions about a particular event across multiple sources, and offering clues of contrastive viewpoint.

Determining a characteristic structure within multiple early warning signals via machine learning and statistical approaches

Co-Project Investigators: Mark Lewis, Hao Wang, Russ Greiner (University of Alberta) and Pouria Ramazi (Brock University)

This project aims to use statistical analysis and machine learning to determine a characteristic structure within multiple early warning signals driven by a disease outbreak. More specifically, we will develop machine-learning models to detect the structural signatures in the coordinated surveillance data of EWS1 and to accurately identify the presence of an outbreak. We will then use statistical analyses and machine learning techniques to detect early warnings from objective EWS2. Bayesian networks will be applied to identify key features and reveal the statistical dependencies between the features.

Network Modelling Approach for Predicting the International and Domestic Spread of Emerging Infectious Disease

Co-Project Investigator: Junling Ma (University of Victoria)

This project aims to develop methods that a) evaluate the risk of case importation into major Canadian cities through international travel; b) detect and give early warnings to domestic spread for cities with imported cases; and c) evaluate the risk of case spread from these to other regions in Canada through domestic travel. When an emerging infectious disease kindles an epidemic outside Canada, it is crucial to estimate the rate that cases land in Canada through international travel, and identity which Canadian cities are at a high risk to see such cases, and the effectiveness of travel restrictions, isolation and quarantine may affect the rate of case importation.