Predicting the severity of disease progression in COVID-19 at the individual and population level: A mathematical model.

Chirmule, Narendra; Nair, Pradip; Desai, Bela; Khare, Ravindra; Nerurkar, Vivek; Gaur, Amitabh

Chirmule, Narendra; Nair, Pradip; Desai, Bela; Khare, Ravindra; Nerurkar, Vivek; Gaur, Amitabh.

Afiliación

Chirmule N; SymphonyTech Biologics, Philadelphia, Pennsylvania, USA.
Nair P; Biocon, Bangalore, Karnataka, India.
Desai B; NanoCellect Biomedical, Inc., San Diego, California, USA.
Khare R; SymphonyTech Biologics, Philadelphia, Pennsylvania, USA.
Nerurkar V; Department of Tropical Medicine, Medical Microbiology and Pharmacology, John A. Burns School of Medicine, University of Hawaii at Manoa, Honolulu, Hawaii, USA.
Gaur A; Innovative Assay Solutions LLC, San Diego, California, USA.

medRxiv ; 2021 Apr 07.

Article en En | MEDLINE | ID: mdl-33851191

Palabras clave

cytokine-storm; innate; interferon; lymphopenia; modeling; neutralizing-antibodies; prediction; viral factors

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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Prognostic_studies / Risk_factors_studies Idioma: En Revista: MedRxiv Año: 2021 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Estados Unidos

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