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Transparency assessment of COVID-19 models.新型冠状病毒肺炎模型的透明度评估。
Lancet Glob Health. 2020 Dec;8(12):e1459-e1460. doi: 10.1016/S2214-109X(20)30447-2. Epub 2020 Oct 27.
2
Methodological challenges of analysing COVID-19 data during the pandemic.疫情期间分析新冠病毒疾病（COVID-19）数据的方法学挑战。
BMC Med Res Methodol. 2020 Apr 14;20(1):81. doi: 10.1186/s12874-020-00972-6.
3
Science, policy, people, and public health: What Is COVID-19 teaching us?科学、政策、民众与公共卫生：新冠疫情给了我们哪些启示？
Indian J Public Health. 2020 Jun;64(Supplement):S87-S89. doi: 10.4103/ijph.IJPH_540_20.
4
Surgeons, Surgical Research, and the COVID-19 Pandemic.外科医生、外科研究与新冠疫情
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Call for transparency of COVID-19 models.呼吁提高新冠病毒模型的透明度。
Science. 2020 May 1;368(6490):482-483. doi: 10.1126/science.abb8637.
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Strengthening the global effort on COVID-19 research.加强全球在新冠病毒研究方面的努力。
Lancet. 2020 Aug 8;396(10248):375. doi: 10.1016/S0140-6736(20)31598-1. Epub 2020 Jul 16.
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An unexpected and suspended time.一段意外且暂停的时光。
J Headache Pain. 2020 Apr 22;21(1):36. doi: 10.1186/s10194-020-01112-7.
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How can the scientific community ensure the integrity of COVID-19 research?科学界如何确保新冠病毒研究的诚信性？
CMAJ. 2020 Jul 13;192(28):E817-E818. doi: 10.1503/cmaj.1095881.
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COVID-19 and the Futures of Machine Learning.新型冠状病毒肺炎与机器学习的未来
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How swamped preprint servers are blocking bad coronavirus research.预印本服务器如何不堪重负，正在阻止不良的新冠病毒研究。
Nature. 2020 May;581(7807):130-131. doi: 10.1038/d41586-020-01394-6.

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Ethics of Mathematical Modeling in Public Health: The Case of Medical Male Circumcision for HIV Prevention in Africa.公共卫生领域数学建模的伦理问题：以非洲男性包皮环切术预防艾滋病为例。
Public Health Ethics. 2024 Oct 14;17(3):125-138. doi: 10.1093/phe/phae009. eCollection 2024 Nov.
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Strengthening a Weak Link: Transparency of Causal Loop Diagrams, Current State and Recommendation.强化薄弱环节：因果循环图的透明度、现状与建议
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Modeling approaches to inform travel-related policies for COVID-19 containment: A scoping review and future directions.为制定与旅行相关的新冠疫情防控政策提供信息的建模方法：一项范围综述及未来方向
Travel Med Infect Dis. 2024 Nov-Dec;62:102730. doi: 10.1016/j.tmaid.2024.102730. Epub 2024 Jun 1.
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Robustness and reproducibility for AI learning in biomedical sciences: RENOIR.人工智能在生物医学科学中的稳健性和可重复性：RENOIR。
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Conceptualising the value of simulation modelling for public engagement with policy: a critical literature review.概念化模拟建模在公众参与政策方面的价值：批判性文献回顾。
Health Res Policy Syst. 2023 Nov 27;21(1):123. doi: 10.1186/s12961-023-01069-4.
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Inter-rater reliability of the infectious disease modeling reproducibility checklist (IDMRC) as applied to COVID-19 computational modeling research.传染病建模再现性清单（IDMRC）在 COVID-19 计算建模研究中的评价者间可靠性。
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Automatic transparency evaluation for open knowledge extraction systems.自动透明度评估用于开放知识提取系统。
J Biomed Semantics. 2023 Aug 31;14(1):12. doi: 10.1186/s13326-023-00293-9.
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Prevalence and predictors of data and code sharing in the medical and health sciences: systematic review with meta-analysis of individual participant data.在医学和健康科学领域中，数据和代码共享的流行率及其预测因素：基于个体参与者数据的系统评价和荟萃分析。
BMJ. 2023 Jul 11;382:e075767. doi: 10.1136/bmj-2023-075767.
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Inter-rater reliability of the Infectious Disease Modeling Reproducibility Checklist (IDMRC) as applied to COVID-19 computational modeling research.应用于新冠肺炎计算建模研究的传染病建模可重复性清单（IDMRC）的评分者间信度。
medRxiv. 2023 Mar 22:2023.03.21.23287529. doi: 10.1101/2023.03.21.23287529.
10
Validation framework for epidemiological models with application to COVID-19 models.流行病学模型验证框架及其在 COVID-19 模型中的应用。
PLoS Comput Biol. 2023 Mar 29;19(3):e1010968. doi: 10.1371/journal.pcbi.1010968. eCollection 2023 Mar.

本文引用的文献

1
Call for transparency of COVID-19 models.呼吁提高新冠病毒模型的透明度。
Science. 2020 May 1;368(6490):482-483. doi: 10.1126/science.abb8637.
2
Commentary on Ferguson, et al., "Impact of Non-pharmaceutical Interventions (NPIs) to Reduce COVID-19 Mortality and Healthcare Demand".评 Ferguson 等人的“减少 COVID-19 死亡率和医疗需求的非药物干预（NPIs）的影响”一文。
Bull Math Biol. 2020 Apr 8;82(4):52. doi: 10.1007/s11538-020-00726-x.
3
An empirical assessment of transparency and reproducibility-related research practices in the social sciences (2014-2017).社会科学中与透明度和可重复性相关的研究实践的实证评估（2014 - 2017年）
R Soc Open Sci. 2020 Feb 19;7(2):190806. doi: 10.1098/rsos.190806. eCollection 2020 Feb.
4
Early in the epidemic: impact of preprints on global discourse about COVID-19 transmissibility.疫情早期：预印本对全球关于新冠病毒传播性讨论的影响。
Lancet Glob Health. 2020 May;8(5):e627-e630. doi: 10.1016/S2214-109X(20)30113-3. Epub 2020 Mar 24.
5
Reproducible research practices, transparency, and open access data in the biomedical literature, 2015-2017.2015-2017 年生物医学文献中的可重复性研究实践、透明度和开放获取数据。
PLoS Biol. 2018 Nov 20;16(11):e2006930. doi: 10.1371/journal.pbio.2006930. eCollection 2018 Nov.
6
Guidelines for Accurate and Transparent Health Estimates Reporting: the GATHER statement.准确透明健康估计报告指南：GATHER 声明。
Lancet. 2016 Dec 10;388(10062):e19-e23. doi: 10.1016/S0140-6736(16)30388-9. Epub 2016 Jun 28.
7
Journals unite for reproducibility.期刊为可重复性而联合。
Science. 2014 Nov 7;346(6210):679. doi: 10.1126/science.aaa1724.

Transparency assessment of COVID-19 models.

作者信息

Jalali Mohammad S, DiGennaro Catherine, Sridhar Devi

机构信息

MGH Institute for Technology Assessment, Harvard Medical School, Boston, MA 02114, USA; Massachusetts Institute of Technology Sloan School of Management, Cambridge, MA, USA.

MGH Institute for Technology Assessment, Harvard Medical School, Boston, MA 02114, USA.

出版信息

Lancet Glob Health. 2020 Dec;8(12):e1459-e1460. doi: 10.1016/S2214-109X(20)30447-2. Epub 2020 Oct 27.

DOI:10.1016/S2214-109X(20)30447-2
PMID:33125915
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7833180/
Abstract
摘要