Spatio-Temporal Modelling of COVID Prevalence Scenarios.

COVID 流行情况的时空建模。

• 批准号: 2440302
• 金额: --
• 依托单位: Lancaster University
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2020
• 资助国家: 英国
• 起止时间: 2020 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2440302
• 关键词: Spatio; Temporal; Modelling; COVID; Prevalence

Since early 2020, the UK has been battling the SARS-CoV-2 (Covid19) pandemic. It has impacted the lives of every one of us, and no region of the UK has been able to avoid the virus. Ultimately, it is not an exaggeration to say the Covid 19 pandemic has rewritten much of the rulebook on successful epidemic modelling.As the Covid 19 pandemic progresses, it is inevitable that we will experience waves and troughs of infections as the prevalence of the virus changes. In the future, the UK will likely reach an endemic state. This is after infection rates decline because of greater proportions of the population gaining resistance from both vaccine programs and surviving infections. Eventually the UK will experience low but sustained infection rates. Currently we are not equipped with the tools to provide meaningful UK wide surveillance in the aforementioned low prevalence, "Endgame", scenario.The primary goal of the PhD project is to fill this gap in Spatio-Temporal statistical epidemiologic modelling, and to develop a meaningful method providing Covid 19 prevalence modelling across the UK as we progress into the Covid 19 Endgame. As the state of the SARS-CoV-2 pandemic is fast moving at the best of times, it is realistic to expect the project to adapt to future concerns identified by the Office of National Statistics, this project's sponsor, as the need arises. The PhD project can be extended to address some open challenges the UK faces, such as Optimal Allocation of Randomised Survey Resources to Minimise Prevalence Uncertainty and extending current methods for Early Detection of Areas at Risk of High Prevalence Increase (hotspot detection). Altogether, the project hopes to help fill in the missing pages in successful epidemic modelling as the country at large looks to a future beyond Covid 19.In partnership with the Office for National Statistics.

自2020年初以来，英国一直在与新冠肺炎（covid - 19）大流行作斗争。它影响了我们每个人的生活，英国没有一个地区能够避免这种病毒。最终，可以毫不夸张地说，Covid - 19大流行已经改写了成功的流行病建模的大部分规则手册。随着Covid - 19大流行的发展，随着病毒流行率的变化，我们将不可避免地经历感染的波折。在未来，英国很可能会达到一种地方性的状态。这是在感染率下降之后，因为更大比例的人口从疫苗项目和幸存感染中获得耐药性。最终，英国将经历低但持续的感染率。目前，我们没有工具来提供有意义的英国范围内的监测，在前面提到的低患病率，“终局”的情况下。博士项目的主要目标是填补时空统计流行病学建模方面的空白，并开发一种有意义的方法，在我们进入Covid - 19终局阶段时，在英国各地提供Covid - 19患病率建模。由于SARS-CoV-2大流行的状况在最好的时候也在迅速发展，因此期望该项目能够根据需要适应国家统计局（该项目的发起人）确定的未来关切是现实的。博士项目可以扩展到解决英国面临的一些公开挑战，例如随机调查资源的最佳分配，以最大限度地减少患病率的不确定性，并扩展当前的方法，以早期发现高患病率增加风险的地区（热点检测）。总而言之，随着整个国家展望2019冠状病毒病之后的未来，该项目希望帮助填补成功的流行病建模中缺失的部分。与国家统计局合作。