Published on in Vol 19, No 11 (2017): November

Subregional Nowcasts of Seasonal Influenza Using Search Trends

Subregional Nowcasts of Seasonal Influenza Using Search Trends

Subregional Nowcasts of Seasonal Influenza Using Search Trends

Authors of this article:

Sasikiran Kandula1 Author Orcid Image ;   Daniel Hsu2 Author Orcid Image ;   Jeffrey Shaman1 Author Orcid Image


  1. Kim M, Yune S, Chang S, Jung Y, Sa S, Han H. The Fever Coach Mobile App for Participatory Influenza Surveillance in Children: Usability Study. JMIR mHealth and uHealth 2019;7(10):e14276 View
  2. Kandula S, Shaman J. Near-term forecasts of influenza-like illness. Epidemics 2019;27:41 View
  3. Osthus D, Daughton A, Priedhorsky R, Broniatowski D. Even a good influenza forecasting model can benefit from internet-based nowcasts, but those benefits are limited. PLOS Computational Biology 2019;15(2):e1006599 View
  4. Kandula S, Shaman J, Segata N. Reappraising the utility of Google Flu Trends. PLOS Computational Biology 2019;15(8):e1007258 View
  5. Barros J, Duggan J, Rebholz-Schuhmann D. The Application of Internet-Based Sources for Public Health Surveillance (Infoveillance): Systematic Review. Journal of Medical Internet Research 2020;22(3):e13680 View
  6. Kandula S, Pei S, Shaman J. Improved forecasts of influenza-associated hospitalization rates with Google Search Trends. Journal of The Royal Society Interface 2019;16(155):20190080 View
  7. Reich N, McGowan C, Yamana T, Tushar A, Ray E, Osthus D, Kandula S, Brooks L, Crawford-Crudell W, Gibson G, Moore E, Silva R, Biggerstaff M, Johansson M, Rosenfeld R, Shaman J, Pitzer V. Accuracy of real-time multi-model ensemble forecasts for seasonal influenza in the U.S.. PLOS Computational Biology 2019;15(11):e1007486 View
  8. Lutz C, Huynh M, Schroeder M, Anyatonwu S, Dahlgren F, Danyluk G, Fernandez D, Greene S, Kipshidze N, Liu L, Mgbere O, McHugh L, Myers J, Siniscalchi A, Sullivan A, West N, Johansson M, Biggerstaff M. Applying infectious disease forecasting to public health: a path forward using influenza forecasting examples. BMC Public Health 2019;19(1) View
  9. Kandula S, Yamana T, Pei S, Yang W, Morita H, Shaman J. Evaluation of mechanistic and statistical methods in forecasting influenza-like illness. Journal of The Royal Society Interface 2018;15(144):20180174 View
  10. Zepecki A, Guendelman S, DeNero J, Prata N. Using Application Programming Interfaces to Access Google Data for Health Research: Protocol for a Methodological Framework. JMIR Research Protocols 2020;9(7):e16543 View
  11. Lu F, Hattab M, Clemente C, Biggerstaff M, Santillana M. Improved state-level influenza nowcasting in the United States leveraging Internet-based data and network approaches. Nature Communications 2019;10(1) View
  12. Mavragani A. Infodemiology and Infoveillance: Scoping Review. Journal of Medical Internet Research 2020;22(4):e16206 View
  13. Mavragani A, Ochoa G. Google Trends in Infodemiology and Infoveillance: Methodology Framework. JMIR Public Health and Surveillance 2019;5(2):e13439 View
  14. Yan Y, Jebara T, Abernathey R, Goes J, Gomes H, Añel J. Robust learning algorithms for capturing oceanic dynamics and transport of Noctiluca blooms using linear dynamical models. PLOS ONE 2019;14(6):e0218183 View
  15. Kolff C, Scott V, Stockwell M. The use of technology to promote vaccination: A social ecological model based framework. Human Vaccines & Immunotherapeutics 2018;14(7):1636 View
  16. Talaei-Khoei A, Wilson J, Kazemi S. Period of Measurement in Time-Series Predictions of Disease Counts from 2007 to 2017 in Northern Nevada: Analytics Experiment. JMIR Public Health and Surveillance 2019;5(1):e11357 View
  17. Pei S, Shaman J, Del Valle S. Aggregating forecasts of multiple respiratory pathogens supports more accurate forecasting of influenza-like illness. PLOS Computational Biology 2020;16(10):e1008301 View
  18. Miliou I, Xiong X, Rinzivillo S, Zhang Q, Rossetti G, Giannotti F, Pedreschi D, Vespignani A, Viboud C. Predicting seasonal influenza using supermarket retail records. PLOS Computational Biology 2021;17(7):e1009087 View
  19. GÜNALAN E, ÇONAK Ö. CAN ONLINE DIETITIAN BE A NOVEL TREND OF POST-PANDEMIC ERA IN TURKEY?. Acibadem Universitesi Saglik Bilimleri Dergisi 2022;13(3) View
  20. Lin C, Yousefi S, Kahoro E, Karisani P, Liang D, Sarnat J, Agichtein E. Detecting Elevated Air Pollution Levels by Monitoring Web Search Queries: Algorithm Development and Validation. JMIR Formative Research 2022;6(12):e23422 View
  21. Reich N, Brooks L, Fox S, Kandula S, McGowan C, Moore E, Osthus D, Ray E, Tushar A, Yamana T, Biggerstaff M, Johansson M, Rosenfeld R, Shaman J. A collaborative multiyear, multimodel assessment of seasonal influenza forecasting in the United States. Proceedings of the National Academy of Sciences 2019;116(8):3146 View
  22. Jing F, Li Z, Qiao S, Zhang J, Olatosi B, Li X. Using geospatial social media data for infectious disease studies: a systematic review. International Journal of Digital Earth 2023;16(1):130 View
  23. Jung S, Moon J, Park S, Hwang E. Self-Attention-Based Deep Learning Network for Regional Influenza Forecasting. IEEE Journal of Biomedical and Health Informatics 2022;26(2):922 View
  24. Kandula S, Olfson M, Gould M, Keyes K, Shaman J, Zhu L. Hindcasts and forecasts of suicide mortality in US: A modeling study. PLOS Computational Biology 2023;19(3):e1010945 View

Books/Policy Documents

  1. Spitzberg B, Tsou M, Jung C. The Handbook of Applied Communication Research. View
  2. Samaras L, García-Barriocanal E, Sicilia M. Innovation in Health Informatics. View
  3. Adiga A, Lewis B, Levin S, Marathe M, Poor H, Ravi S, Rosenkrantz D, Stearns R, Venkatramanan S, Vullikanti A, Wang L. Artificial Intelligence in Covid-19. View