This paper is in the following e-collection/theme issue:

Infodemiology and Infoveillance (1558) Infectious Diseases (non-STD/STI) (1726) Infoveillance, Infodemiology, Digital Disease Surveillance, Infodemic Management (1139)

Published on in Vol 22, No 7 (2020): July

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/17633, first published .
Causality Analysis of Google Trends and Dengue Incidence in Bandung, Indonesia With Linkage of Digital Data Modeling: Longitudinal Observational Study

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

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

Authors of this article:

Muhammad Syamsuddin1 Author Orcid Image ;   Muhammad Fakhruddin1 Author Orcid Image ;   Jane Theresa Marlen Sahetapy-Engel1 Author Orcid Image ;   Edy Soewono1 Author Orcid Image
Article Authors Cited by (20) Tweetations (5) Metrics

Journals

  1. Johnson A, Bhaumik R, Tabidze I, Mehta S. Nowcasting Sexually Transmitted Infections in Chicago: Predictive Modeling and Evaluation Study Using Google Trends. JMIR Public Health and Surveillance 2020;6(4):e20588 View
  2. Wang P, Xu Q, Cao R, Deng F, Lei S. Global Public Interests and Dynamic Trends in Osteoporosis From 2004 to 2019: Infodemiology Study. Journal of Medical Internet Research 2021;23(7):e25422 View
  3. Sato K, Mano T, Iwata A, Toda T. Need of care in interpreting Google Trends-based COVID-19 infodemiological study results: potential risk of false-positivity. BMC Medical Research Methodology 2021;21(1) View
  4. Mao Y, Wang P, Wang X, Ye D. Global Public Interest and Seasonal Variations in Alzheimer's Disease: Evidence From Google Trends. Frontiers in Medicine 2021;8 View
  5. Ali W, Zuo W, Ali R, Zuo X, Rahman G. Causality Mining in Natural Languages Using Machine and Deep Learning Techniques: A Survey. Applied Sciences 2021;11(21):10064 View
  6. Purnama S, Susanna D, Achmadi U, Krianto T, Eryando T. Potential Development of Digital Environmental Surveillance System in Dengue Control: A Qualitative Study. Open Access Macedonian Journal of Medical Sciences 2021;9(E):1443 View
  7. Simonart T, Lam Hoai X, de Maertelaer V. Worldwide Evolution of Vaccinable and Nonvaccinable Viral Skin Infections: Google Trends Analysis. JMIR Dermatology 2022;5(4):e35034 View
  8. Simonart T, Lam Hoai X, De Maertelaer V. Epidemiologic evolution of common cutaneous infestations and arthropod bites: A Google Trends analysis. JAAD International 2021;5:69 View
  9. Mao Y, Duan Y, Guo Y, Wang X, Gao S, Ali G. A Study on the Prediction of House Price Index in First‐Tier Cities in China Based on Heterogeneous Integrated Learning Model. Journal of Mathematics 2022;2022(1) View
  10. Okunoye B, Ning S, Jemielniak D. Searching for HIV and AIDS Health Information in South Africa, 2004-2019: Analysis of Google and Wikipedia Search Trends. JMIR Formative Research 2022;6(3):e29819 View
  11. Sylvestre E, Joachim C, Cécilia-Joseph E, Bouzillé G, Campillo-Gimenez B, Cuggia M, Cabié A, Santos V. Data-driven methods for dengue prediction and surveillance using real-world and Big Data: A systematic review. PLOS Neglected Tropical Diseases 2022;16(1):e0010056 View
  12. Sylvestre E, Cécilia-Joseph E, Bouzillé G, Najioullah F, Etienne M, Malouines F, Rosine J, Julié S, Cabié A, Cuggia M. The Role of Heterogenous Real-world Data for Dengue Surveillance in Martinique: Observational Retrospective Study. JMIR Public Health and Surveillance 2022;8(12):e37122 View
  13. Hu T, Chow J, Chien T, Chou W. Detecting dengue fever in children using online Rasch analysis to develop algorithms for parents: An APP development and usability study. Medicine 2023;102(13):e33296 View
  14. Tuan D, Uyen P. Early prediction of the outbreak risk of dengue fever in Ba Ria-Vung Tau province, Vietnam: An analysis based on Google trends and statistical models. Infectious Disease Modelling 2025;10(3):743 View
  15. Chu A, Tsang J, Chan S, Chan L, So M. Utilizing Google Trends data to enhance forecasts and monitor long COVID prevalence. Communications Medicine 2025;5(1) View
  16. Singh G, Jha A, Aadil M. Analyzing the relationship between Google trends data and dengue outbreaks: a causality and correlation study. Discover Public Health 2025;22(1) View
  17. Yeh D, Leu J, Ye S, Cheng C. An intelligent autoregressive-distributed lag model: A climate-driven approach for predicting dengue fever incidence in Taiwan cities. Acta Tropica 2025;269:107761 View
  18. Sebayang A, Fakhruddin M, Khumaeroh M, Fahlena H, Tay C, Teh S, Sopaheluwakan A, Nuraini N, Soewono E. Unraveling the impact of human mobility on dengue outbreaks in Peninsular Malaysia: a Granger causality approach. International Journal of Environmental Health Research 2025:1 View
  19. Zhu Y, Hu X, Wang X, Li S, Wang P, Pan H. Seasonal pattern, global search trend, and public interest in arthritis from 2004 to 2022: an infodemiology study. Immunologic Research 2025;73(1) View

Books/Policy Documents

  1. Ecleo J, Galido A. Novel and Intelligent Digital Systems: Proceedings of the 4th International Conference (NiDS 2024). View