A. Batrimenko, Svetlana Denisova, D. Lisovskii, Sergey Orlov, S. Soshnikov
{"title":"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","authors":"A. Batrimenko, Svetlana Denisova, D. Lisovskii, Sergey Orlov, S. Soshnikov","doi":"10.1108/ijhg-09-2021-0094","DOIUrl":null,"url":null,"abstract":"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.","PeriodicalId":42859,"journal":{"name":"International Journal of Health Governance","volume":" ","pages":""},"PeriodicalIF":1.8000,"publicationDate":"2022-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"The Internet search engines as an additional tool in public health research in the context of disease outbreaks\",\"authors\":\"A. Batrimenko, Svetlana Denisova, D. Lisovskii, Sergey Orlov, S. 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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. 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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.
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.