Estimation of SARS-CoV-2 mortality during the early stages of an epidemic: A modeling study in Hubei, China, and six regions in Europe

English version

Autoři: Anthony Hauser ^aff001; Michel J. Counotte ^aff001; Charles C. Margossian ^aff002; Garyfallos Konstantinoudis ^aff003; Nicola Low ^aff001; Christian L. Althaus ^aff001; Julien Riou ^aff001
Působiště autorů: Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland ^aff001; Department of Statistics, Columbia University, New York, New York, United States of America ^aff002; MRC Centre for Environment and Health, Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, United Kingdom ^aff003; Division of infectious diseases, Federal Office of Public Health, Bern, Switzerland ^aff004
Vyšlo v časopise: Estimation of SARS-CoV-2 mortality during the early stages of an epidemic: A modeling study in Hubei, China, and six regions in Europe. PLoS Med 17(7): e32767. doi:10.1371/journal.pmed.1003189
Kategorie: Research Article
doi: https://doi.org/10.1371/journal.pmed.1003189

Souhrn

Background

As of 16 May 2020, more than 4.5 million cases and more than 300,000 deaths from disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) have been reported. Reliable estimates of mortality from SARS-CoV-2 infection are essential for understanding clinical prognosis, planning healthcare capacity, and epidemic forecasting. The case–fatality ratio (CFR), calculated from total numbers of reported cases and reported deaths, is the most commonly reported metric, but it can be a misleading measure of overall mortality. The objectives of this study were to (1) simulate the transmission dynamics of SARS-CoV-2 using publicly available surveillance data and (2) infer estimates of SARS-CoV-2 mortality adjusted for biases and examine the CFR, the symptomatic case–fatality ratio (sCFR), and the infection–fatality ratio (IFR) in different geographic locations.

Method and findings

We developed an age-stratified susceptible-exposed-infected-removed (SEIR) compartmental model describing the dynamics of transmission and mortality during the SARS-CoV-2 epidemic. Our model accounts for two biases: preferential ascertainment of severe cases and right-censoring of mortality. We fitted the transmission model to surveillance data from Hubei Province, China, and applied the same model to six regions in Europe: Austria, Bavaria (Germany), Baden-Württemberg (Germany), Lombardy (Italy), Spain, and Switzerland. In Hubei, the baseline estimates were as follows: CFR 2.4% (95% credible interval [CrI] 2.1%–2.8%), sCFR 3.7% (3.2%–4.2%), and IFR 2.9% (2.4%–3.5%). Estimated measures of mortality changed over time. Across the six locations in Europe, estimates of CFR varied widely. Estimates of sCFR and IFR, adjusted for bias, were more similar to each other but still showed some degree of heterogeneity. Estimates of IFR ranged from 0.5% (95% CrI 0.4%–0.6%) in Switzerland to 1.4% (1.1%–1.6%) in Lombardy, Italy. In all locations, mortality increased with age. Among individuals 80 years or older, estimates of the IFR suggest that the proportion of all those infected with SARS-CoV-2 who will die ranges from 20% (95% CrI 16%–26%) in Switzerland to 34% (95% CrI 28%–40%) in Spain. A limitation of the model is that count data by date of onset are required, and these are not available in all countries.

Conclusions

We propose a comprehensive solution to the estimation of SARS-Cov-2 mortality from surveillance data during outbreaks. The CFR is not a good predictor of overall mortality from SARS-CoV-2 and should not be used for evaluation of policy or comparison across settings. Geographic differences in IFR suggest that a single IFR should not be applied to all settings to estimate the total size of the SARS-CoV-2 epidemic in different countries. The sCFR and IFR, adjusted for right-censoring and preferential ascertainment of severe cases, are measures that can be used to improve and monitor clinical and public health strategies to reduce the deaths from SARS-CoV-2 infection.

Klíčová slova:

Age groups – Death rates – Europe – Germany – China – Respiratory infections – Switzerland – SARS CoV 2

Zdroje

1. World Health Organization [Internet]. Coronavirus disease 2019 (COVID-19) Situation Report 117. Geneva, Switzerland: World Health Organization; 2020 [cited 2020 May 17]. Available from: https://www.who.int/docs/default-source/coronaviruse/situation-reports/20200516-covid-19-sitrep-117.pdf?sfvrsn=8f562cc_2.

2. Wang C, Horby PW, Hayden FG, Gao GF. A novel coronavirus outbreak of global health concern. The Lancet. 2020;395(10223):470–473.

3. Huang C, Wang Y, Li X, Ren L, Zhao J, Hu Y, et al. Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. The Lancet. 2020;395(10223):497–506.

4. Riou J, Althaus CL. Pattern of early human-to-human transmission of Wuhan 2019 novel coronavirus (2019-nCoV), December 2019 to January 2020. Eurosurveillance. 2020;25(4).

5. World Health Organization [Internet]. WHO Director-General's opening remarks at the media briefing on COVID-19–3 March 2020. Geneva, Switzerland: World Health Organization; 2020 [cited 2020 May 20]. Available from: https://www.who.int/dg/speeches/detail/who-director-general-s-opening-remarks-at-the-media-briefing-on-covid-19—3-march-2020.

6. Lipsitch M, Donnelly CA, Fraser C, Blake IM, Cori A, Dorigatti I, et al. Potential biases in estimating absolute and relative case-fatality risks during outbreaks. PLoS Negl Trop Dis. 2015;9(7).

7. Battegay M, Kuehl R, Tschudin-Sutter S, Hirsch HH, Widmer AF, Neher RA. 2019-novel Coronavirus (2019-nCoV): estimating the case fatality rate-a word of caution. Swiss Medical Weekly. 2020;150(0506).

8. World Health Organization-China Joint Mission on Coronavirus Disease 2019 Group [Internet]. Report of the WHO-China Joint Mission on Coronavirus Disease 2019 (COVID-19). 2020 [cited 2020 Mar 22]. Available from: https://www.who.int/docs/default-source/coronaviruse/who-china-joint-mission-on-covid-19-final-report.pdf.

9. The Novel Coronavirus Pneumonia Emergency Response Epidemiology Team. Vital Surveillances: The Epidemiological Characteristics of an Outbreak of 2019 Novel Coronavirus Diseases (COVID-19)—China, 2020. China CDC Weekly. 2020; 2(8):113–122 [cited 2020 May 20]. Available from: http://weekly.chinacdc.cn/en/article/id/e53946e2-c6c4-41e9-9a9b-fea8db1a8f51.

10. nCov2019: An R package with real-time data and historical data and Shiny app. [software]. 2020 [cited 2020 Mar 22]. Available from: https://github.com/GuangchuangYu/nCov2019.

11. Zhang J, Klepac P, Read JM, Rosello A, Wang X, Lai S, et al. Patterns of human social contact and contact with animals in Shanghai, china. Scientific reports. 2019;9(1):1–11. doi: 10.1038/s41598-018-37186-2

12. Bundesministerium für Soziales, Gesundheit, Pflege und Konsumentenschutz [Internet]. Amtliches Dashboard COVID19. Bundesministerium für Soziales, Gesundheit, Pflege und Konsumentenschutz, Wien, Austria. 2020 [cited 2020 May 20]. Available from: https://info.gesundheitsministerium.at/.

13. Robert Koch Institute [Internet]. Epidemiological Situation in Germany. Robert Koch-Institut, Berlin, Germany. 2020 [cited 2020 May 20]. Available from: https://corona.rki.de.

14. Presidenza del Consiglio dei Ministri—Dipartimento della Protezione Civile [Internet]. GitHub repository on COVID-19. Dipartimento della Protezione Civile, Roma, Italy. 2020 [cited 2020 Apr 29]. Available from: https://github.com/pcm-dpc.

15. Istituto Superiore di Sanita. EPIDEMIA COVID-19, Aggiornamento nazionale (appendice), 28 aprile 2020—ore 16 : 00. Istituto Superiore di Sanità, Roma, Italy. 2020 [cited 2020 May 20]. Available from: https://www.epicentro.iss.it/coronavirus/bollettino/Bollettino-sorveglianza-integrata-COVID-19_28-aprile-2020.pdf.https://www.epicentro.iss.it/coronavirus/bollettino/Bollettino-sorveglianza-integrata-COVID-19_28-aprile-2020.pdf

16. Ministerio de Sanidad. Actualización n 89. Enfermedad por el coronavirus (COVID-19). 28.04.2020. Ministerio de Sanidad, Madrid, Spain. 2020 [cited 2020 May 20]. Available from: https://www.mscbs.gob.es/profesionales/saludPublica/ccayes/alertasActual/nCov-China/documentos/Actualizacion_89_COVID-19.pdf.https://www.mscbs.gob.es/profesionales/saludPublica/ccayes/alertasActual/nCov-China/documentos/Actualizacion_89_COVID-19.pdf

17. Mossong J, Hens N, Jit M, Beutels P, Auranen K, Mikolajczyk R, et al. Social Contacts and Mixing Patterns Relevant to the Spread of Infectious Diseases. PLoS Med. 2008 Mar;5(3):e74 [cited 2020 May 20]. Available from: https://dx.plos.org/10.1371/journal.pmed.0050074. doi: 10.1371/journal.pmed.0050074 18366252

18. Bi Q, Wu Y, Mei S, Ye C, Zou X, Zhang Z, et al. Epidemiology and Transmission of COVID-19 in Shenzhen China: Analysis of 391 cases and 1,286 of their close contacts. Lancet Inf Dis. 2020 April.

19. Buitrago-Garcia DC, Egli-Gany D, Counotte MJ, Hossmann S, Imeri H, Salanti G, et al. The role of asymptomatic SARS-CoV-2 infections: rapid living systematic review and meta-analysis. medRxiv [Preprint]. 2020 [cited 2020 May 20]. Available from: https://www.medrxiv.org/content/10.1101/2020.04.25.20079103v2.

20. Counotte MJ. Preliminary updated meta-analyses to https://www.medrxiv.org/content/10.1101/2020.04.02.20051318v1. medRxiv [Preprint]. 2020 [cited 2020 May 20]. Available from: https://doi.org/10.7910/DVN/VA62TK.

21. Japanese National Institute of Infectious Diseases. Field Briefing: Diamond Princess COVID-19 Cases. Japanese National Institute of Infectious Diseases; 2020 [cited 2020 Mar 22]. Available from: https://www.niid.go.jp/niid/en/2019-ncov-e/9407-covid-dp-fe-01.html.

22. Linton NM, Kobayashi T, Yang Y, Hayashi K, Akhmetzhanov AR, Jung Sm, et al. Incubation Period and Other Epidemiological Characteristics of 2019 Novel Coronavirus Infections with Right Truncation: A Statistical Analysis of Publicly Available Case Data. Journal of Clinical Medicine. 2020;9(2):538.

23. Carpenter B, Gelman A, Hoffman MD, Lee D, Goodrich B, Betancourt M, et al. Stan: A probabilistic programming language. Journal of statistical software. 2017;76(1).

24. Pellis L, Cauchemez S, Ferguson NM, Fraser C. Systematic selection between age and household structure for models aimed at emerging epidemic predictions. Nature Communications. 2020;11(1):1–11. doi: 10.1038/s41467-019-13993-7

25. Riou J, Poletto C, Boëlle PY. Improving early epidemiological assessment of emerging Aedes-transmitted epidemics using historical data. PLoS Negl Trop Dis. 2018;12(6):e0006526. doi: 10.1371/journal.pntd.0006526 29864129

26. Pérez-Trallero E, Piñeiro L, Vicente D, Montes M, Cilla G. Residual immunity in older people against the influenza A (H1N1)–recent experience in northern Spain. Eurosurveillance. 2009;14(39):19344. doi: 10.2807/ese.14.39.19344-en 19814966

27. Mizumoto K, Kagaya K, Zarebski A, Chowell G. Estimating the asymptomatic proportion of coronavirus disease 2019 (COVID-19) cases on board the Diamond Princess cruise ship, Yokohama, Japan, 2020. Eurosurveillance. 2020;25(10) [cited 2020 May 20]. Available from: https://www.eurosurveillance.org/content/10.2807/1560-7917.ES.2020.25.10.2000180.

28. Carrat F, Vergu E, Ferguson NM, Lemaitre M, Cauchemez S, Leach S, et al. Time lines of infection and disease in human influenza: a review of volunteer challenge studies. American journal of epidemiology. 2008;167(7):775–785. doi: 10.1093/aje/kwm375 18230677

29. Stringhini S, Wisniak A, Piumatti G, Azman AS, Baysson H, De Ridder D, et al. Repeated seroprevalence of anti-SARS-CoV-2 IgG antibodies in a population-based sample from Geneva, Switzerland. medRxiv [Preprint]. 2020 [cited 2020 May 20]. Available from: https://doi.org/10.1101/2020.05.02.20088898.

30. Ministerio de Sanidad. ESTUDIO ENE-COVID19: PRIMERA RONDA ESTUDIO NACIONAL DE SERO-EPIDEMIOLOGÍA DE LA INFECCIÓN POR SARS-COV-2 EN ESPAÑA.

31. Modi C, Boehm V, Ferraro S, Stein G, Seljak U. Total COVID-19 Mortality in Italy: Excess Mortality and Age Dependence through Time-Series Analysis. medRxiv [Preprint]. 2020 [cited 2020 May 20]. Available from: https://www.medrxiv.org/content/10.1101/2020.04.15.20067074v2.full.pdf.

32. Verity R, Okell LC, Dorigatti I, Winskill P, Whittaker C, Imai N, et al. Estimates of the severity of coronavirus disease 2019: a model-based analysis. The Lancet Infectious Diseases. 2020;20(6):669–77. doi: 10.1016/S1473-3099(20)30243-7 32240634

33. Wu JT, Leung K, Bushman M, Kishore N, Niehus R, de Salazar PM, et al. Estimating clinical severity of COVID-19 from the transmission dynamics in Wuhan, China. Nature Medicine. 2020;26 : 506–10. doi: 10.1038/s41591-020-0822-7 32284616

34. Wang X, Ma Z, Ning Y, Chen C, Chen R, Chen Q, et al. Estimating the case fatality ratio of the COVID-19 epidemic in China. medRxiv [Preprint]. 2020 [cited 2020 May 20]. Available from: https://www.medrxiv.org/content/10.1101/2020.02.17.20023630v5.

35. Sm Jung, Akhmetzhanov AR, Hayashi K, Linton NM, Yang Y, Yuan B, et al. Real-time estimation of the risk of death from novel coronavirus (covid-19) infection: Inference using exported cases. Journal of clinical medicine. 2020;9(2):523.

36. Dorigatti I, Okell L, Cori A, Imai N, Baguelin M, Bhatia S. Severity of 2019 novel coronavirus (nCoV); 2020 [cited 2020 Mar 22]. Available from:https://www.imperial.ac.uk/media/imperial-college/medicine/sph/ide/gida-fellowships/Imperial-College-COVID19-severity-10-02-2020.pdf.

37. Riou J, Hauser A, Counotte MJ, Althaus CL. Adjusted age-specific case fatality ratio during the COVID-19 epidemic in Hubei, China, January and February 2020. medRxiv [Preprint]. 2020 [cited 2020 Mar 22]. Available from: https://www.medrxiv.org/content/10.1101/2020.03.04.20031104v1.

38. Hethcote HW. The mathematics of infectious diseases. SIAM review. 2000;42(4):599–653.

39. Flaxman S, Mishra S, Gandy A, Unwin JT, Coupland H, Mellan TA, et al. Report 13: Estimating the number of infections and the impact of non-pharmaceutical interventions on COVID-19 in 11 European countries. Available from: https://doi.org/10.25561/77731.

Článek Evaluation of systems reform in public hospitals, Victoria, Australia, to improve access to antenatal care for women of refugee background: An interrupted time series design

Článek Participation in adherence clubs and on-time drug pickup among HIV-infected adults in Zambia: A matched-pair cluster randomized trial

Článek Efficacy and safety of bempedoic acid for the treatment of hypercholesterolemia: A systematic review and meta-analysis

Článek Health and cost impact of stepping down asthma medication for UK patients, 2001–2017: A population-based observational study

Článek Detection rate and treatment gap for atrial fibrillation identified through screening in community health centers in China (AF-CATCH): A prospective multicenter study

Článek Healthy behaviors at age 50 years and frailty at older ages in a 20-year follow-up of the UK Whitehall II cohort: A longitudinal study

Článek Carrying out embedded implementation research in humanitarian settings: A qualitative study in Cox's Bazar, Bangladesh

Článek Development of a polygenic risk score to improve screening for fracture risk: A genetic risk prediction study

Článek The association of skin autofluorescence with cardiovascular events and all-cause mortality in persons with chronic kidney disease stage 3: A prospective cohort study

Článek Impact of self-imposed prevention measures and short-term government-imposed social distancing on mitigating and delaying a COVID-19 epidemic: A modelling study

Článek Consistent sleep onset and maintenance of body weight after weight loss: An analysis of data from the NoHoW trial

Článek Associations of substance use, psychosis, and mortality among people living in precarious housing or homelessness: A longitudinal, community-based study in Vancouver, Canada