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Real-Time Infectious Disease Modeling to Inform Emergency Public Health Decision Making.
Bershteyn, Anna; Kim, Hae-Young; Braithwaite, R Scott.
Afiliación
  • Bershteyn A; New York University Grossman School of Medicine, New York, NY, USA; email: Anna.Bershteyn@nyulangone.org.
  • Kim HY; New York University Grossman School of Medicine, New York, NY, USA; email: Anna.Bershteyn@nyulangone.org.
  • Braithwaite RS; New York University Grossman School of Medicine, New York, NY, USA; email: Anna.Bershteyn@nyulangone.org.
Annu Rev Public Health ; 43: 397-418, 2022 04 05.
Article en En | MEDLINE | ID: mdl-34995131
ABSTRACT
Infectious disease transmission is a nonlinear process with complex, sometimes unintuitive dynamics. Modeling can transform information about a disease process and its parameters into quantitative projections that help decision makers compare public health response options. However, modelers face methodologic challenges, data challenges, and communication challenges, which are exacerbated under the time constraints of a public health emergency. We review methods, applications, challenges and opportunities for real-time infectious disease modeling during public health emergencies, with examples drawn from the two deadliest pandemics in recent history HIV/AIDS and coronavirus disease 2019 (COVID-19).
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Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Enfermedades Transmisibles / COVID-19 Tipo de estudio: Prognostic_studies Límite: Humans Idioma: En Revista: Annu Rev Public Health Año: 2022 Tipo del documento: Article

Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Enfermedades Transmisibles / COVID-19 Tipo de estudio: Prognostic_studies Límite: Humans Idioma: En Revista: Annu Rev Public Health Año: 2022 Tipo del documento: Article