A framework for reconstructing SARS-CoV-2 transmission dynamics using excess mortality data

Mahan Ghafari; Oliver J. Watson; Ariel Karlinsky; Luca Ferretti; Aris Katzourakis

doi:10.1038/s41467-022-30711-y

Nature Communications (May 2022)

A framework for reconstructing SARS-CoV-2 transmission dynamics using excess mortality data

Mahan Ghafari,
Oliver J. Watson,
Ariel Karlinsky,
Luca Ferretti,
Aris Katzourakis

Affiliations

Mahan Ghafari: Department of Zoology, University of Oxford
Oliver J. Watson: Department of Infectious Disease Epidemiology, Imperial College London
Ariel Karlinsky: Department of Economics, Hebrew University of Jerusalem
Luca Ferretti: Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Medicine, University of Oxford
Aris Katzourakis: Department of Zoology, University of Oxford

DOI: https://doi.org/10.1038/s41467-022-30711-y
Journal volume & issue: Vol. 13, no. 1
pp. 1 – 9

Abstract

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Testing capacity continues to limit detection of COVID-19 infections and impacts reliability of mortality estimates. Here, the authors develop a statistical model to estimate COVID-19 attributable deaths using all-cause mortality data from Iran and estimate that around half of these deaths have been reported.

Published in Nature Communications

ISSN: 2041-1723 (Online)
Publisher: Nature Portfolio
Country of publisher: United Kingdom
LCC subjects: Science
Website: https://www.nature.com/ncomms/

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