|CANMOD Network1 #701005|
CANadian Network for MODelling infectious Disease / Réseau CANadien de MODélisation des maladies infectieuses
Strengthening the community of people who are making and using infectious disease models to improve public health both inside and outside of Canada.
- CANMOD aims to increase Canada’s capacity for data-driven emerging infectious disease modelling (EIDM) to directly support short, medium, and long-term public health decisions.
- Our network comprises collaborative teams of modellers, statisticians, epidemiologists, public health decision-makers, and those implementing and delivering interventions.
- The questions we tackle will be grounded in public health needs and generated in partnership between research investigators and knowledge users—public health leaders, health administrators and policy-makers.
- This collaborative research will drive data collection, curation and access, such that critical information is available when needed.
- While the network will address questions of immediate relevance in the shorter term, we will take this approach with longer-term challenges in mind, building a trajectory of enhancing modelling expertise and capacity in infectious disease in Canada.]
- We are on this path already, as researchers move from the daily challenges of decision-making during the COVID-19 pandemic to working on longer-term policy questions. CANMOD will build and coordinate national capacity in infectious disease modelling at the forefront of public health.
- This capacity will position public health across Canada for better control of any infectious disease, and will build better preparedness and resilience in case of future pandemics. It will offer extensive experiential training opportunities to postdoctoral fellows (PDFs), graduate and undergraduate students at the intersection of infectious disease modelling, public health policy and decision making.
- CANMOD is committed to increasing equity, diversity, and inclusion in the next generation of infectious disease modellers. Trainees will be well-placed for quantitatively-oriented careers in academia, industry and the public sector.
- CitationsAdd new citationList by: CiterankMap
|Link NSERC Funding Decisions: Emerging Infectious Diseases Modelling Initiative|
Cited by: David Price 9:56 AM 16 September 2022 GMT
Citerank: (9) 679703EIDM?The Emerging Infectious Diseases Modelling Initiative (EIDM) – by the Public Health Agency of Canada and NSERC – aims to establish multi-disciplinary network(s) of specialists across the country in modelling infectious diseases to be applied to public needs associated with emerging infectious diseases and pandemics such as COVID-19. 7F1CEB7, 679712CANMOD – PeopleCANMOD is a national network, with members located across the country and associated with a broader Emerging Infectious Disease Modelling (EIDM) initiative. We are a community of modellers, statisticians, epidemiologists, public health decision-makers, and those implementing and delivering interventions.10019D3ABAB, 679714OMNI – PeopleOur English and French acronyms of the network, One Health Modelling Network for Emerging Infections (OMNI)/RÉseau UNe seule santé sur la modélisation des InfectionS (RÉUNIS) symbolize universality and the bringing together of people and ideas. This is precisely what we have done, having amassed an amazing network of interdisciplinary people with a commitment to a One Health approach to stopping emerging infectious diseases (EIDs) at all levels.10019D3ABAB, 679715OSN – PeopleThe One Society Network (OSN), led by Dr. Christopher McCabe at the University of Alberta, will include developing modelling for evaluating alternative policy responses during pandemics for all sectors of the economy and aspects of society, including marginalised groups. They will also be collaborating on multi-disciplinary training programs for skills development to support public policy making in future pandemics. 10019D3ABAB, 679716SMMEID – PeopleStatistical Methods for Managing Emerging Infectious Diseases (SMMEID), led by Dr. Patrick Brown at the University of Toronto, will develop methods and tools to get an accurate picture of the nature and extent of infectious disease transmission in the population, relying on real-world data from administrative sources and surveys. They are seeking to augment Canada's capacity to respond to emerging infectious diseases. 10019D3ABAB, 701002OMNIThe One Health Modelling Network for Emerging Infections (OMNI), led by Dr. Huaiping Zhu at York University, will identify gaps that can be used to prioritize more targeted surveillance or data collection and then use those data to refine models. This work will contribute to an improved understanding of the conditions that enable pathogen spread and transmission and identify actions that can most effectively manage these conditions. 1002079B9B9, 701006MfPHMathematics for Public Health (MfPH), led by Dr. V. Kumar Murty, Director of the Fields Institute and Professor at the University of Toronto, will aim to bridge the gap between mathematical research and real public health issues. The team will seek to produce models that are effective, practical and reliable for applications to public health issues for COVID-19 as well as boost Canada’s future pandemic preparedness. 1002079B9B9, 701007OSNThe One Society Network (OSN), led by Dr. Christopher McCabe at the University of Alberta, will include developing modelling for evaluating alternative policy responses during pandemics for all sectors of the economy and aspects of society, including marginalised groups. They will also be collaborating on multi-disciplinary training programs for skills development to support public policy making in future pandemics. 1002079B9B9, 701008SMMEIDThis project assembles the top biostatisticians in Canada working on infectious diseases, and joins them with epidemiologists developing novel methods for data collection during the COVID-19 pandemic. Our group is developing methods and tools to get an accurate picture of the nature and extent of infectious disease transmission in the population, relying on real-world data from administrative sources and surveys. 1002079B9B9URL: