用于控制 COVID-19 政策的政策分析和数据挖掘工具。

IF 2 Q3 MATHEMATICAL & COMPUTATIONAL BIOLOGY Network Modeling and Analysis in Health Informatics and Bioinformatics Pub Date : 2023-01-01 Epub Date: 2022-12-05 DOI:10.1007/s13721-022-00400-3
Yoshiyasu Takefuji
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摘要

针对 COVID-19 大流行的疫苗疗效已进行了大量研究,但这些说法尚未在现实世界中实现。本文提出了三种 COVID-19 政策结果分析工具,如 jpscore(用于评分和揭示日本最佳都道府县政策)、scorecovid(用于评分和揭示世界最佳国家政策)和 hiscovid(用于可视化和识别政策制定者在时间序列评分中的失误)。得分较差的国家或县可以从得分优秀的国家或县那里学到好的策略。三种工具都基于一个单一指标,即用 COVID-19 死亡人数除以百万人口。三种工具建议我们在全球范围内采用可持续的强制检测隔离策略来缓解疫情。本文还论述了日本在缓解疫情的科学循证研究方面的不足之处。可视化工具以及政策结果的分类和时间序列评分有助于决策者做出正确决策。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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Policy analysis and data mining tools for controlling COVID-19 policies.

Much research has been done on the efficacy of vaccines against the COVID-19 pandemic, but the claims have not yet been realized in the real world. This paper proposes three COVID-19 policy outcome analysis tools such as jpscore for scoring and revealing the best prefecture policy in Japan, scorecovid for scoring and revealing the best country policy in the world, and finally hiscovid for visualizing and identifying when policymakers made mistakes in time-series scores. Poorly scored countries or prefectures can learn good strategies from the best country or prefecture with excellent scores. Three tools are based on a single metric dividing the number of COVID-19 deaths by the population in millions. Three tools suggest us that the sustainable mandatory test-isolation strategy should be adopted in the world for mitigating the pandemic. This paper also addresses what is lacking in Japan for scientific evidence-based research for mitigating the pandemic. Visualization tools and sorted and time-series scores of policy outcomes help policymakers make the right decisions.

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来源期刊
CiteScore
5.40
自引率
4.30%
发文量
43
期刊介绍: NetMAHIB publishes original research articles and reviews reporting how graph theory, statistics, linear algebra and machine learning techniques can be effectively used for modelling and analysis in health informatics and bioinformatics. It aims at creating a synergy between these disciplines by providing a forum for disseminating the latest developments and research findings; hence, results can be shared with readers across institutions, governments, researchers, students, and the industry. The journal emphasizes fundamental contributions on new methodologies, discoveries and techniques that have general applicability and which form the basis for network based modelling, knowledge discovery, knowledge sharing and decision support to the benefit of patients, healthcare professionals and society in traditional and advanced emerging settings, including eHealth and mHealth .
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