Suppr超能文献

谷歌趋势分析与圣保罗州登革热和黄热病疫情爆发的相关性和敏感性。

Google Trends correlation and sensitivity for outbreaks of dengue and yellow fever in the state of São Paulo.

机构信息

Faculdade Israelita de Ciências da Saúde Albert Einstein, Hospital Israelita Albert Einstein, São Paulo, SP, Brazil.

出版信息

Einstein (Sao Paulo). 2021 Aug 2;19:eAO5969. doi: 10.31744/einstein_journal/2021AO5969. eCollection 2021.

Abstract

OBJECTIVE

To assess Google Trends accuracy for epidemiological surveillance of dengue and yellow fever, and to compare the incidence of these diseases with the popularity of its terms in the state of São Paulo.

METHODS

Retrospective cohort. Google Trends survey results were compared to the actual incidence of diseases, obtained from Centro de Vigilância Epidemiológica "Prof. Alexandre Vranjac", in São Paulo, Brazil, in periods between 2017 and 2019. The correlation was calculated by Pearson's coefficient and cross-correlation function. The accuracy was analyzed by sensitivity and specificity values.

RESULTS

There was a statistically significant correlation between the variables studied for both diseases, Pearson coefficient of 0.91 for dengue and 0.86 for yellow fever. Correlation with up to 4 weeks of anticipation for time series was identified. Sensitivity was 87% and 90%, and specificity 69% and 78% for dengue and yellow fever, respectively.

CONCLUSION

The incidence of dengue and yellow fever in the State of São Paulo showed a strong correlation with the popularity of its terms measured by Google Trends in weekly periods. Google Trends tool provided early warning, with high sensitivity, for the detection of outbreaks of these diseases.

摘要

目的

评估 Google Trends 在登革热和黄热病流行病学监测中的准确性,并比较这些疾病的发病率与巴西圣保罗州其术语的流行程度。

方法

回顾性队列研究。将 Google Trends 调查结果与巴西圣保罗 Centro de Vigilância Epidemiológica“Prof. Alexandre Vranjac”获得的 2017 年至 2019 年期间疾病实际发病率进行比较。通过 Pearson 系数和交叉相关函数计算相关性。通过灵敏度和特异性值分析准确性。

结果

两种疾病的变量之间存在统计学显著相关性,登革热的 Pearson 系数为 0.91,黄热病为 0.86。识别出与时间序列提前 4 周的相关性。登革热和黄热病的灵敏度分别为 87%和 90%,特异性分别为 69%和 78%。

结论

巴西圣保罗州的登革热和黄热病发病率与 Google Trends 测量的其术语的流行程度呈强相关,在每周时间段内提供了对这些疾病暴发的早期预警,具有较高的灵敏度。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0494/8302225/8d407f7efb45/2317-6385-eins-19-eAO5969-gf01.jpg

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验