IDEAS home Printed from https://ideas.repec.org/r/taf/jnlasa/v107y2012i500p1410-1426.html
   My bibliography  Save this item

Tracking Epidemics With Google Flu Trends Data and a State-Space SEIR Model

Citations

RePEc Biblio mentions

As found on the RePEc Biblio, the curated bibliography for Economics:
  1. > Economics of Welfare > Health Economics > Economics of Pandemics

Citations

Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
as


Cited by:

  1. Havranek, Tomas & Zeynalov, Ayaz, 2018. "Forecasting Tourist Arrivals with Google Trends and Mixed Frequency Data," EconStor Preprints 187420, ZBW - Leibniz Information Centre for Economics.
  2. M. Hubert & P. Rousseeuw & K. Vakili, 2014. "Shape bias of robust covariance estimators: an empirical study," Statistical Papers, Springer, vol. 55(1), pages 15-28, February.
  3. Taesik Lee & Hayong Shin, 2016. "Combining syndromic surveillance and ILI data using particle filter for epidemic state estimation," Flexible Services and Manufacturing Journal, Springer, vol. 28(1), pages 233-253, June.
  4. Beytía, Pablo & Infante, Carlos Cruz, 2020. "Digital Pathways, Pandemic Trajectories. Using Google Trends to Track Social Responses to COVID-19," SocArXiv yndb7, Center for Open Science.
  5. Mendolia, Silvia & Stavrunova, Olena & Yerokhin, Oleg, 2021. "Determinants of the community mobility during the COVID-19 epidemic: The role of government regulations and information," Journal of Economic Behavior & Organization, Elsevier, vol. 184(C), pages 199-231.
  6. Jianbin Tan & Ye Shen & Yang Ge & Leonardo Martinez & Hui Huang, 2023. "Age‐related model for estimating the symptomatic and asymptomatic transmissibility of COVID‐19 patients," Biometrics, The International Biometric Society, vol. 79(3), pages 2525-2536, September.
  7. Aouadi, Amal & Arouri, Mohamed & Roubaud, David, 2018. "Information demand and stock market liquidity: International evidence," Economic Modelling, Elsevier, vol. 70(C), pages 194-202.
  8. Paul Fearnhead & Vasilieos Giagos & Chris Sherlock, 2014. "Inference for reaction networks using the linear noise approximation," Biometrics, The International Biometric Society, vol. 70(2), pages 457-466, June.
  9. Tomas Havranek & Ayaz Zeynalov, 2021. "Forecasting tourist arrivals: Google Trends meets mixed-frequency data," Tourism Economics, , vol. 27(1), pages 129-148, February.
  10. Rongying Zhao & Xinlai Li & Zhisen Liang & Danyang Li, 2019. "Development strategy and collaboration preference in S&T of enterprises based on funded papers: a case study of Google," Scientometrics, Springer;Akadémiai Kiadó, vol. 121(1), pages 323-347, October.
  11. Szalkowski, Gabriel Andy & Mikalef, Patrick, 2023. "Understanding digital platform evolution using compartmental models," Technological Forecasting and Social Change, Elsevier, vol. 193(C).
  12. Wan Yang & Alicia Karspeck & Jeffrey Shaman, 2014. "Comparison of Filtering Methods for the Modeling and Retrospective Forecasting of Influenza Epidemics," PLOS Computational Biology, Public Library of Science, vol. 10(4), pages 1-15, April.
  13. Andrew Hoegh & Marco A. R. Ferreira & Scotland Leman, 2016. "Spatiotemporal model fusion: multiscale modelling of civil unrest," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 65(4), pages 529-545, August.
  14. Amir Hassan Zadeh & Hamed M. Zolbanin & Ramesh Sharda & Dursun Delen, 2019. "Social Media for Nowcasting Flu Activity: Spatio-Temporal Big Data Analysis," Information Systems Frontiers, Springer, vol. 21(4), pages 743-760, August.
  15. Christoph Zimmer & Reza Yaesoubi & Ted Cohen, 2017. "A Likelihood Approach for Real-Time Calibration of Stochastic Compartmental Epidemic Models," PLOS Computational Biology, Public Library of Science, vol. 13(1), pages 1-21, January.
  16. Baek, Changryong & Davis, Richard A. & Pipiras, Vladas, 2017. "Sparse seasonal and periodic vector autoregressive modeling," Computational Statistics & Data Analysis, Elsevier, vol. 106(C), pages 103-126.
  17. Feng, Jingxue & Wang, Liangliang, 2024. "A switching state-space transmission model for tracking epidemics and assessing interventions," Computational Statistics & Data Analysis, Elsevier, vol. 197(C).
  18. Jonathan Fintzi & Jon Wakefield & Vladimir N. Minin, 2022. "A linear noise approximation for stochastic epidemic models fit to partially observed incidence counts," Biometrics, The International Biometric Society, vol. 78(4), pages 1530-1541, December.
  19. Zhengming Xing & Bradley Nicholson & Monica Jimenez & Timothy Veldman & Lori Hudson & Joseph Lucas & David Dunson & Aimee K. Zaas & Christopher W. Woods & Geoffrey S. Ginsburg & Lawrence Carin, 2014. "Bayesian modeling of temporal properties of infectious disease in a college student population," Journal of Applied Statistics, Taylor & Francis Journals, vol. 41(6), pages 1358-1382, June.
  20. Tevfik Aktekin & Nicholas G. Polson & Refik Soyer, 2020. "A family of multivariate non‐gaussian time series models," Journal of Time Series Analysis, Wiley Blackwell, vol. 41(5), pages 691-721, September.
  21. Mostafa Abbas & Thomas B. Morland & Eric S. Hall & Yasser EL-Manzalawy, 2021. "Associations between Google Search Trends for Symptoms and COVID-19 Confirmed and Death Cases in the United States," IJERPH, MDPI, vol. 18(9), pages 1-24, April.
  22. Lu Tang & Yiwang Zhou & Lili Wang & Soumik Purkayastha & Leyao Zhang & Jie He & Fei Wang & Peter X.‐K. Song, 2020. "A Review of Multi‐Compartment Infectious Disease Models," International Statistical Review, International Statistical Institute, vol. 88(2), pages 462-513, August.
  23. Leigh Fisher & Jon Wakefield & Cici Bauer & Steve Self, 2017. "Time series modeling of pathogen-specific disease probabilities with subsampled data," Biometrics, The International Biometric Society, vol. 73(1), pages 283-293, March.
  24. Zeynalov, Ayaz, 2014. "Nowcasting Tourist Arrivals to Prague: Google Econometrics," MPRA Paper 60945, University Library of Munich, Germany.
  25. Zeynalov, Ayaz, 2017. "Forecasting Tourist Arrivals in Prague: Google Econometrics," MPRA Paper 83268, University Library of Munich, Germany.
  26. Lili Zhuang & Noel Cressie, 2014. "Bayesian hierarchical statistical SIRS models," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 23(4), pages 601-646, November.
  27. David J Albers & Matthew Levine & Bruce Gluckman & Henry Ginsberg & George Hripcsak & Lena Mamykina, 2017. "Personalized glucose forecasting for type 2 diabetes using data assimilation," PLOS Computational Biology, Public Library of Science, vol. 13(4), pages 1-38, April.
IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.