Augmenting laboratory COVID serology data granularity for SARS-CoV-2 reporting. | Zendy

Esmond Urwin | Zendy; A. I. Harris | Zendy; Jenny Johnstone | Zendy; Erum Masood | Zendy; Antony Chuter | Zendy; Michael A. J. Ferguson | Zendy; Joanne E. Martin | Zendy; Neil J. Sebire | Zendy; Philip Quinlan | Zendy; Emily Jefferson | Zendy

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Augmenting laboratory COVID serology data granularity for SARS-CoV-2 reporting.

Author(s) -

Esmond Urwin,

A. I. Harris,

Jenny Johnstone,

Erum Masood,

Antony Chuter,

Michael A. J. Ferguson,

Joanne E. Martin,

Neil J. Sebire,

Philip Quinlan,

Emily Jefferson

Publication year - 2022

Publication title -

international journal for population data science

Language(s) - Uncategorized

Resource type - Journals

SCImago Journal Rank - 0.602

H-Index - 7

ISSN - 2399-4908

DOI - 10.23889/ijpds.v7i3.1887

Subject(s) - computer science , interoperability , covid-19 , analyser , serology , identifier , data set , data science , set (abstract data type) , pandemic , test (biology) , data mining , snowball sampling , medicine , artificial intelligence , world wide web , pathology , immunology , disease , infectious disease (medical specialty) , paleontology , chemistry , chromatography , antibody , biology , programming language

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