Methods for early characterisation of the severity and dynamics of SARS-CoV-2 variants: a population-based time series analysis in South Africa.

Autor:	Reichert E; Center for Communicable Disease Dynamics, Harvard T.H. Chan School of Public Health, Boston, MA, USA. Electronic address: ereichert@hsph.harvard.edu., Schaeffer B; Center for Communicable Disease Dynamics, Harvard T.H. Chan School of Public Health, Boston, MA, USA., Gantt S; Center for Communicable Disease Dynamics, Harvard T.H. Chan School of Public Health, Boston, MA, USA., Rumpler E; Center for Communicable Disease Dynamics, Harvard T.H. Chan School of Public Health, Boston, MA, USA., Govender N; Division of Public Health Surveillance and Response, National Institute for Communicable Diseases, National Health Laboratory Service, Johannesburg, South Africa., Welch R; Division of Public Health Surveillance and Response, National Institute for Communicable Diseases, National Health Laboratory Service, Johannesburg, South Africa., Shonhiwa AM; Division of Public Health Surveillance and Response, National Institute for Communicable Diseases, National Health Laboratory Service, Johannesburg, South Africa., Iwu CD; Division of Public Health Surveillance and Response, National Institute for Communicable Diseases, National Health Laboratory Service, Johannesburg, South Africa., Lamola TM; Division of Public Health Surveillance and Response, National Institute for Communicable Diseases, National Health Laboratory Service, Johannesburg, South Africa., Moema-Matiea I; Division of Public Health Surveillance and Response, National Institute for Communicable Diseases, National Health Laboratory Service, Johannesburg, South Africa., Muganhiri D; Division of Public Health Surveillance and Response, National Institute for Communicable Diseases, National Health Laboratory Service, Johannesburg, South Africa., Hanage W; Center for Communicable Disease Dynamics, Harvard T.H. Chan School of Public Health, Boston, MA, USA., Santillana M; Center for Communicable Disease Dynamics, Harvard T.H. Chan School of Public Health, Boston, MA, USA., Jassat W; Division of Public Health Surveillance and Response, National Institute for Communicable Diseases, National Health Laboratory Service, Johannesburg, South Africa., Cohen C; Centre for Respiratory Diseases and Meningitis, National Institute for Communicable Diseases, National Health Laboratory Service, Johannesburg, South Africa; School of Public Health, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa., Swerdlow D; Center for Communicable Disease Dynamics, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
Jazyk:	angličtina
Zdroj:	The Lancet. Microbe [Lancet Microbe] 2022 Oct; Vol. 3 (10), pp. e753-e761. Date of Electronic Publication: 2022 Aug 31.
DOI:	10.1016/S2666-5247(22)00182-3
Abstrakt:	Background: Assessment of disease severity associated with a novel pathogen or variant provides crucial information needed by public health agencies and governments to develop appropriate responses. The SARS-CoV-2 omicron variant of concern (VOC) spread rapidly through populations worldwide before robust epidemiological and laboratory data were available to investigate its relative severity. Here we develop a set of methods that make use of non-linked, aggregate data to promptly estimate the severity of a novel variant, compare its characteristics with those of previous VOCs, and inform data-driven public health responses. Methods: Using daily population-level surveillance data from the National Institute for Communicable Diseases in South Africa (March 2, 2020, to Jan 28, 2022), we determined lag intervals most consistent with time from case ascertainment to hospital admission and within-hospital death through optimisation of the distance correlation coefficient in a time series analysis. We then used these intervals to estimate and compare age-stratified case-hospitalisation and case-fatality ratios across the four epidemic waves that South Africa has faced, each dominated by a different variant. Findings: A total of 3 569 621 cases, 494 186 hospitalisations, and 99 954 deaths attributable to COVID-19 were included in the analyses. We found that lag intervals and disease severity were dependent on age and variant. At an aggregate level, fluctuations in cases were generally followed by a similar trend in hospitalisations within 7 days and deaths within 15 days. We noted a marked reduction in disease severity throughout the omicron period relative to previous waves (age-standardised case-fatality ratios were consistently reduced by >50%), most substantial for age strata with individuals 50 years or older. Interpretation: This population-level time series analysis method, which calculates an optimal lag interval that is then used to inform the numerator of severity metrics including the case-hospitalisation and case-fatality ratio, provides useful and timely estimates of the relative effects of novel SARS-CoV-2 VOCs, especially for application in settings where resources are limited. Funding: National Institute for Communicable Diseases of South Africa, South African National Government. Competing Interests: Declaration of interests WH reports his position as a member of Biobot Analytics’ scientific advisory board and has received stock options in Biobot Analytics, as well as payment for expert witness testimony on the expected course of the COVID-19 pandemic. DS reports previous employment at Pfizer before the initiation of this analysis and stock in Pfizer. DS also reports compensation for occasional 1 h blinded consultancies for several consulting companies over the past 36 months. MS reports receipt of institutional research funds from the Johnson and Johnson Foundation and from Pfizer; neither funder had a role in the design or content of the current manuscript. CC reports grant funding from the Wellcome Trust, South African Medical Research Council, US Centers for Disease Control and Prevention, and Sanofi Pasteur to institute COVID-19 research in the past 36 months, as well as a role on the scientific advisory committee for “BCHW: Burden of COVID-19 among health care workers, assessing infection, risk factors, vaccine effectiveness, working experiences and one-health implications: a mixed methodology, multisite international study”. RW reports shareholding stock in the following health or pharmaceutical companies in South Africa, none related to this work: Adcock Ingram Holdings, Dischem Pharmacies, Discovery, Netcare, and Aspen Pharmacare Holdings. All interests listed are outside the current work. All other authors declare no competing interests. (Copyright © 2022 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY-NC-ND 4.0 license. Published by Elsevier Ltd.. All rights reserved.)
Databáze:	MEDLINE
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