Statistical methods for characterising the severity of an emerging pathogen: case studies of the COVID-19 pandemic
描述新兴病原体严重性的统计方法:COVID-19 大流行的案例研究
基本信息
- 批准号:2899154
- 负责人:
- 金额:--
- 依托单位:
- 依托单位国家:英国
- 项目类别:Studentship
- 财政年份:2019
- 资助国家:英国
- 起止时间:2019 至 无数据
- 项目状态:未结题
- 来源:
- 关键词:
项目摘要
Modelling of an ongoing epidemic of a new pathogen poses several particular challenges, especially when the cases are often symptomless or easy to mistake for a common disease such as a cold or flu. Mathematical models of infectious diseases rely on the input of data, and at the beginning of the COVID-19 pandemic very limited datasets were available to the researchers. This caused the estimates to have large biases, including large uncertainty intervals or the need to assume the severity of the virus across different countries was the same. This assumption was necessary at the time but made the results highly questionable e.g. when data from a high-income country were used to model the epidemic in a low- or middle-income country.An additional problem posed by the data was the delay with which they were collected. Surveillance of an ongoing epidemic requires daily monitoring of the numbers of new cases or deaths, but this was often impossible due to the reporting delays, as a lot of hospital staff was overwhelmed with the number of patients and unable to promptly update the data online. This led to systematic underreporting of the true state of the epidemic.The main topic of my PhD projects is closing some of those data gaps and decreasing the bias in the data. In the first project, we provided estimates of common epidemiological distributions, such as onset-to-death time, for the COVID-19 epidemic in Brazil based on hospitalisation data. Our estimates gave evidence of spatial heterogeneity in the fitted distributions. In the second project, we proposed a new approach to correcting the delays in data reporting using latent Gaussian processes (GPs) and presented the applicability of the method to the COVID-19 mortality data in Brazil.
对一种新病原体的持续流行进行建模带来了几个特殊的挑战,特别是当这些病例通常是不确定的或容易被误认为是感冒或流感等常见疾病时。传染病的数学模型依赖于数据的输入,在COVID-19大流行开始时,研究人员可获得的数据非常有限。这导致估计存在较大偏差,包括较大的不确定性区间或需要假设不同国家的病毒严重程度相同。这一假设在当时是必要的,但结果却很成问题,例如,当来自一个高收入国家的数据被用来模拟一个低收入或中等收入国家的流行病时,数据造成的另一个问题是数据收集的延迟。对正在进行的流行病的监测需要每天监测新病例或死亡人数,但由于报告延迟,这往往是不可能的,因为许多医院工作人员被患者人数压垮,无法及时更新在线数据。这导致了对疫情真实状况的系统性低估。我博士项目的主要课题是填补一些数据缺口,减少数据中的偏差。在第一个项目中,我们根据住院数据提供了巴西COVID-19流行病常见流行病学分布的估计,例如发病至死亡时间。我们的估计提供了空间异质性的证据,在拟合分布。在第二个项目中,我们提出了一种新的方法来纠正使用潜在高斯过程(GP)的数据报告延迟,并介绍了该方法对巴西COVID-19死亡率数据的适用性。
项目成果
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其他文献
吉治仁志 他: "トランスジェニックマウスによるTIMP-1の線維化促進機序"最新医学. 55. 1781-1787 (2000)
Hitoshi Yoshiji 等:“转基因小鼠中 TIMP-1 的促纤维化机制”现代医学 55. 1781-1787 (2000)。
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LiDAR Implementations for Autonomous Vehicle Applications
- DOI:
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2021 - 期刊:
- 影响因子:0
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吉治仁志 他: "イラスト医学&サイエンスシリーズ血管の分子医学"羊土社(渋谷正史編). 125 (2000)
Hitoshi Yoshiji 等人:“血管医学与科学系列分子医学图解”Yodosha(涉谷正志编辑)125(2000)。
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Effect of manidipine hydrochloride,a calcium antagonist,on isoproterenol-induced left ventricular hypertrophy: "Yoshiyama,M.,Takeuchi,K.,Kim,S.,Hanatani,A.,Omura,T.,Toda,I.,Akioka,K.,Teragaki,M.,Iwao,H.and Yoshikawa,J." Jpn Circ J. 62(1). 47-52 (1998)
钙拮抗剂盐酸马尼地平对异丙肾上腺素引起的左心室肥厚的影响:“Yoshiyama,M.,Takeuchi,K.,Kim,S.,Hanatani,A.,Omura,T.,Toda,I.,Akioka,
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