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Predictive Models for Decision Support in the COVID-19 Crisis | 1:a upplagan
- Danskt band, Engelska, 2020
- Författare: Simon James Fong, Joao Alexandre Lobo Marques, Francisco Nauber Bernardo Gois, José Xavier-Neto
- Betyg:
561
kr
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Beskrivning
COVID-19 has hit the world unprepared, as the deadliest pandemic of the century. Governments and authorities, as leaders and decision makers fighting the virus, enormously tap into the power of artificial intelligence and its predictive models for urgent decision support. This book showcases a collection of important predictive models that used during the pandemic, and discusses and compares their efficacy and limitations.
Readers from both healthcare industries and academia can gain unique insights on how predictive models were designed and applied on epidemic data. Taking COVID19 as a case study and showcasing the lessons learnt, this book will enable readers to be better prepared in the event of virus epidemics or pandemics in the future.
Readers from both healthcare industries and academia can gain unique insights on how predictive models were designed and applied on epidemic data. Taking COVID19 as a case study and showcasing the lessons learnt, this book will enable readers to be better prepared in the event of virus epidemics or pandemics in the future.
Om denna bok
Predictive Models for Decision Support in the COVID-19 Crisis av Simon James Fong, Joao Alexandre Lobo Marques, Francisco Nauber Bernardo Gois och José Xavier-Neto är en Danskt band bok med 98 sidor på Engelska. Detta är den 1:a upplagan som utgavs 2020 av Springer Nature.
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Produktinformation
Kategori:
Okänd
Bandtyp:
Danskt band
Språk:
Engelska
ISBN:
9783030619121
Upplaga:
1
Utgiven:
2020-12-01
Förlag:
Springer Nature
Sidantal:
98