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Estimating severity from multiple data sources using Bayesian evidence synthesis

使用贝叶斯证据综合估计多个数据源的严重性

基本信息

批准号：
MC_PC_19074
负责人：
Anne Presanis
金额：
$ 23.01万
依托单位：
University of Cambridge
依托单位国家：
英国
项目类别：
Intramural
财政年份：
2020
资助国家：
英国
起止时间：
2020 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=MC_PC_19074
关键词：
Estimating severity multiple data sources

项目摘要

As we prepare for a possible COVID-19 pandemic, understanding the severity of theepidemic, i.e. the proportion of infections that result in a severe event such ashospitalisation or death, is crucial to monitoring and predicting the burden of the epidemicon healthcare services. This burden is measured by the number of people infected whorequire primary care from general practitioners, hospital admission, respiratory supportand/or admission to intensive care. Severity is quantified by infection-severity and caseseverityrisks, namely the probability that an infection (whether with or without symptoms)or a symptomatic infection (clinical case) leads to a severe event. These quantities arechallenging to observe directly from a single dataset, as it is not possible to detect andfollow-up every case in a population, particularly early in the epidemic when case countsmay miss many asymptomatic or mild cases. The problem is compounded by the fact thatfor patients still ill in hospital, we have not yet had time to observe whether they willrecover or not. We propose combining information from multiple datasets, both onindividuals and aggregated counts, to estimate severity while accounting for thechallenges of missing cases and not yet observing outcomes.

在我们为可能发生的COVID-19大流行做准备时，了解疫情的严重程度，即导致住院或死亡等严重事件的感染比例，对于监测和预测流行病医疗保健服务的负担至关重要。这种负担是通过需要全科医生提供初级保健、住院、呼吸支持和/或重症监护的感染者人数来衡量的。严重程度是通过感染严重程度和病例严重风险来量化的，即感染（无论有无症状）或症状性感染（临床病例）导致严重事件的概率。从单一数据集直接观察这些数量具有挑战性，因为不可能在人群中发现和随访每一例病例，特别是在疫情早期，病例计数可能会遗漏许多无症状或轻度病例。使问题更加复杂的是，对于仍在住院的病人，我们还没有时间观察他们是否会康复。我们建议结合来自多个数据集的信息，包括个人和汇总计数，以估计严重程度，同时考虑到缺失病例的挑战和尚未观察结果。

项目成果

期刊论文数量（10）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

A comparison of two frameworks for multi-state modelling, applied to outcomes after hospital admissions with COVID-19.

DOI：
10.1177/09622802221106720
发表时间：
2022-09
期刊：
STATISTICAL METHODS IN MEDICAL RESEARCH
影响因子：
2.3
作者：
Jackson, Christopher H.;Tom, Brian D. M.;Kirwan, Peter D.;Mandal, Sema;Seaman, Shaun R.;Kunzmann, Kevin;Presanis, Anne M.;De Angelis, Daniela
通讯作者：
De Angelis, Daniela

Trends in COVID-19 hospital outcomes in England before and after vaccine introduction, a cohort study

一项队列研究：英格兰引入疫苗前后 COVID-19 医院结果的趋势

DOI：
10.48550/arxiv.2112.10661
发表时间：
2021
期刊：
影响因子：
0
作者：
Kirwan P
通讯作者：
Kirwan P

Decreasing hospital burden of COVID-19 during the first wave in Regione Lombardia: an emergency measures context.

在伦巴第局的第一波中减轻了Covid-19的医院负担：紧急措施。

DOI：
10.1186/s12889-021-11669-w
发表时间：
2021-09-03
期刊：
BMC public health
影响因子：
4.5
作者：
Grosso FM;Presanis AM;Kunzmann K;Jackson C;Corbella A;Grasselli G;Andreassi A;Bodina A;Gramegna M;Castaldi S;Cereda D;Angelis D;Covid-19 Lombardy Working Group
通讯作者：
Covid-19 Lombardy Working Group

Trends in COVID-19 hospital outcomes in England before and after vaccine introduction, a cohort study.

一项队列研究，英格兰引入疫苗前后 COVID-19 医院结果的趋势。