Grants and Contributions

Funding will support a study of the emergence of conspiracy theories related to COVID-19 on social media in Canada, in order to better understand the development, dissemination and adherence to conspiracy theories.

Funding will support efforts to map and create an online database of existing expertise on Women, Peace and Security (WPS) at academic institutions and in government, military, activist and not-for-profit organizations across Canada, in English and French.

Funding will support research on Chinese intellectual property theft in Canada and its national security implications.

Funding will support research on the Canadian domestic and international politics of capacity building/training military operations, using Canada’s participation in NATO’s training mission in Iraq as a case study.

Funding will support a study of Canadian conspiracy theories that potentially promote hateful attitudes, conduct, and radicalization within the Canadian Armed Forces.

Funding will support research on defence policy and evolving role of middle powers, to identify the best-practice defence policies that Canada should consider to promote the rules-based order and deter threats as a middle power.

Funding will support work to improve the online tools and resources of the Servicewomen’s Salute Canada Portal, as well as efforts to conduct a scan of existing research on servicewomen’s experiences during COVID-19 and the links between it and online misogyny.

The COVID-19 pandemic has exposed weaknesses in public health preparedness and response to infectious diseases. A major challenge has been real-time policy and resource planning: policy analysts have limited tools to explore policy options and their impact on the trajectory of the pandemic, with a lot of their work being based on exploring predefined scenarios. Moreover, the impact of policy options on socioeconomic factors usually is decoupled from the public health analysis leading to less ideal actions that might in turn impact mental and physical health in the population. In the case of COVID-19, for example, large-scale social distancing measures enacted in response to the current pandemic were effective at limiting the spread of SARS-CoV-2; however, these measures are unsustainable interventions for long periods of time, and have shown to affect the economy, education and the mental health of many members of society. With the number of COVID-19 cases falling, quantifying the impact of lifting these policies on public health has been a major challenge for policy analysts, once again showcasing the need for tools that can support such analysis. Another major issue in analyzing the COVID-19 pandemic has been the high uncertainty in the data available about the disease and its impact on the population. Quantifying these uncertainties and their impact on model results is key to developing a proper course of action. The aim of this project is to resolve the problems highlighted above by automating the process of exploring policy options for controlling the spread of infectious diseases. Coupling artificial intelligence tools with strong agent-based epidemiological models and models of other socioeconomic factors affected by policy interventions, The University of British Columbia (UBC) believes it can improve situational awareness and policy planning in face of a pandemic like COVID-19.

Decisions are being made every day, at the level of individuals to countries, in response to the outbreak of COVID-19. Scientists from around the world have been quick to respond by developing a wide range of mathematical models to predict future COVID-19 infections and deaths. Delivering this science to decision makers in an actionable form, however, remains a challenge. Without an inhouse team of modelers ready on stand-by, most policy makers are unable to direct modeling efforts towards their daily questions and circumstances. Instead they are often forced to rely on projections made for different questions and/or jurisdictions, and often outdated by the time they are released.

Our solution to this challenge has been to develop a software framework for providing real-time forecasts of COVID-19 infections and deaths that can be rapidly deployed for use anywhere in the world. Built upon our existing SyncroSim software platform, our framework will allow end users to generate forecasts that are specific to their jurisdiction and questions. Through a collaboration with researchers from the University of British Columbia and Simon Fraser University, the framework will provide access to the best of the world’s open-source forecasting models, along with real-time daily data, in a standardized and user-friendly format. Using the latest source control techniques, scientists will be able to continually adapt and improve the framework’s underlying models as understanding of COVID-19 evolves over time. The framework will allow decision makers to assess and compare alternative model projections for local accuracy and relevance, thus building confidence over time in their forecasts. Finally, the framework provides the flexibility for policy makers to introduce their own local “what-if” scenarios regarding the effects of possible future changes to public health measures. The result is a tool that generates locally responsive, meaningful, and ultimately actionable forecasts.

Technologies that can speedily and efficiently analyze large amounts of data in real-time are playing a critical role in helping healthcare professionals and governments predict the impact and spread of the Covid-19 virus over time. Enhanced data-sharing capabilities, particularly syndromic data between the population and health professionals, are also proving to be an indispensable tool for governments to confront the pandemic. Syndrome Anomaly Detection System (SADS) is an innovative platform that will help governments manage any health emergencies efficiently. SADS is an advanced real-time symptom collection and disease spreading analytics platform. SADS uses deep learning natural language processing (NLP) to capture a set of symptoms using conversations of patients from various sources including hospital triages, doctor’s offices, telehealth/pandemic hotlines, ambulances, online reporting systems, and social media. Other important information, such as age, gender, ethnicity, time, and location, are also anonymously collected from verbal and textual conversations. SADS then aggregates the syndromic and individual characteristics from these multiple sources and apply machine learning (ML) algorithms to classify and detect sudden increases in unusual syndromes in communities. SADS will immediately notify public health and government officials upon outbreak detection. Authorized parties can access both realtime and historical information in the analytics tools and further analysis of the data can be performed and shared with others. SADS will allow governments to manage disease outbreaks efficiently and in real-time. This platform will help governments save lives, reduce health care costs and hospitalizations.