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Published on in Vol 10 (2024)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/54340, first published .
Influenza virus particle with surface glycoproteins

Responding to the Return of Influenza in the United States by Applying Centers for Disease Control and Prevention Surveillance, Analysis, and Modeling to Inform Understanding of Seasonal Influenza

Responding to the Return of Influenza in the United States by Applying Centers for Disease Control and Prevention Surveillance, Analysis, and Modeling to Inform Understanding of Seasonal Influenza

Journals

  1. Mathis S, Webber A, León T, Murray E, Sun M, White L, Brooks L, Green A, Hu A, Rosenfeld R, Shemetov D, Tibshirani R, McDonald D, Kandula S, Pei S, Yaari R, Yamana T, Shaman J, Agarwal P, Balusu S, Gururajan G, Kamarthi H, Prakash B, Raman R, Zhao Z, Rodríguez A, Meiyappan A, Omar S, Baccam P, Gurung H, Suchoski B, Stage S, Ajelli M, Kummer A, Litvinova M, Ventura P, Wadsworth S, Niemi J, Carcelen E, Hill A, Loo S, McKee C, Sato K, Smith C, Truelove S, Jung S, Lemaitre J, Lessler J, McAndrew T, Ye W, Bosse N, Hlavacek W, Lin Y, Mallela A, Gibson G, Chen Y, Lamm S, Lee J, Posner R, Perofsky A, Viboud C, Clemente L, Lu F, Meyer A, Santillana M, Chinazzi M, Davis J, Mu K, Pastore y Piontti A, Vespignani A, Xiong X, Ben-Nun M, Riley P, Turtle J, Hulme-Lowe C, Jessa S, Nagraj V, Turner S, Williams D, Basu A, Drake J, Fox S, Suez E, Cojocaru M, Thommes E, Cramer E, Gerding A, Stark A, Ray E, Reich N, Shandross L, Wattanachit N, Wang Y, Zorn M, Aawar M, Srivastava A, Meyers L, Adiga A, Hurt B, Kaur G, Lewis B, Marathe M, Venkatramanan S, Butler P, Farabow A, Ramakrishnan N, Muralidhar N, Reed C, Biggerstaff M, Borchering R. Evaluation of FluSight influenza forecasting in the 2021–22 and 2022–23 seasons with a new target laboratory-confirmed influenza hospitalizations. Nature Communications 2024;15(1) View
  2. Li P, Huang R, Xie R, Xie J, Liu K. Prediction of influenza-like illness incidence using meteorological factors in Kunming : deep learning model study. BMC Public Health 2025;25(1) View
  3. Frutos A, Moon S, Binder A, Cool A, Iyawe K, Thompson T, Dudeck M, Bozio C, Tenforde M, Biggerstaff M, Sumner K. Evaluation of a Novel Data Source for National Influenza Surveillance: Influenza Hospitalization Data in the National Healthcare Safety Network, United States, September 2021–April 2024. American Journal of Public Health 2025:e1 View
  4. Hamzat H, Ezeala O, Durham S, Qian J, Westrick S. Determinants of Influenza Vaccine Uptake Among Rural Populations in a Southeastern U.S. State. Vaccines 2025;13(12):1208 View
  5. Wang Y, Zhai S, Wu C, Chen B, Zhang W, Li J, Cheng X, Xiao L. Dynamic ensemble deep learning with multi-source data for robust influenza forecasting in Yangzhou. BMC Public Health 2025;26(1) View
  6. Ma Y, Li W, Zhang M, Zhou J, Wang Y, Li N. ICD-11 in global public health surveillance: Advancements in infectious disease tracking, non-communicable disease management, and antimicrobial resistance monitoring. Informatics and Health 2026;3(1):19 View
  7. White L, León T, Scarpino S. Forecastability of infectious disease time series: are some seasons and pathogens intrinsically more difficult to forecast?. PLOS Computational Biology 2026;22(4):e1014175 View
  8. Gao X, Qin P, Qi X, Wan Y, Xu Z, Hu H. Suppression and resurgence: the evolving epidemiology of seasonal influenza from 2015 to 2024 in a core urban district of Beijing, China. Frontiers in Public Health 2026;14 View