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Spatio-Temporal Modelling of COVID Prevalence Scenarios.

Spatio-Temporal Modelling of COVID Prevalence Scenarios.
COVID 流行情况的时空建模。
批准号:
2440302
负责人:
金额:
$0.0万
依托单位:
依托单位国家:
英国
项目类别:
Studentship
财政年份:
2020
资助国家:
英国
项目状态:
未结题
起止时间:
2020 至 --

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中文摘要
翻译
自2020年初以来,英国一直在与SARS-CoV-2(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,新冠肺炎疫情改写了成功建立流行病模型的大部分规则。随着新冠肺炎疫情的发展,随着病毒流行率的变化,我们将不可避免地经历感染的波动和低谷。在未来,英国可能会达到地方病状态。这是在感染率下降之后,因为更大比例的人口从疫苗计划和幸存感染中获得抵抗力。最终,英国将经历低但持续的感染率。目前,我们还没有配备在上述低流行率“残局”情景中提供有意义的英国范围内监测的工具。博士项目的主要目标是填补时空统计流行病学建模的这一空白,并开发一种有意义的方法,在我们进入Covid 19 Endgame时提供英国各地的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,期望该项目在需要时适应该项目的赞助者国家统计局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开放性挑战,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,并扩展目前的方法,用于早期检测高危地区(热点检测)。总而言之,该项目希望帮助填补成功的流行病建模中缺失的页面,因为整个国家都在展望COVID 19之后的未来。
英文摘要
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.
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