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

Infoveillance, Infodemiology, Digital Disease Surveillance, Infodemic Management (1041) JMIR Theme Issue: COVID-19 Special Issue (2452) Infodemiology and Infoveillance (1349) Depression and Mood Disorders; Suicide Prevention (1363) Surveillance Systems (462) Innovative Methods in Public Health and Surveillance (332) Methods and New Tools in Mental Health Research (454) JMH Theme Issue: COVID-19 and Mental Health: Impact and Interventions (383) Statistical Methods for Surveillance and Population Health (156)

Published on in Vol 7, No 12 (2021): December

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/34016, first published .
Predicting the Number of Suicides in Japan Using Internet Search Queries: Vector Autoregression Time Series Model

Predicting the Number of Suicides in Japan Using Internet Search Queries: Vector Autoregression Time Series Model

Predicting the Number of Suicides in Japan Using Internet Search Queries: Vector Autoregression Time Series Model

Authors of this article:

Kazuya Taira1 Author Orcid Image ;   Rikuya Hosokawa1 Author Orcid Image ;   Tomoya Itatani2 Author Orcid Image ;   Sumio Fujita3 Author Orcid Image
Article Authors Cited by (7) Tweetations (7) Metrics

Journals

  1. Niu Q, Liu J, Zhao Z, Onishi M, Kawaguchi A, Bandara A, Harada K, Aoyama T, Nagai-Tanima M. Explanation of hand, foot, and mouth disease cases in Japan using Google Trends before and during the COVID-19: infodemiology study. BMC Infectious Diseases 2022;22(1) View
  2. Matsumoto R, Kawano Y, Motomura E, Shiroyama T, Okada M. Analyzing the changing relationship between personal consumption and suicide mortality during COVID-19 pandemic in Japan, using governmental and personal consumption transaction databases. Frontiers in Public Health 2022;10 View
  3. Perez C, Karmakar S. An NLP-assisted Bayesian time-series analysis for prevalence of Twitter cyberbullying during the COVID-19 pandemic. Social Network Analysis and Mining 2023;13(1) View
  4. Wang S, Ning H, Huang X, Xiao Y, Zhang M, Yang E, Sadahiro Y, Liu Y, Li Z, Hu T, Fu X, Li Z, Zeng Y. Public Surveillance of Social Media for Suicide Using Advanced Deep Learning Models in Japan: Time Series Study From 2012 to 2022. Journal of Medical Internet Research 2023;25:e47225 View
  5. Anjali , B R. Exploring cause-specific strategies for suicide prevention in India: A multivariate VARMA approach. Asian Journal of Psychiatry 2024;92:103871 View
  6. Arai T, Tsubaki H, Wakano A, Shimizu Y. Association Between School-Related Google Trends Search Volume and Suicides Among Children and Adolescents in Japan During 2016-2020: Retrospective Observational Study With a Time-Series Analysis. Journal of Medical Internet Research 2024;26:e51710 View