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Translating Scientific Knowledge to Government Decision Makers Has Crucial Importance in the Management of the COVID-19 Pandemic

机译:将科学知识转化为政府决策者对Covid-19大流行的管理至关重要

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摘要

In times of epidemics and humanitarian crises, it is essential to translate scientific findings into digestible information for government policy makers who have a short time to make critical decisions. To predict how far and fast the disease would spread across Hungary and to support the epidemiological decision-making process, a multidisciplinary research team performed a large amount of scientific data analysis and mathematical and socioeconomic modeling of the COVID-19 epidemic in Hungary, including modeling the medical resources and capacities, the regional differences, gross domestic product loss, the impact of closing and reopening elementary schools, and the optimal nationwide screening strategy for various virus-spreading scenarios and R metrics. KETLAK prepared 2 extensive reports on the problems identified and suggested solutions, and presented these directly to the National Epidemiological Policy-Making Body. The findings provided crucial data for the government to address critical measures regarding health care capacity, decide on restriction maintenance, change the actual testing strategy, and take regional economic, social, and health differences into account. Hungary managed the first part of the COVID-19 pandemic with low mortality rate. In times of epidemics, the formation of multidisciplinary research groups is essential for policy makers. The establishment, research activity, and participation in decision-making of these groups, such as KETLAK, can serve as a model for other countries, researchers, and policy makers not only in managing the challenges of COVID-19, but in future pandemics as well.
机译:在流行病和人道主义危机时期,必须将科学发现转化为有短时间做出重要决策的政府政策制定者的可消化信息。为了预测疾病在匈牙利蔓延和支持流行病学决策过程中,多学科研究团队在匈牙利进行了大量科学数据分析和数学和社会经济和社会经济建模,包括建模医疗资源和能力,区域差异,国内生产总值,关闭和重新开放的小学的影响,以及各种病毒传播场景和R度量的最佳全国范围内筛选策略。 Ketlak编写了关于所识别和建议解决方案的问题的大量报告,并直接向国家流行病学政策制定机构展示。该调查结果为政府提供了解决有关医疗保健能力的关键措施的重要数据,决定限制维护,改变实际测试策略,并考虑到区域经济,社会和健康差异。匈牙利以低死亡率管理Covid-19大流行的第一部分。在流行病时期,多学科研究群体的形成对于政策制定者至关重要。建立,研究活动和参与这些团体的决策,如Ketlak,可以作为其他国家,研究人员和政策制定者的模型,不仅在管理Covid-19的挑战,而且在未来的流行病中好。

著录项

  • 来源
    《Population health management》 |2021年第1期|35-45|共11页
  • 作者单位

    Univ Pecs Med Sch Dept Lab Med Pecs Hungary;

    Univ Pecs Genom & Bioinformat Core Facil Szentagothai Res Ctr Bioinformat Res Grp Pecs Hungary;

    Univ Pecs Med Sch Inst Translat Med Pecs Hungary;

    Ctr Econ & Reg Studies Inst Reg Studies Pecs Hungary;

    Ctr Econ & Reg Studies Inst Reg Studies Budapest Hungary;

    Univ Pecs Med Sch Dept Lab Med Pecs Hungary;

    Univ Pecs Med Sch Inst Translat Med Pecs Hungary|Univ Szeged Dept Med Ctr Translat Med Szeged Hungary;

    Univ Pecs Med Sch Inst Translat Med Pecs Hungary;

    Univ Pecs Med Sch Inst Translat Med Pecs Hungary;

    Univ Pecs Med Sch Inst Translat Med Pecs Hungary|Univ Szeged Dept Med Ctr Translat Med Szeged Hungary;

    Heim Pal Natl Pediat Inst Budapest Hungary;

    Univ Sopron Inst Int & Reg Econ Alexandre Lamfalussy Fac Econ Sopron Hungary;

    Univ Pecs Med Sch Inst Translat Med Pecs Hungary;

    Univ Pecs Genom & Bioinformat Core Facil Szentagothai Res Ctr Bioinformat Res Grp Pecs Hungary|Med Univ Bialystok Clin Res Ctr Bialystok Poland;

  • 收录信息
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    COVID-19; mortality; interdisciplinary; ICU capacity; testing; modeling;

    机译:Covid-19;死亡率;跨学科;ICU容量;测试;建模;
  • 入库时间 2022-08-19 01:57:25

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