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

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