"VSports注册入口" Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The . gov means it’s official. Federal government websites often end in . gov or VSports app下载. mil. Before sharing sensitive information, make sure you’re on a federal government site. .

Https

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely. V体育官网.

. 2019 Feb 20;21(2):e10450.
doi: 10.2196/10450.

VSports - Causal Relationships Among Pollen Counts, Tweet Numbers, and Patient Numbers for Seasonal Allergic Rhinitis Surveillance: Retrospective Analysis

Shoko Wakamiya  1   2   3 , Shoji Matsune  4 , Kimihiro Okubo  5 , Eiji Aramaki  1   2   3
Affiliations

Causal Relationships Among Pollen Counts, Tweet Numbers, and Patient Numbers for Seasonal Allergic Rhinitis Surveillance: Retrospective Analysis

Shoko Wakamiya et al. J Med Internet Res. .

Abstract

Background: Health-related social media data are increasingly used in disease-surveillance studies, which have demonstrated moderately high correlations between the number of social media posts and the number of patients. However, there is a need to understand the causal relationship between the behavior of social media users and the actual number of patients in order to increase the credibility of disease surveillance based on social media data VSports手机版. .

Objective: This study aimed to clarify the causal relationships among pollen count, the posting behavior of social media users, and the number of patients with seasonal allergic rhinitis in the real world. V体育安卓版.

Methods: This analysis was conducted using datasets of pollen counts, tweet numbers, and numbers of patients with seasonal allergic rhinitis from Kanagawa Prefecture, Japan. We examined daily pollen counts for Japanese cedar (the major cause of seasonal allergic rhinitis in Japan) and hinoki cypress (which commonly complicates seasonal allergic rhinitis) from February 1 to May 31, 2017. The daily numbers of tweets that included the keyword "kafunshō" (or seasonal allergic rhinitis) were calculated between January 1 and May 31, 2017. Daily numbers of patients with seasonal allergic rhinitis from January 1 to May 31, 2017, were obtained from three healthcare institutes that participated in the study V体育ios版. The Granger causality test was used to examine the causal relationships among pollen count, tweet numbers, and the number of patients with seasonal allergic rhinitis from February to May 2017. To determine if time-variant factors affect these causal relationships, we analyzed the main seasonal allergic rhinitis phase (February to April) when Japanese cedar trees actively produce and release pollen. .

Results: Increases in pollen count were found to increase the number of tweets during the overall study period (P=. 04), but not the main seasonal allergic rhinitis phase (P=. 05). In contrast, increases in pollen count were found to increase patient numbers in both the study period (P=. 04) and the main seasonal allergic rhinitis phase (P=. 01). Increases in the number of tweets increased the patient numbers during the main seasonal allergic rhinitis phase (P=. 02), but not the overall study period (P=. 89). Patient numbers did not affect the number of tweets in both the overall study period (P=. 24) and the main seasonal allergic rhinitis phase (P=. 47) VSports最新版本. .

Conclusions: Understanding the causal relationships among pollen counts, tweet numbers, and numbers of patients with seasonal allergic rhinitis is an important step to increasing the credibility of surveillance systems that use social media data. Further in-depth studies are needed to identify the determinants of social media posts described in this exploratory analysis V体育平台登录. .

Keywords: Twitter; causal relationship; disease surveillance; infoveillance; seasonal allergic rhinitis; social media. VSports注册入口.

PubMed Disclaimer

Conflict of interest statement

Conflicts of Interest: None declared.

VSports手机版 - Figures

Figure 1
Figure 1
Time-based changes in data in Kanagawa Prefecture, Japan. The X-axes represent the date and the Y-axes represent data counts, to which min-max normalization is applied for the following variables: (a) changes in pollen count (mean daily pollen count from three observation sites within Kanagawa Prefecture), (b) changes in the number of SAR-related tweets, and (c) changes in the number of SAR patients (daily number of patients from three participating health care institutes within Kanagawa Prefecture). The solid line represents the changes in the number of patients on nonconsultation days (ie, days when an institute is closed) that were supplemented by the patient numbers from the preceding and proceeding days; the dashed line represents the changes in the reported number of patients. Our analysis used the supplemented patient numbers. SAR: seasonal allergic rhinitis.
Figure 2
Figure 2
Causal relationships between pollen count, number of SAR-related tweets, and number of patients with SAR (Granger causality test results) for (a) the overall study period and (b) the main SAR phase. The Granger causality test did not reveal any causal relationships between these variables in the concluding phase of the season. SAR: seasonal allergic rhinitis.
Figure 3
Figure 3
Peak number of SAR-related tweets, number of SAR patients, and pollen count. The X-axes represent the date and the Y-axes represent the min-max normalized data values for the three variables. The blue line represents the number of tweets, the green line represents the number of patients, the orange line represents the pollen count, and the grey line represents the mean temperature. SAR: seasonal allergic rhinitis.
See this image and copyright information in PMC

References

    1. Lee JL, DeCamp M, Dredze M, Chisolm MS, Berger ZD. What are health-related users tweeting? A qualitative content analysis of health-related users and their messages on twitter. J Med Internet Res. 2014;16(10):e237. doi: 10.2196/jmir.3765. http://www.jmir.org/2014/10/e237/ v16i10e237 - DOI - PMC - PubMed
    1. Alnemer KA, Alhuzaim WM, Alnemer AA, Alharbi BB, Bawazir AS, Barayyan OR, Balaraj FK. Are Health-Related Tweets Evidence Based? Review and Analysis of Health-Related Tweets on Twitter. J Med Internet Res. 2015 Oct;17(10):e246. doi: 10.2196/jmir.4898. http://www.jmir.org/2015/10/e246/ v17i10e246 - DOI - PMC - PubMed
    1. Eysenbach G. Infodemiology and infoveillance: framework for an emerging set of public health informatics methods to analyze search, communication and publication behavior on the Internet. J Med Internet Res. 2009;11(1):e11. doi: 10.2196/jmir.1157. http://www.jmir.org/2009/1/e11/ v11i1e11 - DOI - PMC - PubMed
    1. Charles-Smith L, Reynolds T, Cameron M, Conway M, Lau E, Olsen J, Pavlin J, Shigematsu M, Streichert L, Suda K, Corley C. Using Social Media for Actionable Disease Surveillance and Outbreak Management: A Systematic Literature Review. PLoS One. 2015 Oct 05;10(10):e0139701. doi: 10.1371/journal.pone.0139701. http://dx.plos.org/10.1371/journal.pone.0139701 PONE-D-15-21599 - DOI - DOI - PMC - PubMed
    1. Paul MJ, Sarker A, Brownstein JS, Nikfarjam A, Scotch M, Smith KL, Gonzalez G. Social Media Mining for Public Health Monitoring and Surveillance. Pacific Symposium on Biocomputing 2016, PSB 2016; The 21st Pacific Symposium on Biocomputing (PSB '16); 2016; Big Island, United States. World Scientific Publishing Co. Pte Ltd; 2016. pp. 468–479.

"V体育官网" Publication types

LinkOut - more resources (V体育官网)