A computational modelling framework for identifying smart vaccination strategies

点击翻译按钮获取中文摘要

Medicago, a clinical-stage biopharmaceutical company, has developed a very innovative production method, which has the potential to enable the development of a vaccine for testing within 3 weeks of the identification of genetic sequences from an influenza strain, allowing for large-scale vaccination of the population very early during an outbreak. The company would like to know how many doses of vaccine is required and what distribution strategies should be used to maximize population-wide benefits of vaccination in the event of an influenza pandemic. We will apply an agent-based modelling approach to incorporate information on population profiles; health indicators; disease characteristics; and novel vaccine technology production and effectiveness parameters in order to determine the required number of vaccine doses and distribution rates under constraints of vaccine manufacturing capacity and delivery. We will communicate the outcomes of this project with Medicago, which will enable them to project and improve Medicago's vaccine production capacity, so that effective communications can take place between Medicago and its customers worldwide. In addition to mitigating the negative effects of a pandemic on the Canadian economy, Medicago will use this technology to improve its market share and to become more competitive nationally and internationally. It will thereby contribute to the Canadian economy, and as this technology improves, it will provide opportunities for job creation and growth within Canada. This project provides an excellent opportunity for HQP trainees to leverage cutting edge techniques and methodologies for performing world-class research, but also to gain off-campus industrial experience involving applied-driven problems.