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

Infodemiology and Infoveillance (1349) Infectious Diseases (non-STD/STI) (1575) Infoveillance, Infodemiology, Digital Disease Surveillance, Infodemic Management (1041)

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 (14) 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

Books/Policy Documents

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