Using data to improve public health: COVID-19 secondment

新冠肺炎疫情让人们看到了利用数据了解冠状病毒传播、预测疫情在空间和时间上的未来趋势、预测医疗服务面临的挑战以及为政府管理政策提供信息的重要性。国家电子健康档案和纵向队列研究的可获得性为利用大数据更好地了解新冠肺炎对健康、社会和经济的影响提供了独特的机会。该奖学金的目的是开发设计良好的统计分析，充分利用来自多个来源的数据，以了解新冠肺炎大流行的影响和管理，以改善公众健康。该奖学金的目标是：1.确定与长期COVID相关的风险因素。评价疫苗的有效性和安全性。量化在不同的混乱浪潮中造成的医疗服务中断。评估新冠肺炎感染对其他健康结局的影响。这项奖学金的一个关键重点将是让我的研究生涯从一名独立研究员发展成为大数据支持的健康研究领域的领先专家。拟议中的研究将使我能够：a)通过参与纵向健康与福祉国家核心研究来开展新冠肺炎研究。奖学金将使我能够应用并提升我在大数据、统计建模和流行病学方面的专业知识，以开发统计分析，以解决将对公共健康产生影响的重要研究问题。b)发展一支新冠肺炎研究和健康数据科学的研究团队。这将通过聚集研究人员支持这项奖学金来实现。并通过申请资金来支持团队的活动，并在此奖学金之外培训职业早期研究人员。c)提高我在使用大数据进行健康研究方面的专业知识。我对使用真实世界的大数据解决重要研究问题，特别是在临床决策框架内，有着内在的热情，并不断发展我的经验。这一奖学金将使我能够扩展我在这个令人兴奋的领域的专业知识，这个领域具有巨大的潜力，可以影响医疗保健政策，从而改善人口健康。d)广泛传播研究成果。我将在同行评议的期刊出版物和会议演讲中传播这项研究的成果。此外，我将创建一个网站，向包括普通公众在内的更广泛的受众传达我的工作的主要发现。我还将主持与新冠肺炎相关的研讨会或利益相关者研讨会，以激发人们对研究的进一步兴趣、合作和影响。实现这些奖学金目标将使我能够实现我的职业目标，成为大数据健康方法方面的领先专家，领导一个研究团队，开发量化方法来解决与新冠肺炎公共卫生相关的研究问题。大数据方法将使我们能够为卫生政策提供更好的见解、更有力的结论和更好的建议，这最终将使人们的健康受益。

The COVID-19 pandemic has seen the importance of using data to understand the spread of the Coronavirus, predict future trends of the pandemic in space and time, forecast challenges for healthcare delivery and inform government management policies. The availability of national electronic health records and longitudinal cohort studies provides unique opportunities for using big data to better understand the impact of COVID-19 on health, society and economics. The aim of this fellowship is to develop well-designed statistical analyses which make the best use of data from multiple sources in order to understand the impacts and management of the COVID-19 pandemic to improve public health.The objectives of this fellowship are to:1. Identify risk factors associated with Long COVID.2. Evaluate the effectiveness and safety of vaccines.3. Quantify the healthcare disruptions during different waves of the pandemic.4. Assess the effects of COVID-19 infection on other health outcomes. A key focus of this fellowship will be for me to advance my research career from an independent researcher to become a leading expert in big data enabled health research. The proposed research will allow me to:a) Conduct COVID-19 research through engaging in the Longitudinal Health and Wellbeing National Core Study.The fellowship will allow me to apply and advance my expertise in big data, statistical modelling and epidemiology to develop statistical analyses for addressing important research questions which will have an impact on public health.b) Grow a research team for COVID-19 research and health data science.This will be achieved through bringing together researchers to support this fellowship, and by making funding applications to support the team's activities and to train early-career researchers beyond this fellowship.c) Advance my expertise in using big data for health research.I have an intrinsic passion for, and continually develop my experience in, using real-world big data to address important research questions, especially within the clinical decision-making framework. This fellowship will allow me to extend my expertise in this exciting area that has a great potential to influence healthcare policy leading to better population health.d) Disseminate research outputs widely.I will disseminate the outputs from this research in peer-reviewed journal publications and conference presentations. Furthermore, I will create a website to communicate the key findings of my work to a wider audience including the general public. I will also host seminars or a stakeholder workshop related to COVID-19, to generate further interests, collaborations and impact of the research.Achieving these fellowship objectives will enable me to reach my career goal to be a leading expert in big data approaches to health, heading up a research team that develops quantitative methods to address research questions related to COVID-19 public health and beyond. Big data approaches will allow us to provide better insights, more robust conclusions, and better recommendations for health policy, which will ultimately benefit the population health.