Estimating COVID-19 mortality in Italy early in the COVID-19 pandemic

Nature Communications - Tập 12 Số 1
Chirag Modi1, Vanessa Böhm1, Simone Ferraro1, George Stein1, Uroš Seljak2
1Berkeley Center for Cosmological Physics, Department of Physics, University of California, Berkeley, CA, USA
2Lawrence Berkeley National Laboratory, One Cyclotron Road, Berkeley, CA, USA

Tóm tắt

AbstractEstimating rates of COVID-19 infection and associated mortality is challenging due to uncertainties in case ascertainment. We perform a counterfactual time series analysis on overall mortality data from towns in Italy, comparing the population mortality in 2020 with previous years, to estimate mortality from COVID-19. We find that the number of COVID-19 deaths in Italy in 2020 until September 9 was 59,000–62,000, compared to the official number of 36,000. The proportion of the population that died was 0.29% in the most affected region, Lombardia, and 0.57% in the most affected province, Bergamo. Combining reported test positive rates from Italy with estimates of infection fatality rates from the Diamond Princess cruise ship, we estimate the infection rate as 29% (95% confidence interval 15–52%) in Lombardy, and 72% (95% confidence interval 36–100%) in Bergamo.

Từ khóa


Tài liệu tham khảo

Johns Hopkins Coronavirus Resource Center: COVID-19 Map, https://coronavirus.jhu.edu/map.html (2020).

De Natale, G. et al. The covid-19 infection in italy: a statistical study of an abnormally severe disease. medRxiv https://doi.org/10.1101/2020.03.28.20046243 (2020).

Hauser, A. et al. Estimation of sars-cov-2 mortality during the early stages of an epidemic: a modelling study in hubei, china and northern italy. medRxiv https://doi.org/10.1101/2020.03.04.20031104 (2020).

Buonanno, P., Galletta, S. & Puca, M. Estimating the severity of covid-19: evidence from the italian epicenter. SSRN https://doi.org/10.2139/ssrn.3567093 (2020).

Verity, e. a., Robert. Estimates of the severity of coronavirus disease 2019: a model-based analysis. Lancet Infect. Dis. https://doi.org/10.1016/S1473-3099(20)30243-7 (2020).

Russell, T. W. et al. Estimating the infection and case fatality ratio for coronavirus disease (covid-19) using age-adjusted data from the outbreak on the diamond princess cruise ship, February 2020. Euro Surveill. 25, 2000256 (2020).

Song, P. X. et al. An epidemiological forecast model and software assessing interventions on covid-19 epidemic in china. medRxiv https://doi.org/10.1101/2020.02.29.20029421 (2020).

Wu, J. T. et al. Estimating clinical severity of covid-19 from the transmission dynamics in wuhan, china. Nat. Med. https://doi.org/10.1038/s41591-020-0822-7 (2020).

Spiegelhalter, David. https://medium.com/wintoncentre/why-i-am-in-a-higher-priority-group-for-a-vaccine-than-younger-people-with-chronic-health-974621eec30 (2020).

Centers for Disease Control and Prevention. https://covid.cdc.gov/covid-data-tracker/#serology-surveillance.

New York State Department of Health. https://www.health.ny.gov/statistics/vital_statistics/.

Weinberger, D. et al. Estimating the early death toll of covid-19 in the united states. medRxiv https://doi.org/10.1101/2020.04.15.20066431 (2020).

NYC-DOHMH COVID-19 Response Team. Preliminary estimate of excess mortality during the covid-19 outbreak - new york city, March 11–May 2, 2020. MMWR 69, https://doi.org/10.15585/mmwr.mm6919e5 (2020).

Euromomo. https://www.euromomo.eu/graphs-and-maps/.

Johns Hopkins University & Medicine. https://coronavirus.jhu.edu/map.html.

The World Bank. https://data.worldbank.org/indicator/SP.DYN.CDRT.IN?locations=IS.

Streeck, H. et al. Infection fatality rate of sars-cov-2 infection in a german community with a super-spreading event. medRxiv https://doi.org/10.1101/2020.05.04.20090076 (2020).

Sergey, B., Krasnikov, N. & Taperechkina, V. Confidence intervals for poisson distribution parameter. https://arxiv.org/abs/hep-ex/0108020v1 (2001).

Presidenza del Consiglio dei Ministri. https://github.com/pcm-dpc/COVID-19.

Abadie, A., Diamond, A. & Hainmueller, J. Synthetic control methods for comparative case studies: estimating the effect of california’s tobacco control program. J. Am. Stat. Assoc. 105, 493–505 (2010).

Modi, C. & Seljak, U. Generative learning of counterfactual for synthetic control applications in econometrics. https://arxiv.org/abs/1910.07178 (2019).

Modi, C. https://github.com/bccp/covid-19-data (2020).

Modi, C. https://zenodo.org/record/4619864 (2021).