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Causality Analysis of Google Trends and Dengue Incidence in Bandung, Indonesia With Linkage of Digital Data Modeling: Longitudinal Observational Study

BACKGROUND: The popularity of dengue can be inferred from Google Trends that summarizes Google searches of related topics. Both the disease and its Google Trends have a similar source of causation in the dengue virus, leading us to hypothesize that dengue incidence and Google Trends results have a l...

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Autores principales: Syamsuddin, Muhammad, Fakhruddin, Muhammad, Sahetapy-Engel, Jane Theresa Marlen, Soewono, Edy
Formato: Online Artículo Texto
Lenguaje:English
Publicado: JMIR Publications 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7414412/
https://www.ncbi.nlm.nih.gov/pubmed/32706682
http://dx.doi.org/10.2196/17633
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author Syamsuddin, Muhammad
Fakhruddin, Muhammad
Sahetapy-Engel, Jane Theresa Marlen
Soewono, Edy
author_facet Syamsuddin, Muhammad
Fakhruddin, Muhammad
Sahetapy-Engel, Jane Theresa Marlen
Soewono, Edy
author_sort Syamsuddin, Muhammad
collection PubMed
description BACKGROUND: The popularity of dengue can be inferred from Google Trends that summarizes Google searches of related topics. Both the disease and its Google Trends have a similar source of causation in the dengue virus, leading us to hypothesize that dengue incidence and Google Trends results have a long-run equilibrium. OBJECTIVE: This research aimed to investigate the properties of this long-run equilibrium in the hope of using the information derived from Google Trends for the early detection of upcoming dengue outbreaks. METHODS: This research used the cointegration method to assess a long-run equilibrium between dengue incidence and Google Trends results. The long-run equilibrium was characterized by their linear combination that generated a stationary process. The Dickey-Fuller test was adopted to check the stationarity of the processes. An error correction model (ECM) was then adopted to measure deviations from the long-run equilibrium to examine the short-term and long-term effects. The resulting models were used to determine the Granger causality between the two processes. Additional information about the two processes was obtained by examining the impulse response function and variance decomposition. RESULTS: The Dickey-Fuller test supported an implicit null hypothesis that the dengue incidence and Google Trends results are nonstationary processes (P=.01). A further test showed that the processes were cointegrated (P=.01), indicating that their particular linear combination is a stationary process. These results permitted us to construct ECMs. The model showed the direction of causality of the two processes, indicating that Google Trends results will Granger-cause dengue incidence (not in the reverse order). CONCLUSIONS: Various hypothesis testing results in this research concluded that Google Trends results can be used as an initial indicator of upcoming dengue outbreaks.
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spelling pubmed-74144122020-08-20 Causality Analysis of Google Trends and Dengue Incidence in Bandung, Indonesia With Linkage of Digital Data Modeling: Longitudinal Observational Study Syamsuddin, Muhammad Fakhruddin, Muhammad Sahetapy-Engel, Jane Theresa Marlen Soewono, Edy J Med Internet Res Original Paper BACKGROUND: The popularity of dengue can be inferred from Google Trends that summarizes Google searches of related topics. Both the disease and its Google Trends have a similar source of causation in the dengue virus, leading us to hypothesize that dengue incidence and Google Trends results have a long-run equilibrium. OBJECTIVE: This research aimed to investigate the properties of this long-run equilibrium in the hope of using the information derived from Google Trends for the early detection of upcoming dengue outbreaks. METHODS: This research used the cointegration method to assess a long-run equilibrium between dengue incidence and Google Trends results. The long-run equilibrium was characterized by their linear combination that generated a stationary process. The Dickey-Fuller test was adopted to check the stationarity of the processes. An error correction model (ECM) was then adopted to measure deviations from the long-run equilibrium to examine the short-term and long-term effects. The resulting models were used to determine the Granger causality between the two processes. Additional information about the two processes was obtained by examining the impulse response function and variance decomposition. RESULTS: The Dickey-Fuller test supported an implicit null hypothesis that the dengue incidence and Google Trends results are nonstationary processes (P=.01). A further test showed that the processes were cointegrated (P=.01), indicating that their particular linear combination is a stationary process. These results permitted us to construct ECMs. The model showed the direction of causality of the two processes, indicating that Google Trends results will Granger-cause dengue incidence (not in the reverse order). CONCLUSIONS: Various hypothesis testing results in this research concluded that Google Trends results can be used as an initial indicator of upcoming dengue outbreaks. JMIR Publications 2020-07-24 /pmc/articles/PMC7414412/ /pubmed/32706682 http://dx.doi.org/10.2196/17633 Text en ©Muhammad Syamsuddin, Muhammad Fakhruddin, Jane Theresa Marlen Sahetapy-Engel, Edy Soewono. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 24.07.2020. https://creativecommons.org/licenses/by/4.0/ 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 the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on http://www.jmir.org/, as well as this copyright and license information must be included.
spellingShingle Original Paper
Syamsuddin, Muhammad
Fakhruddin, Muhammad
Sahetapy-Engel, Jane Theresa Marlen
Soewono, Edy
Causality Analysis of Google Trends and Dengue Incidence in Bandung, Indonesia With Linkage of Digital Data Modeling: Longitudinal Observational Study
title Causality Analysis of Google Trends and Dengue Incidence in Bandung, Indonesia With Linkage of Digital Data Modeling: Longitudinal Observational Study
title_full Causality Analysis of Google Trends and Dengue Incidence in Bandung, Indonesia With Linkage of Digital Data Modeling: Longitudinal Observational Study
title_fullStr Causality Analysis of Google Trends and Dengue Incidence in Bandung, Indonesia With Linkage of Digital Data Modeling: Longitudinal Observational Study
title_full_unstemmed Causality Analysis of Google Trends and Dengue Incidence in Bandung, Indonesia With Linkage of Digital Data Modeling: Longitudinal Observational Study
title_short Causality Analysis of Google Trends and Dengue Incidence in Bandung, Indonesia With Linkage of Digital Data Modeling: Longitudinal Observational Study
title_sort causality analysis of google trends and dengue incidence in bandung, indonesia with linkage of digital data modeling: longitudinal observational study
topic Original Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7414412/
https://www.ncbi.nlm.nih.gov/pubmed/32706682
http://dx.doi.org/10.2196/17633
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