互联网搜索引擎作为疾病暴发背景下公共卫生研究的额外工具

IF 1.8 Q3 HEALTH POLICY & SERVICES International Journal of Health Governance Pub Date : 2022-01-27 DOI:10.1108/ijhg-09-2021-0094
A. Batrimenko, Svetlana Denisova, D. Lisovskii, Sergey Orlov, S. Soshnikov
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引用次数: 2

摘要

目的该研究旨在帮助流行病学家利用搜索引擎数据,以当前的2019冠状病毒病(COVID-19)大流行为例,识别感染传播的新模式和趋势。该研究确定了俄罗斯互联网用户的主题搜索类型以及与公共卫生指标(COVID-19的死亡率和发病率)具有数学上证实的相关性的查询。该研究旨在确定当前COVID-19大流行的数字流行病学搜索趋势。该研究确定了RuNet用户的主题搜索类型和查询类型,这些类型和查询在数学上证实与公共卫生指标(COVID-19的死亡率和发病率)具有相关性。设计/方法/方法作者探索了两类数据:(1)从Yandex搜索引擎中提取的与COVID-19相关的关键词月度数据集;(2)官方公布的统计数据。此外,作者还搜索了该数据集中所有变量之间的关联。对得到的结果进行多假设检验的Benjamin-Hochberg校正,提高结果的信度。作者建立了一个独特的网站,有机会更新数据集,并设计了仪表板,使用PHP和Python可视化研究成果。研究结果表明,作者将流行病学解释为公共卫生研究的新工具,研究结果显示了许多重要的关系。有132个数据组合的相关性高于75%,从而有可能确定搜索统计趋势与死亡率/发病率指标之间在数学上可靠的关系。统计上最显著的效应在“查询”-“查询”,“查询”-“发病率”,“查询”-“死亡率”中发现。原创性/价值作者在对流行病学和信息学数据进行综合分析的基础上,提出了一种分析传染病暴发及其后果的新方法。研究结果与公共卫生相关,可以作为市民和专家的其他决策和情景分析工具,帮助他们对新冠肺炎疫情控制和监测总部的官方统计指标进行额外确认。
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The Internet search engines as an additional tool in public health research in the context of disease outbreaks
PurposeThe study aims to help epidemiologists identify new patterns and trends in spreading infections on the example of the current coronavirus disease 2019 (COVID-19) pandemic using data from search engines. The study identified the types of thematic search of Russian Internet users and queries that have a mathematically confirmed correlation with public health indicators: mortality and morbidity from COVID-19. The study aims to determine digital epidemiology search trends to the current COVID-19 pandemic. The study identified the types of thematic search of RuNet users and queries that have a mathematically confirmed correlation with public health indicators: mortality and morbidity from COVID-19.Design/methodology/approachThe authors explored two types of data: (1) the monthly datasets of keywords relevant to COVID-19 extracted from the Yandex search engine and (2) officially published statistics data. Alongside, the authors searched for associations between all variables in this dataset. The Benjamin–Hochberg correction for multiple hypothesis testing was applied to the obtained results to improve the reliability of the results. The authors built a unique website with opportunities to update datasets and designed dashboards to visualize the research outcomes using PHP and Python.FindingsThe research results show the number of significant relationships that the authors interpreted in epidemiology as a new instrument in Public Health research. There are 132 data combinations with a correlation higher than 75%, making it possible to determine a mathematically reliable relationship between search statistics trends and mortality/morbidity indicators. The most statistically significant effects identified in bundles “query” – “query”, “query” – “morbidity”, “query” – “mortality”.Originality/valueThe authors developed a new approach in analyzing outbreaks of infections and their consequences based on a comprehensive analysis of epidemiological and infodemic data. The research results are relevant to public health as other decision-making and situational analysis tools for citizens and specialists who want to receive additional confirmation for the indicators of the official statistics of the headquarters for control and monitoring of the situation with coronavirus and others infections.
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来源期刊
International Journal of Health Governance
International Journal of Health Governance HEALTH POLICY & SERVICES-
CiteScore
3.30
自引率
15.40%
发文量
28
期刊介绍: International Journal of Health Governance (IJHG) is oriented to serve those at the policy and governance levels within government, healthcare systems or healthcare organizations. It bridges the academic, public and private sectors, presenting case studies, research papers, reviews and viewpoints to provide an understanding of health governance that is both practical and actionable for practitioners, managers and policy makers. Policy and governance to promote, maintain or restore health extends beyond the clinical care aspect alone.
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