Improving public health policy through infection transmission modelling: Guidelines for creating a Community of Practice.

BACKGROUND: Despite significant research efforts in Canada, real application of modelling in public health decision making and practice has not yet met its full potential. There is still room to better address the diversity of the Canadian population and ensure that research outcomes are translated for use within their relevant contexts. OBJECTIVES: To strengthen connections to public health practice and to broaden its scope, the Pandemic Influenza Outbreak Research Modelling team partnered with the National Collaborating Centre for Infectious Diseases to hold a national workshop. Its objectives were to: understand areas where modelling terms, methods and results are unclear; share information on how modelling can best be used in informing policy and improving practice, particularly regarding the ways to integrate a focus on health equity considerations; and sustain and advance collaborative work in the development and application of modelling in public health. METHOD: The Use of Mathematical Modelling in Public Health Decision Making for Infectious Diseases workshop brought together research modellers, public health professionals, policymakers and other experts from across the country. Invited presentations set the context for topical discussions in three sessions. A final session generated reflections and recommendations for new opportunities and tasks. CONCLUSIONS: Gaps in content and research include the lack of standard frameworks and a glossary for infectious disease modelling. Consistency in terminology, clear articulation of model parameters and assumptions, and sustained collaboration will help to bridge the divide between research and practice.

HISTORIQUE: Malgré l'ampleur des recherches au Canada, la mise en œuvre de la modélisation n'a pas encore atteint son plein potentiel en santé publique dans la prise de décision et la pratique. Il y a matière à mieux intégrer la diversité de la population canadienne et d'utiliser les résultats de la recherche dans les contextes pertinents. OBJECTIFS: Pour renforcer les liens avec l'exercice de la santé publique et en élargir la portée, l'équipe de Pandemic Influenza Outbreak Research Modelling s'est associée au Centre de collaboration nationale des maladies infectieuses pour organiser un atelier national. Cet atelier visait à déterminer les secteurs où la terminologie, les méthodo-logies et les résultats de la modélisation manquent de clarté, à transmettre de l'information sur l'utilisation optimale de la modélisation pour étayer les politiques et améliorer la pratique, notamment en accordant plus d'importance aux questions d'équité en santé, et à maintenir et faire progresser la collaboration pour élaborer et mettre en œuvre la modélisation en santé publique. MÉTHODOLOGIE: L'atelier sur l'utilisation de la modélisation mathématique dans la prise de décision relative aux maladies infectieuses en santé publique a réuni des chercheurs modélisateurs, des professionnels de la santé publique, des décideurs et d'autres experts du pays. Les conférenciers ont mis en contexte les discussions dans le cadre de trois séances. Une dernière séance a suscité des réflexions et des recommandations sur les futures tâches et possibilités. CONCLUSIONS: Les lacunes en matière de contenu et de recherche incluent l'absence de cadres standardisés et de glossaire de la modélisation des maladies infectieuses. Une terminologie uniforme, la formulation claire des paramètres et des hypothèses de modélisation ainsi qu'une collaboration soutenue contribueront à corriger l'écart entre la recherche et la pratique.

Effect of flight connectivity on the introduction and evolution of the COVID-19 outbreak in Canadian provinces and territories.

BACKGROUND: The COVID-19 pandemic has challenged health services and governments in Canada and around the world. Our research aims to evaluate the effect of domestic and international air travel patterns on the COVID-19 pandemic in Canadian provinces and territories. METHODS: Air travel data were obtained through licensed access to the 'BlueDot Intelligence Platform', BlueDot Inc. Daily provincial and territorial COVID-19 cases for Canada and global figures, including mortality, cases recovered and population data were downloaded from public datasets. The effects of domestic and international air travel and passenger volume on the number of local and non-local infected people in each Canadian province and territory were evaluated with a semi-Markov model. Provinces and territories are grouped into large (>100 000 confirmed COVID-19 cases and >1 000 000 inhabitants) and small jurisdictions (≤100 000 confirmed COVID-19 cases and ≤1 000 000 inhabitants). RESULTS: Our results show a clear decline in passenger volumes from March 2020 due to public health policies, interventions and other measures taken to limit or control the spread of COVID-19. As the measures were eased, some provinces and territories saw small increases in passenger volumes, although travel remained below pre-pandemic levels. During the early phase of disease introduction, the burden of illness is determined by the connectivity of jurisdictions. In provinces with a larger population and greater connectivity, the burden of illness is driven by case importation, although local transmission rapidly replaces imported cases as the most important driver of increasing new infections. In smaller jurisdictions, a steep increase in cases is seen after importation, leading to outbreaks within the community. CONCLUSIONS: Historical travel volumes, combined with data on an emerging infection, are useful to understand the behaviour of an infectious agent in regions of Canada with different connectivity and population size. Historical travel information is important for public health planning and pandemic resource allocation.

Toward Standardizing a Lexicon of Infectious Disease Modeling Terms.

Disease modeling is increasingly being used to evaluate the effect of health intervention strategies, particularly for infectious diseases. However, the utility and application of such models are hampered by the inconsistent use of infectious disease modeling terms between and within disciplines. We sought to standardize the lexicon of infectious disease modeling terms and develop a glossary of terms commonly used in describing models' assumptions, parameters, variables, and outcomes. We combined a comprehensive literature review of relevant terms with an online forum discussion in a virtual community of practice, mod4PH (Modeling for Public Health). Using a convergent discussion process and consensus amongst the members of mod4PH, a glossary of terms was developed as an online resource. We anticipate that the glossary will improve inter- and intradisciplinary communication and will result in a greater uptake and understanding of disease modeling outcomes in heath policy decision-making. We highlight the role of the mod4PH community of practice and the methodologies used in this endeavor to link theory, policy, and practice in the public health domain.