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意大利四种最广泛传播的虫媒病毒疾病的信息流行病学与信息监测

Infodemiology and Infoveillance of the Four Most Widespread Arbovirus Diseases in Italy.

作者信息

Santangelo Omar Enzo, Provenzano Sandro, Vella Carlotta, Firenze Alberto, Stacchini Lorenzo, Cedrone Fabrizio, Gianfredi Vincenza

机构信息

Regional Health Care and Social Agency of Lodi, ASST Lodi, 26900 Lodi, Italy.

Faculty of Medicine, University of Milan, 20133 Milan, Italy.

出版信息

Epidemiologia (Basel). 2024 Jul 5;5(3):340-352. doi: 10.3390/epidemiologia5030024.

DOI:10.3390/epidemiologia5030024
PMID:39051204
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11270390/
Abstract

The purpose of this observational study was to evaluate the potential epidemiological trend of arboviral diseases most reported in Italy by the dedicated national surveillance system (ISS data) compared to searches on the internet, assessing whether a correlation/association between users' searches in Google and Wikipedia and real cases exists. The study considers a time interval from June 2012 to December 2023. We used the following Italian search terms: "Virus Toscana", "Virus del Nilo occidentale" (West Nile Virus in English), "Encefalite trasmessa da zecche" (Tick Borne encephalitis in English), and "Dengue". We overlapped Google Trends and Wikipedia data to perform a linear regression and correlation analysis. Statistical analyses were performed using Pearson's correlation coefficient (r) or Spearman's rank correlation coefficient (rho) as appropriate. All the correlations between the ISS data and Wikipedia or GT exhibited statistical significance. The correlations were strong for Dengue GT and ISS (rho = 0.71) and TBE GT and ISS (rho = 0.71), while the remaining correlations had values of r and rho between 0.32 and 0.67, showing a moderate temporal correlation. The observed correlations and regression models provide a foundation for future research, encouraging a more nuanced exploration of the dynamics between digital information-seeking behavior and disease prevalence.

摘要

这项观察性研究的目的是,评估意大利专门的国家监测系统(ISS数据)报告的最常见虫媒病毒病的潜在流行病学趋势,并与互联网搜索结果进行比较,以评估谷歌和维基百科上用户搜索与实际病例之间是否存在相关性/关联性。该研究考虑的时间间隔为2012年6月至2023年12月。我们使用了以下意大利语搜索词:“托斯卡纳病毒”、“西尼罗河病毒”、“蜱传脑炎”和“登革热”。我们将谷歌趋势和维基百科数据重叠,以进行线性回归和相关性分析。根据情况,使用皮尔逊相关系数(r)或斯皮尔曼等级相关系数(rho)进行统计分析。ISS数据与维基百科或谷歌趋势之间的所有相关性均具有统计学意义。登革热谷歌趋势与ISS之间的相关性很强(rho = 0.71),蜱传脑炎谷歌趋势与ISS之间的相关性也很强(rho = 0.71),而其余相关性的r和rho值在0.32至0.67之间,显示出中等程度的时间相关性。观察到的相关性和回归模型为未来的研究奠定了基础,鼓励对数字信息搜索行为与疾病流行率之间的动态关系进行更细致入微的探索。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6822/11270390/3c2fb47ee75b/epidemiologia-05-00024-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6822/11270390/7bb105340df0/epidemiologia-05-00024-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6822/11270390/5d8697e3759c/epidemiologia-05-00024-g002a.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6822/11270390/4d6f6b80cd55/epidemiologia-05-00024-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6822/11270390/3c2fb47ee75b/epidemiologia-05-00024-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6822/11270390/7bb105340df0/epidemiologia-05-00024-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6822/11270390/5d8697e3759c/epidemiologia-05-00024-g002a.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6822/11270390/4d6f6b80cd55/epidemiologia-05-00024-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6822/11270390/3c2fb47ee75b/epidemiologia-05-00024-g004.jpg

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本文引用的文献

1
Infodemiology and infoveillance: framework for contagious exanthematous diseases, of childhood in Italy.信息流行病学和信息监测:意大利儿童传染性出疹性疾病框架。
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2
Arbovirus and its potential to lead the next global pandemic from sub-Saharan Africa: What lessons have we learned from COVID-19?虫媒病毒及其引发撒哈拉以南非洲地区下一次全球大流行的可能性:我们从新冠疫情中学到了什么?
Germs. 2022 Dec 31;12(4):538-547. doi: 10.18683/germs.2022.1358. eCollection 2022 Dec.
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Wikipedia page views for health research: a review.
维基百科健康研究页面浏览量:一项综述
Front Big Data. 2023 Jul 4;6:1199060. doi: 10.3389/fdata.2023.1199060. eCollection 2023.
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Digital Disease Surveillance for Emerging Infectious Diseases: An Early Warning System Using the Internet and Social Media Data for COVID-19 Forecasting in Canada.利用互联网和社交媒体数据进行数字疾病监测以发现新发传染病:用于加拿大 COVID-19 预测的预警系统。
Stud Health Technol Inform. 2023 May 18;302:861-865. doi: 10.3233/SHTI230290.
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Using Google Trends and Wikipedia to Investigate the Global Public's Interest in the Pancreatic Cancer Diagnosis of a Celebrity.利用谷歌趋势和维基百科调查全球公众对名人胰腺癌诊断的兴趣。
Int J Environ Res Public Health. 2023 Jan 24;20(3):2106. doi: 10.3390/ijerph20032106.
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Internet search data with spatiotemporal analysis in infectious disease surveillance: Challenges and perspectives.利用时空分析进行传染病监测的互联网搜索数据:挑战与展望。
Front Public Health. 2022 Dec 5;10:958835. doi: 10.3389/fpubh.2022.958835. eCollection 2022.
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Effectiveness of early warning systems in the detection of infectious diseases outbreaks: a systematic review.早期预警系统在传染病爆发检测中的效果:系统评价。
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Explanation of hand, foot, and mouth disease cases in Japan using Google Trends before and during the COVID-19: infodemiology study.在COVID-19疫情之前及期间利用谷歌趋势对日本手足口病病例进行的解释:信息流行病学研究
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