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Results from the centers for disease control and prevention’s predict the 2013–2014 Influenza Season Challenge

CITE Title : Results from the centers for disease control and prevention’s predict the 2013–2014 Influenza Season Challenge Personal Author(s) : Biggerstaff, Matthew;Alper, David;Dredze, Mark;Fox, Spencer;Fung, Isaac Chun-Hai;Hickmann, Kyle S.;Lewis, Bryan;Rosenfeld, Roni;Shaman, Jeffrey;Tsou, Ming-Hsiang;Velardi, Paola;Vespignani, Alessandro;Finelli, Lyn; Corporate Authors(s) : for the Influenza Forecasting Contest Working Group Published Date : Jul 22 2016 Source : BMC Infect Dis. 16. URL : https://stacks.cdc.gov/view/cdc/41501 Biggerstaff, Matthew et al. (2016). Results from the centers for disease control and prevention’s predict the 2013–2014 Influenza Season Challenge. 16 Biggerstaff, Matthew et al. "Results from the centers for disease control and prevention’s predict the 2013–2014 Influenza Season Challenge" 16 (2016) Biggerstaff, Matthew et al. "Results from the centers for disease control and prevention’s predict the 2013–2014 Influenza Season Challenge" vol. 16, 2016 Export RIS Citation Information.

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Biggerstaff, Matthew ;

Alper, David

Jul 22 2016 | BMC Infect Dis. 16.

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BackgroundEarly insights into the timing of the start, peak, and intensity of the influenza season could be useful in planning influenza prevention an...
Data-driven mechanistic framework with stratified immunity and effective transmissibility for COVID-19 scenario projections

CITE Title : Data-driven mechanistic framework with stratified immunity and effective transmissibility for COVID-19 scenario projections Personal Author(s) : Porebski, Przemyslaw;Venkatramanan, Srinivasan;Adiga, Aniruddha;Klahn, Brian;Hurt, Benjamin;Wilson, Mandy L.;Chen, Jiangzhuo;Vullikanti, Anil;Marathe, Madhav;Lewis, Bryan; Published Date : 6-2024 Source : Epidemics. 47:100761 URL : https://stacks.cdc.gov/view/cdc/158166 Porebski, Przemyslaw et al. (2024). Data-driven mechanistic framework with stratified immunity and effective transmissibility for COVID-19 scenario projections. 47 Porebski, Przemyslaw et al. "Data-driven mechanistic framework with stratified immunity and effective transmissibility for COVID-19 scenario projections" 47 (2024) Porebski, Przemyslaw et al. "Data-driven mechanistic framework with stratified immunity and effective transmissibility for COVID-19 scenario projections" vol. 47, 2024 Export RIS Citation Information.

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Porebski, Przemyslaw ;

Venkatramanan, Srinivasan

6-2024 | Epidemics. 47:100761

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Scenario-based modeling frameworks have been widely used to support policy-making at state and federal levels in the United States during the COVID-19...
Modeling of Future COVID-19 Cases, Hospitalizations, and Deaths, by Vaccination Rates and Nonpharmaceutical Intervention Scenarios — United States, April–September 2021

CITE Title : Modeling of Future COVID-19 Cases, Hospitalizations, and Deaths, by Vaccination Rates and Nonpharmaceutical Intervention Scenarios — United States, April–September 2021 Personal Author(s) : Borchering, Rebecca K.;Viboud, Cécile;Howerton, Emily;Smith, Claire P.;Truelove, Shaun;Runge, Michael C.;Reich, Nicholas G.;Contamin, Lucie;Levander, John;Salerno, Jessica;van Panhuis, Wilbert;Matt Kinsey, PhD;Tallaksen, Kate;Obrecht, R. Freddy;Asher, Laura;Costello, Cash;Kelbaugh, Michael;Wilson, Shelby;Shin, Lauren;Gallagher, Molly E.;Mullany, Luke C.;Rainwater-Lovett, Kaitlin;Lemaitre, Joseph C.;Dent, Juan;Grantz, Kyra H.;Kaminsky, Joshua;Lauer, Stephen A.;Lee, Elizabeth C.;Meredith, Hannah R.;Perez-Saez, Javier;Keegan, Lindsay T.;Karlen, Dean;Chinazzi, Matteo;Davis, Jessica T.;Mu, Kunpeng;Xiong, Xinyue;Pastore y Piontti, Ana;Vespignani, Alessandro;Srivastava, Ajitesh;Porebski, Przemyslaw;Venkatramanan, Srinivasan;Adiga, Aniruddha;Lewis, Bryan;Klahn, Brian;Outten, Joseph;Schlitt, James;Corbett, Patrick;Telionis, Pyrros Alexander;Wang, Lijing;Peddireddy, Akhil Sai;Hurt, Benjamin;Chen, Jiangzhuo;Vullikanti, Anil;Marathe, Madhav;Healy, Jessica M.;Slayton, Rachel B.;Biggerstaff, Matthew;Johansson, Michael A.;Shea, Katriona;Lessler, Justin; Corporate Authors(s) : Pennsylvania State University.;John E. Fogarty International Center for Advanced Study in the Health Sciences.;Johns Hopkins Bloomberg School of Public Health.;Geological Survey (U.S.);University of Massachusetts at Amherst.;University of Pittsburgh, Pittsburgh.;Johns Hopkins University Applied Physics Laboratory.;École polytechnique fédérale de Lausanne.;University of Utah.;University of Victoria (B.C.);Northeastern University (Boston, Mass);University of Southern California.;University of Virginia.;CDC COVID-19 Response Team.; Published Date : May 5, 2021 Pages in Document : 6 pdf pages Series : Weekly Report: Morbidity and Mortality Weekly Report (MMWR) Source : MMWR: Morbidity and Mortality Weekly Report 2021; v. 70 Early Release URL : https://stacks.cdc.gov/view/cdc/105827 Borchering, Rebecca K. et al. (2021). Modeling of Future COVID-19 Cases, Hospitalizations, and Deaths, by Vaccination Rates and Nonpharmaceutical Intervention Scenarios — United States, April–September 2021. 70 Borchering, Rebecca K. et al. "Modeling of Future COVID-19 Cases, Hospitalizations, and Deaths, by Vaccination Rates and Nonpharmaceutical Intervention Scenarios — United States, April–September 2021" 70 (2021) Borchering, Rebecca K. et al. "Modeling of Future COVID-19 Cases, Hospitalizations, and Deaths, by Vaccination Rates and Nonpharmaceutical Intervention Scenarios — United States, April–September 2021" vol. 70, 2021 Export RIS Citation Information.

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Borchering, Rebecca K. ;

Viboud, Cécile

May 5, 2021 | Weekly Report: Morbidity and Mortality Weekly Report (MMWR)

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What is already known about this topic? Increases in COVID-19 cases in March and early April occurred despite a large-scale vaccination program. Incre...