Forecasting the west nile virus in the United States: An extensive novel data streams-based time series analysis and structural equation modeling of related digital searching behavior

Abdulla Watad; Samaa Watad; Naim Mahroum; Kassem Sharif; Howard Amital; Nicola Luigi Bragazzi; Mohammad Adawi

doi:10.2196/publichealth.9176

Forecasting the west nile virus in the United States: An extensive novel data streams-based time series analysis and structural equation modeling of related digital searching behavior

Abdulla Watad, Samaa Watad, Naim Mahroum, Kassem Sharif, Howard Amital, Nicola Luigi Bragazzi, Mohammad Adawi

Bar-Ilan University - Azrieli Faculty of Medicine

Research output: Contribution to journal › Article › peer-review

Abstract

Background: West Nile virus is an arbovirus responsible for an infection that tends to peak during the late summer and early fall. Tools monitoring Web searches are emerging as powerful sources of data, especially concerning infectious diseases such as West Nile virus. Objective: This study aimed at exploring the potential predictive power of West Nile virus-related Web searches. Methods: Different novel data streams, including Google Trends, WikiTrends, YouTube, and Google News, were used to extract search trends. Data regarding West Nile virus cases were obtained from the Centers for Disease Control and Prevention. Data were analyzed using regression, times series analysis, structural equation modeling, and clustering analysis. Results: In the regression analysis, an association between Web searches and “real-world” epidemiological figures was found. The best seasonal autoregressive integrated moving average model with explicative variable (SARIMAX) was found to be (0,1,1)x(0,1,1)4. Using data from 2004 to 2015, we were able to predict data for 2016. From the structural equation modeling, the consumption of West Nile virus-related news fully mediated the relation between Google Trends and the consumption of YouTube videos, as well as the relation between the latter variable and the number of West Nile virus cases. Web searches fully mediated the relation between epidemiological figures and the consumption of YouTube videos, as well as the relation between epidemiological data and the number of accesses to the West Nile virus-related Wikipedia page. In the clustering analysis, the consumption of news was most similar to the Web searches pattern, which was less close to the consumption of YouTube videos and least similar to the behavior of accessing West Nile virus-related Wikipedia pages. Conclusions: Our study demonstrated an association between epidemiological data and search patterns related to the West Nile virus. Based on this correlation, further studies are needed to examine the practicality of these findings.

Original language	English
Article number	e9176
Journal	JMIR Public Health and Surveillance
Volume	5
Issue number	1
DOIs	https://doi.org/10.2196/publichealth.9176
State	Published - Jan 2019

Bibliographical note

Publisher Copyright:
© Abdulla Watad, Samaa Watad, Naim Mahroum, Kassem Sharif, Howard Amital, Nicola Luigi Bragazzi, Mohammad Adawi. Originally published in JMIR Public Health and Surveillance (http://publichealth.jmir.org), 28.02.2019. This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Public Health and Surveillance, is properly cited. The complete bibliographic information, a link to the original publication on http://publichealth.jmir.org, as well as this copyright and license information must be included.

Keywords

Forecasting model
Google trends
Infodemiology
Infoveillance
Seasonal autoregressive integrated moving average model with explicative variable (SARIMAX)
West nile virus

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10.2196/publichealth.9176

Cite this

Watad, A., Watad, S., Mahroum, N., Sharif, K., Amital, H., Bragazzi, N. L., & Adawi, M. (2019). Forecasting the west nile virus in the United States: An extensive novel data streams-based time series analysis and structural equation modeling of related digital searching behavior. JMIR Public Health and Surveillance, 5(1), [e9176]. https://doi.org/10.2196/publichealth.9176

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title = "Forecasting the west nile virus in the United States: An extensive novel data streams-based time series analysis and structural equation modeling of related digital searching behavior",

abstract = "Background: West Nile virus is an arbovirus responsible for an infection that tends to peak during the late summer and early fall. Tools monitoring Web searches are emerging as powerful sources of data, especially concerning infectious diseases such as West Nile virus. Objective: This study aimed at exploring the potential predictive power of West Nile virus-related Web searches. Methods: Different novel data streams, including Google Trends, WikiTrends, YouTube, and Google News, were used to extract search trends. Data regarding West Nile virus cases were obtained from the Centers for Disease Control and Prevention. Data were analyzed using regression, times series analysis, structural equation modeling, and clustering analysis. Results: In the regression analysis, an association between Web searches and “real-world” epidemiological figures was found. The best seasonal autoregressive integrated moving average model with explicative variable (SARIMAX) was found to be (0,1,1)x(0,1,1)4. Using data from 2004 to 2015, we were able to predict data for 2016. From the structural equation modeling, the consumption of West Nile virus-related news fully mediated the relation between Google Trends and the consumption of YouTube videos, as well as the relation between the latter variable and the number of West Nile virus cases. Web searches fully mediated the relation between epidemiological figures and the consumption of YouTube videos, as well as the relation between epidemiological data and the number of accesses to the West Nile virus-related Wikipedia page. In the clustering analysis, the consumption of news was most similar to the Web searches pattern, which was less close to the consumption of YouTube videos and least similar to the behavior of accessing West Nile virus-related Wikipedia pages. Conclusions: Our study demonstrated an association between epidemiological data and search patterns related to the West Nile virus. Based on this correlation, further studies are needed to examine the practicality of these findings.",

keywords = "Forecasting model, Google trends, Infodemiology, Infoveillance, Seasonal autoregressive integrated moving average model with explicative variable (SARIMAX), West nile virus",

author = "Abdulla Watad and Samaa Watad and Naim Mahroum and Kassem Sharif and Howard Amital and Bragazzi, {Nicola Luigi} and Mohammad Adawi",

note = "Publisher Copyright: {\textcopyright} Abdulla Watad, Samaa Watad, Naim Mahroum, Kassem Sharif, Howard Amital, Nicola Luigi Bragazzi, Mohammad Adawi. Originally published in JMIR Public Health and Surveillance (http://publichealth.jmir.org), 28.02.2019. This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Public Health and Surveillance, is properly cited. The complete bibliographic information, a link to the original publication on http://publichealth.jmir.org, as well as this copyright and license information must be included.",

year = "2019",

month = jan,

doi = "10.2196/publichealth.9176",

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Watad, A, Watad, S, Mahroum, N, Sharif, K, Amital, H, Bragazzi, NL & Adawi, M 2019, 'Forecasting the west nile virus in the United States: An extensive novel data streams-based time series analysis and structural equation modeling of related digital searching behavior', JMIR Public Health and Surveillance, vol. 5, no. 1, e9176. https://doi.org/10.2196/publichealth.9176

Forecasting the west nile virus in the United States: An extensive novel data streams-based time series analysis and structural equation modeling of related digital searching behavior. / Watad, Abdulla; Watad, Samaa; Mahroum, Naim et al.
In: JMIR Public Health and Surveillance, Vol. 5, No. 1, e9176, 01.2019.

Research output: Contribution to journal › Article › peer-review

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T1 - Forecasting the west nile virus in the United States

T2 - An extensive novel data streams-based time series analysis and structural equation modeling of related digital searching behavior

AU - Watad, Abdulla

AU - Watad, Samaa

AU - Mahroum, Naim

AU - Sharif, Kassem

AU - Amital, Howard

AU - Bragazzi, Nicola Luigi

AU - Adawi, Mohammad

N1 - Publisher Copyright: © Abdulla Watad, Samaa Watad, Naim Mahroum, Kassem Sharif, Howard Amital, Nicola Luigi Bragazzi, Mohammad Adawi. Originally published in JMIR Public Health and Surveillance (http://publichealth.jmir.org), 28.02.2019. This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Public Health and Surveillance, is properly cited. The complete bibliographic information, a link to the original publication on http://publichealth.jmir.org, as well as this copyright and license information must be included.

PY - 2019/1

Y1 - 2019/1

N2 - Background: West Nile virus is an arbovirus responsible for an infection that tends to peak during the late summer and early fall. Tools monitoring Web searches are emerging as powerful sources of data, especially concerning infectious diseases such as West Nile virus. Objective: This study aimed at exploring the potential predictive power of West Nile virus-related Web searches. Methods: Different novel data streams, including Google Trends, WikiTrends, YouTube, and Google News, were used to extract search trends. Data regarding West Nile virus cases were obtained from the Centers for Disease Control and Prevention. Data were analyzed using regression, times series analysis, structural equation modeling, and clustering analysis. Results: In the regression analysis, an association between Web searches and “real-world” epidemiological figures was found. The best seasonal autoregressive integrated moving average model with explicative variable (SARIMAX) was found to be (0,1,1)x(0,1,1)4. Using data from 2004 to 2015, we were able to predict data for 2016. From the structural equation modeling, the consumption of West Nile virus-related news fully mediated the relation between Google Trends and the consumption of YouTube videos, as well as the relation between the latter variable and the number of West Nile virus cases. Web searches fully mediated the relation between epidemiological figures and the consumption of YouTube videos, as well as the relation between epidemiological data and the number of accesses to the West Nile virus-related Wikipedia page. In the clustering analysis, the consumption of news was most similar to the Web searches pattern, which was less close to the consumption of YouTube videos and least similar to the behavior of accessing West Nile virus-related Wikipedia pages. Conclusions: Our study demonstrated an association between epidemiological data and search patterns related to the West Nile virus. Based on this correlation, further studies are needed to examine the practicality of these findings.

AB - Background: West Nile virus is an arbovirus responsible for an infection that tends to peak during the late summer and early fall. Tools monitoring Web searches are emerging as powerful sources of data, especially concerning infectious diseases such as West Nile virus. Objective: This study aimed at exploring the potential predictive power of West Nile virus-related Web searches. Methods: Different novel data streams, including Google Trends, WikiTrends, YouTube, and Google News, were used to extract search trends. Data regarding West Nile virus cases were obtained from the Centers for Disease Control and Prevention. Data were analyzed using regression, times series analysis, structural equation modeling, and clustering analysis. Results: In the regression analysis, an association between Web searches and “real-world” epidemiological figures was found. The best seasonal autoregressive integrated moving average model with explicative variable (SARIMAX) was found to be (0,1,1)x(0,1,1)4. Using data from 2004 to 2015, we were able to predict data for 2016. From the structural equation modeling, the consumption of West Nile virus-related news fully mediated the relation between Google Trends and the consumption of YouTube videos, as well as the relation between the latter variable and the number of West Nile virus cases. Web searches fully mediated the relation between epidemiological figures and the consumption of YouTube videos, as well as the relation between epidemiological data and the number of accesses to the West Nile virus-related Wikipedia page. In the clustering analysis, the consumption of news was most similar to the Web searches pattern, which was less close to the consumption of YouTube videos and least similar to the behavior of accessing West Nile virus-related Wikipedia pages. Conclusions: Our study demonstrated an association between epidemiological data and search patterns related to the West Nile virus. Based on this correlation, further studies are needed to examine the practicality of these findings.

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KW - Google trends

KW - Infodemiology

KW - Infoveillance

KW - Seasonal autoregressive integrated moving average model with explicative variable (SARIMAX)

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Forecasting the west nile virus in the United States: An extensive novel data streams-based time series analysis and structural equation modeling of related digital searching behavior

Abstract

Bibliographical note

Keywords

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