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

Mahan Ghafari (), Oliver J. Watson, Ariel Karlinsky, Luca Ferretti and Aris Katzourakis ()
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Mahan Ghafari: University of Oxford
Oliver J. Watson: Imperial College London
Luca Ferretti: University of Oxford
Aris Katzourakis: University of Oxford

Nature Communications, 2022, vol. 13, issue 1, 1-9

Abstract: Abstract The transmission dynamics and burden of SARS-CoV-2 in many regions of the world is still largely unknown due to the scarcity of epidemiological analyses and lack of testing to assess the prevalence of disease. In this work, we develop a quantitative framework based on excess mortality data to reconstruct SARS-CoV-2 transmission dynamics and assess the level of underreporting in infections and deaths. Using weekly all-cause mortality data from Iran, we are able to show a strong agreement between our attack rate estimates and seroprevalence measurements in each province and find significant heterogeneity in the level of exposure across the country with 11 provinces reaching near 100% attack rates. Despite having a young population, our analysis reveals that incorporating limited access to medical services in our model, coupled with undercounting of COVID-19-related deaths, leads to estimates of infection fatality rate in most provinces of Iran that are comparable to high-income countries.

Date: 2022
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DOI: 10.1038/s41467-022-30711-y

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