Mathematical and Statistical Modelling of Emerging Challenges

One of the key lessons from the current COVID-19 crisis is the understanding of how unprepared the world remains to deal with an emerging disease of large scale impact. This fact rings true in all dimensions of our organized societies: government, health institutions, education, economies, public policy makers, regulation, supply chains — all of which were not prepared to react promptly and efficiently, or with the ability to scale and sustain those actions, once implemented at a local scale. This unpreparedness was also evident at the level of our data systems and our ability to promptly generate, collect, aggregate, analyze, and visualize data to enable informed decision-making processes across society. We also lacked in our capabilities to develop and deploy systems that could leverage data and state-of-the-art information and communication technology to efficiently assist with decision-making as a critical component to epidemiology.

The COVID-19 crisis uncovered the many deficiencies that exist in global preparedness for emerging diseases in particular. For example, inefficiency across private and public sector organizations (including government, health institutions, education, economies, public policy makers, regulation, supply chains, etc.) resulted in a delayed and imperfect global response to COVID-19. In particular, inflexible and disconnected data systems resulted in an inability to promptly generate, collect, aggregate and analyze the large levels of data generated by global institutions. This in turn blunted efforts to model, predict, and further analyze COVID-19, potentially resulting in inadequate responses leading to economic and social stress and the unnecessary loss of life. The same level of unpreparedness exists in general when it comes down to a larger array of emerging interconnected challenges of the century. As we enter the second quarter of this century, the environmental crisis is looming ever nearer. Addressing the structural challenges required in order to steer our economic and social activities into alignment with sustainable development goals is more urgent than ever. Yet, our societies are not well equipped to promptly respond and transfer newly generated knowledge into decision-making tools and systems in times of crisis.

In view of this, heightening our level of preparedness in the mathematical and statistical front is of utmost importance and of great strategic value. Sustained research in applied modelling is key to increase and sustain a high level of readiness. We believe that this theme is crucial to improving national and international crises response for future emerging challenges in the fields of epidemiology, environmental science, sustainable development, responsible economics and finance.

More than 90% of all data available today was produced in the last two years, and the rate of data production is only expected to increase. Therefore quantitative sciences are playing an increasingly dominant role in all aspects of science and industry, and there is a growing need for more powerful and diverse modelling and analysis tools to fully leverage this information and transform it into actionable insights. The advances generated by research in the mathematical sciences also entail socio-economic challenges that need to be addressed collaboratively by all those with stakes and expertise.

An objective of this strategic research theme is then to radically increase the quantitative sciences capabilities of Canada’s private and public sectors through structuring knowledge transfer initiatives around emerging challenges. This is aligned with the Economic Strategy Tables of the Government of Canada and it complements and augments the missions of recently established research consortia (CFREF, CIFAR-MILA, Vector, Amii) and emerging superclusters (in particular the Canadian AI consortium).

The context summarized above underlines the importance of the path:

data – > modelling – > policy

that is crucial for an efficient societal response to the emerging challenges of the century.

The CRM views the modelling node of this path as one of its main current priorities and intends to approach it by leveraging its two main strengths:

The Institute is a regroupment of top level researchers in a variety of fields, mainly in mathematical and statistical sciences but also beyond, and covering geographically most of Québec. In this capacity, the CRM has a unique role to play in Québec in focusing attention and organizing efforts on these topics.

The CRM is a leading international mathematical sciences research center and has an extensive network of provincial, national and international partners. By coordinating and collaborating with them on these important and timely scientific topics, it can significantly widen the impact of the outcomes.

The strength of data science research in Québec and of IVADO, in particular, is a crucial asset to make this program successful. Indeed, the current call for strategic research themes is a great opportunity to strengthen and render more efficiently the linkage between data and modelling.
Thus, we believe that the theme discussed here is of crucial importance and that placing it among its priorities, in partnership with the CRM and its researchers, is of high strategic value for IVADO.

Mots-clefs : artificial intelligence, optimization, health, risk, clean resources and technologies, advanced manufacturing, societal issues, environmental sciences, sustainable agro-industry, sustainable finance, public health sciences, epidemiology, medical-tech.

Organisations pertinentes : FQRNT, NSERC, Ministère de l’économie et l’innovation du Québec, CRM, Epidemiology group at CRM, Statistics Lab at CRM, CAMBAM Lab of the CRM, PhysNum Lab of the CRM, Fin-ML, MfPH group at Fields (joint with CRM and PIMS), Cimmul – Centre Interdisciplinaire en Modélisation Mathématique de l’Université Laval, Fields Institute of Mathematical Sciences Pacific Institute for Mathematical Sciences (PIMS), Santé Canada, Environnement Canada, Mila, CIRANO,
Banque du Canada.

Personnes pertinentes suggérées durant la consultation :

Les noms suivants ont été proposés par la communauté et les personnes mentionnées ci-dessous ont accepté d’afficher publiquement leur nom. Notez cependant que tous les noms des professeur.e.s (qu’ils soient affichés publiquement ou non sur notre site web) seront transmis au comité conseil pour l’étape d’identification et de sélection des thèmes stratégiques. Notez également que les personnes identifiées durant l’étape de consultation n’ont pas la garantie de recevoir une partie du financement. Cette étape sert avant tout à présenter un panorama du domaine, incluant les personnes pertinentes et non à monter des équipes pour les programmes-cadres.

• Bouchra Nasri
• Arthur Charpentier
• Erica Moodie
• Guillaume Lajoie
• Octav Cornea
• Bruno Rémillard
• Guy Wolf
• Manuel Morales
• Jacques Bélair

• Epidemiology and Environment Modelling
• Integrated Economic/Epidemiology/Environment models
• Data-driven Public Health Decision-making Research
• Responsible Finance
• Sustainable macroeconometrics
• Santé numérique
• Early-warning systems

(pas de documentation complémentaire pour le moment)