Causality Analysis of Google Trends and Dengue Incidence in Bandung, Indonesia With Linkage of Digital Data Modeling: Longitudinal Observational Study
BackgroundThe 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 T...
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doaj-2ec3a43c6171413680cbb3cb17f018f62021-04-02T21:36:35ZengJMIR PublicationsJournal of Medical Internet Research1438-88712020-07-01227e1763310.2196/17633Causality Analysis of Google Trends and Dengue Incidence in Bandung, Indonesia With Linkage of Digital Data Modeling: Longitudinal Observational StudySyamsuddin, MuhammadFakhruddin, MuhammadSahetapy-Engel, Jane Theresa MarlenSoewono, Edy BackgroundThe 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. ObjectiveThis 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. MethodsThis 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. ResultsThe 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). ConclusionsVarious hypothesis testing results in this research concluded that Google Trends results can be used as an initial indicator of upcoming dengue outbreaks.http://www.jmir.org/2020/7/e17633/ |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Syamsuddin, Muhammad Fakhruddin, Muhammad Sahetapy-Engel, Jane Theresa Marlen Soewono, Edy |
spellingShingle |
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 Journal of Medical Internet Research |
author_facet |
Syamsuddin, Muhammad Fakhruddin, Muhammad Sahetapy-Engel, Jane Theresa Marlen Soewono, Edy |
author_sort |
Syamsuddin, Muhammad |
title |
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_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_sort |
causality analysis of google trends and dengue incidence in bandung, indonesia with linkage of digital data modeling: longitudinal observational study |
publisher |
JMIR Publications |
series |
Journal of Medical Internet Research |
issn |
1438-8871 |
publishDate |
2020-07-01 |
description |
BackgroundThe 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.
ObjectiveThis 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.
MethodsThis 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.
ResultsThe 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).
ConclusionsVarious hypothesis testing results in this research concluded that Google Trends results can be used as an initial indicator of upcoming dengue outbreaks. |
url |
http://www.jmir.org/2020/7/e17633/ |
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