Serology-informed estimates of SARS-CoV-2 infection fatality risk in Geneva, Switzerland
Authors: Javier Perez-Saez, Stephen A Lauer, Laurent Kaiser, Simon Regard, Elisabeth Delaporte, Idris Guessous, Silvia Stringhini, Andrew S Azman, Davidovic Alioucha, Isabelle Arm-Vernez, Sultan Bahta, Jonathan Barbolini, Hélène Baysson, Rebecca Butzberger, Sophie Cattani, François Chappuis, Alison Chiovini, Prune Collombet, Delphine Courvoisier, David De Ridder, Eugénie De Weck, Paola D'ippolito, Antoine Daeniker, Odile Desvachez, Yaron Dibner, Céline Dubas, Joséphine Duc, Isabella Eckerle, Céline Eelbode, Nacira El Merjani, Benjamin Emery, Benoit Favre, Antoine Flahault, Natalie Francioli, Laurent Gétaz, Alice Gilson, Acem Gonul, Julie Guérin, Lina Hassar, Aurélia Hepner, Francesca Hovagemyan, Samia Hurst, Olivia Keiser, Melis Kir, Gaëlle Lamour, Pierre Lescuyer, Fanny Lombard, Amélie Mach, Yasmina Malim, Eva Marchetti, Kailing Marcus, Soraya Maret, Chantal Martinez, Kourosh Massiha, Virginie Mathey-Doret, Loan Mattera, Philippe Matute, Jean-Michel Maugey, Benjamin Meyer, Tom Membrez, Natacha Michel, Aleksandra Mitrovic, Emmanuelle Marie Mohbat, Mayssam Nehme, Natacha Noël, Hugo-Ken Oulevey, Febronio Pardo, Francesco Pennacchio, Dusan Petrovic, Attilio Picazio, Giovanni Piumatti, Didier Pittet, Jane Portier, Géraldine Poulain, Klara Posfay-Barbe, Jean-François Pradeau, Caroline Pugin, Rakotomiaramanana Barinjaka Rakotomiaramanana, Aude Richard, Christiane Rocchia Fine, Irine Sakvarelidze, Lilas Salzmann-Bellard, Magdalena Schellongova, Stephanie Schrempft, Mélanie Seixas Miranda, Milena Stimec, Michel Tacchino, Sophie Theurillat, Mélissa Tomasini, Kor-Gael Toruslu, Nawel Tounsi, Didier Trono, Natacha Vincent, Guillemette Violot, Nicolas Vuilleumier, Zoé Waldmann, Sylvie Welker, Manon Will, Ania Wisniak, Sabine Yerly, Maria-Eugenia Zaballa, Alenka Zeballos Valle
Journal: The Lancet Infectious Diseases (2020)

The infection fatality risk (IFR) is the average number of deaths per infection by a pathogen and is key to characterising the severity of infection across the population and for specific demographic groups. To date, few empirical estimates of the IFR for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) have been published owing to challenges in measuring infection rates.1Russell TW Hellewell J Jarvis CI 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.Eurosurveillance. 2020; 252000256Crossref Scopus (266) Google Scholar, 2Meyerowitz-Katz G Merone L A systematic review and meta-analysis of published research data on COVID-19 infection-fatality rates.medRxiv. 2020; (published online May 27.) (preprint)https://doi.org/10.1101/2020.05.03.20089854Google Scholar Outside of closed, closely observed populations where infection rates can be monitored through viral surveillance, we must rely on indirect measures of infection, such as antibodies. Representative seroprevalence studies provide an important opportunity to estimate the number of infections in a community, and when combined with death counts can lead to robust estimates of the IFR. We estimated overall and age-specific IFRs for the canton of Geneva, Switzerland, using age-stratified daily case and death incidence reports combined with population-based seroprevalence estimates done each week for 5 consecutive weeks (table).3Stringhini S Wisniak A Piumatti G et al.Seroprevalence of anti-SARS-CoV-2 IgG antibodies in Geneva, Switzerland (SEROCoV-POP): a population-based study.Lancet. 2020; (published online June 11.)https://doi.org/10.1016/S0140-6736(20)31304-0Summary Full Text Full Text PDF PubMed Scopus (654) Google Scholar From Feb 24, to June 2, 2020, there were 5039 confirmed cases of COVID-19 and 286 reported deaths within Geneva (population of 506 765). We inferred age-stratified (5–9 years, 10–19 years, 20–49 years, 50–64 years, and ≥65 years) IFRs by linking the observed number of deaths to the estimated number of infected individuals from each serosurvey. We account for the delays between infection and seroconversion, as well as between infection and death (including deaths that had not yet been observed at the time of the analyses).4Nishiura H Klinkenberg D Roberts M Heesterbeek JAP Early epidemiological assessment of the virulence of emerging infectious diseases: a case study of an influenza pandemic.PLoS One. 2009; 4e6852Crossref PubMed Scopus (91) Google Scholar Inference is drawn in a Bayesian framework that incorporates uncertainty in seroprevalence estimates (appendix p 3).TableAge-stratified estimates of the IFR of SARS-CoV-2 in the canton of Geneva, SwitzerlandPopulationSeroconverted population as of May 6 (95% CrI)Deaths as of June 1IFR (95% CrI), %5–9 years26 4661200 (400–2400)00·0016 (0 to 0·019)10–19 years53 1806100 (3900–8800)00·00032 (0 to 0·0033)20–49 years219 44028 800 (21 400–37 300)20·0092 (0·0042 to 0·016)50–64 years98 52810 300 (7200–13 900)160·14 (0·096 to 0·19)≥65 years83 574*Of whom approximately 4065 (4·9%) live in assisted care facilities.5700 (3200–8800)2685·6 (4·3 to 7·4)All506 76554 800 (41 300–70 700)2860·64 (0·38 to 0·98)CrI=credible interval. IFR=infection fatality risk. SARS-CoV-2=severe acute respiratory syndrome coronavirus 2.* Of whom approximately 4065 (4·9%) live in assisted care facilities. Open table in a new tab CrI=credible interval. IFR=infection fatality risk. SARS-CoV-2=severe acute respiratory syndrome coronavirus 2. Of the 286 reported deaths caused by SARS-CoV-2, the youngest person to die was 31 years old. Infected individuals aged 20–49 years had an IFR of 0·0092% (95% credible interval 0·0042–0·016; one in 10 870 risk of death), with the IFR increasing to 0·14% (0·096–0·19) for those aged 50–64 years and to 5·6% (4·3–7·4) for those aged 65 years and older. After accounting for demography and age-specific seroprevalence, we estimated a population-wide IFR of 0·64% (0·38–0·98; table). Our results are subject to two notable limitations. Of 268 individuals aged 65 years and older who died, 134 (50%) were residents of assisted care facilities, where around 0·8% of the Geneva population resides. Although the serosurvey protocol did not explicitly exclude these individuals, they are likely to have been absent or severely under-represented. This under-representation would lead to an overestimation of the IFR in the 65 years and older age group if seroprevalence in this institutionalised population was higher than in the general population of the same age (appendix p 6). Furthermore, our IFR estimates are based on existing evidence regarding post-infection antibody kinetics, which might differ between severe and mild infections. If mild infections have substantially lower and short-lived antibody responses, our estimates of the IFR might be biased upwards.5Takahashi S Greenhouse B Rodríguez-Barraquer I Are SARS-CoV-2 seroprevalence estimates biased?.OSF Preprints. 2020; (published online May 30.) (preprint)https://doi.org/10.31219/osf.io/y3fxtGoogle Scholar Estimates of the IFR are key to understanding the true pandemic burden and for comparing different risk-reduction strategies. The IFR is not solely determined by host and pathogen biology, but also by the capacity of health systems to treat severe cases. Despite having among the highest per-capita incidence of confirmed COVID-19 in Switzerland, Geneva's health system, with additional COVID-19 surge capacity, accommodated the influx of cases needing intensive care (peak of 80 of 110 surge capacity intensive care unit beds were in use at one time) while maintaining care quality standards. As such, our IFR estimates can be seen as a best-case scenario with respect to health system capacity. Our results reveal that population-wide estimates of IFR mask great heterogeneity by age and point towards the importance of age-targeted interventions to reduce exposures among those at highest risk of death. We declare no competing interests. SS and ASA are co-senior authors. The code and data needed to reproduce analyses are available at https://github.com/HopkinsIDD/sarscov2-ifr-gva. Download .pdf (.99 MB) Help with pdf files Supplementary appendix