Understanding the dynamics and drivers of the COVID-2019 epidemic using real-time outbreak analytics

使用实时疫情分析了解 COVID-2019 疫情的动态和驱动因素

• 批准号: MC_PC_19065
• 负责人: John Edmunds
• 金额: $ 130.07万
• 依托单位: London School of Hygiene & Tropical Medicine
• 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_19065
• 关键词: Understanding; dynamics; drivers; COVID; 2019

Tracking and predicting an epidemic can help tailor public health measures so that werespond quickly and appropriately to evolving threats. We will fit statistical models toemerging data to monitor key epidemiological and clinical parameters related totransmissibility and severity, and their likely impact on health services. Mathematicalmodels will be fitted to surveillance data to forecast future cases and hospitalisations andevaluate the impact of different public health policies. Importantly, we will also measurepeople’s behaviour over time to detect changes in response to the epidemic, assessingwhat measures they take to reduce risk and where they get their evidence from. Thisinformation will be used to refine our models and improve epidemiological forecasting. Itwill also allow public health authorities to tailor their messaging to ensure that peoplerespond appropriately to changes in risk. The information that we generate, either fromour analyses of the emerging data, epidemic forecasts, or from our panel of respondents,will be rapidly summarised and shared on easy-to-use websites so that scientists, healthworkers and the public can be best informed about the threats posed by COVID-19 andthe efficacy of different measures to reduce risk.

跟踪和预测流行病可以帮助调整公共卫生措施，以便我们对不断变化的威胁做出快速和适当的反应。我们将根据新出现的数据拟合统计模型，以监测与传染性和严重程度相关的关键流行病学和临床参数，以及它们对卫生服务的可能影响。数学模型将与监测数据相匹配，以预测未来的病例和住院情况，并评估不同公共卫生政策的影响。重要的是，我们还将测量人们在一段时间内的行为，以发现对流行病的反应变化，评估他们采取了什么措施来降低风险，以及他们从哪里得到证据。这些信息将被用来完善我们的模型和改进流行病学预测。信息技术还使公共卫生当局能够调整其信息，以确保人们对风险的变化做出适当的反应。我们通过分析新出现的数据、流行病预测或调查对象获得的信息，将在易于使用的网站上迅速汇总和分享，以便科学家、卫生工作者和公众能够最好地了解COVID-19带来的威胁以及降低风险的不同措施的有效性。

项目成果

Explosive nosocomial outbreak of SARS-CoV-2 in a rehabilitation clinic: the limits of genomics for outbreak reconstruction.

SARS-COV-2在康复诊所的爆炸性医学院爆发：爆发重建的基因组学的局限性。

• DOI: 10.1016/j.jhin.2021.07.013
• 发表时间: 2021-11
• 期刊: The Journal of hospital infection
• 作者: Abbas M;Robalo Nunes T;Cori A;Cordey S;Laubscher F;Baggio S;Jombart T;Iten A;Vieux L;Teixeira D;Perez M;Pittet D;Frangos E;Graf CE;Zingg W;Harbarth S
• 通讯作者: Harbarth S

Overall and cause-specific hospitalisation and death after COVID-19 hospitalisation in England: cohort study in OpenSAFELY using linked primary care, secondary care and death registration data

• DOI: 10.1101/2021.07.16.21260628
• 发表时间: 2021-07
• 作者: K. Bhaskaran;C. Rentsch;G. Hickman;W. Hulme;Anna Schultze;H. Curtis;K. Wing;C. Warren-Gash;L. Tomlinson;C. Bates;R. Mathur;B. Mackenna;V. Mahalingasivam;A. Wong;A. Walker;C. Morton;D. Grint;A. Mehrkar;R. Eggo;P. Inglesby;I. Douglas;H. Mcdonald;J. Cockburn;E. Williamson;D. Evans;J. Parry;F. Hester;S. Harper;S. Evans;S. Bacon;L. Smeeth;B. Goldacre

Factors associated with deaths due to COVID-19 versus other causes: population-based cohort analysis of UK primary care data and linked national death registrations within the OpenSAFELY platform.

• DOI: 10.1016/j.lanepe.2021.100109
• 发表时间: 2021-07
• 期刊: The Lancet regional health. Europe
• 作者: Bhaskaran K;Bacon S;Evans SJ;Bates CJ;Rentsch CT;MacKenna B;Tomlinson L;Walker AJ;Schultze A;Morton CE;Grint D;Mehrkar A;Eggo RM;Inglesby P;Douglas IJ;McDonald HI;Cockburn J;Williamson EJ;Evans D;Curtis HJ;Hulme WJ;Parry J;Hester F;Harper S;Spiegelhalter D;Smeeth L;Goldacre B
• 通讯作者: Goldacre B

Benefit-risk analysis of health benefits of routine childhood immunisation against the excess risk of SARS-CoV-2 infections during the COVID-19 pandemic in Africa

非洲 COVID-19 大流行期间常规儿童免疫接种对预防 SARS-CoV-2 感染过度风险的健康益处的效益-风险分析

• DOI: 10.1101/2020.05.19.20106278
• 发表时间: 2020
• 作者: Abbas K