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Grant: Alliance Grants

York University

$140,811.00

Advanced Mathematical Technologies for Respiratory Infection Risk Assessment and Pharmaceutical Intervention Scenario Analysis

EF 2024-25

Résumé
The program will establish and sustain a dedicated cadre of academic researchers and train highly qualified individuals, in collaboration with Sanofi scientists, to provide evidence-based and data-driven strategic direction and enhanced research capacity around respiratory infectious diseases relevant to vaccine product pipeline and the optimal use of vaccinations in Canada and globally. The program will support mathematical modelling, neural network-based data analytics and health economics for rapid response to outbreaks, and for proactive, real-time, and both prospective and retrospective evaluation of control and prevention programs using multi-source data. The initial focus will be on Sanofi's vaccines at different stages of development and registration against prototypical respiratory infections such as influenza, Covid-19 and Respiratory Syncytial Virus, but the methodologies and technologies, as well as the datasets collected and analyzed will be as generic as possible. The established capacity will serve as a vaccine R&D platform and can be activated for rapid mobilization in response to emerging public health threats and industrial production needs. We develop models and modelling technologies for scenario planning as a central component of risk management efforts to meet the critical need in compressing timelines for vaccines to be discovered, produced and made accessible at scale. Our research supports scenario planning over three distinct periods. During a short-term period, our Nowcasting and Nearcasting Techniques support decisions on interventions and the required manufacturing capacity for development and deployment, and provide decision support on clinical trials for new vaccines. During a mid- to long-term period, our Transmission Dynamics Models and Scenario Optimization Technologies support decision making on where/when to test new vaccine candidates and what scale of the production capacity is needed. During a long-term period, when the disease may be endemic and can cause recurrent outbreaks, our Bifurcation and Optimization Techniques support planing for further vaccine manufacturing capacity needs, and inform optimal vaccine boosting scenarios and intervals.
Programme
Alliance Grants
Bénéficiaire
Emplacement
Ontario, Canada
Type de bénéficiaire
Academia
Type
Grant
Source
NSERC's Awards Data

Tel que publié

Application id
576914-2022
Name nom
Wu, Jianhong J
Department département
Mathematics and Statistics
Organization id
38
Institution établissement
York University
Province en
Ontario
Province fr
Ontario
Country en
CANADA
Country fr
CANADA
Fiscal year exercice financier
2024
Competition year année de concours
2022
Award amount
140811
Program id
ALLRP
Program name en
Alliance Grants
Program name fr
Subventions Alliance
Group en
RESEARCH PARTNERSHIPS
Group fr
PARTENARIATS DE RECHERCHE
Committee code
96
Committee name en
RPP Internal Decision Cttee
Committee name fr
Cté de décision interne - PPR
Area of application code
1205
Area of application group en
Advancement of knowledge
Area of application group fr
Avancement des connaissances
Area of application en
Mathematical sciences
Area of application fr
Sciences mathématiques
Research subject code
2960
Research subject group en
Applied mathematics
Research subject group fr
Mathématiques appliquées
Research subject en
Mathematical modelling
Research subject fr
Modélisation mathématique
Field of research list names en
Not available
Field of research list names fr
Non disponible
Socioeconomic objective list names en
Not available
Socioeconomic objective list names fr
Non disponible
Application title
Advanced Mathematical Technologies for Respiratory Infection Risk Assessment and Pharmaceutical Intervention Scenario Analysis
Keywords
contact mixing; decision support; immunization; intelligent risk; mass gathering; mathematical modelling; neural networks; pharmaceutics and public health; respiratory infection; vaccine science
Application summary
The program will establish and sustain a dedicated cadre of academic researchers and train highly qualified individuals, in collaboration with Sanofi scientists, to provide evidence-based and data-driven strategic direction and enhanced research capacity around respiratory infectious diseases relevant to vaccine product pipeline and the optimal use of vaccinations in Canada and globally. The program will support mathematical modelling, neural network-based data analytics and health economics for rapid response to outbreaks, and for proactive, real-time, and both prospective and retrospective evaluation of control and prevention programs using multi-source data. The initial focus will be on Sanofi's vaccines at different stages of development and registration against prototypical respiratory infections such as influenza, Covid-19 and Respiratory Syncytial Virus, but the methodologies and technologies, as well as the datasets collected and analyzed will be as generic as possible. The established capacity will serve as a vaccine R&D platform and can be activated for rapid mobilization in response to emerging public health threats and industrial production needs. We develop models and modelling technologies for scenario planning as a central component of risk management efforts to meet the critical need in compressing timelines for vaccines to be discovered, produced and made accessible at scale. Our research supports scenario planning over three distinct periods. During a short-term period, our Nowcasting and Nearcasting Techniques support decisions on interventions and the required manufacturing capacity for development and deployment, and provide decision support on clinical trials for new vaccines. During a mid- to long-term period, our Transmission Dynamics Models and Scenario Optimization Technologies support decision making on where/when to test new vaccine candidates and what scale of the production capacity is needed. During a long-term period, when the disease may be endemic and can cause recurrent outbreaks, our Bifurcation and Optimization Techniques support planing for further vaccine manufacturing capacity needs, and inform optimal vaccine boosting scenarios and intervals.