Using observational data to quantify bias of traveller-derived COVID-19 prevalence estimates in Wuhan, China.
Journal
The Lancet. Infectious diseases
ISSN: 1474-4457
Titre abrégé: Lancet Infect Dis
Pays: United States
ID NLM: 101130150
Informations de publication
Date de publication:
07 2020
07 2020
Historique:
received:
18
02
2020
revised:
10
03
2020
accepted:
13
03
2020
pubmed:
5
4
2020
medline:
11
7
2020
entrez:
5
4
2020
Statut:
ppublish
Résumé
The incidence of coronavirus disease 2019 (COVID-19) in Wuhan, China, has been estimated using imported case counts of international travellers, generally under the assumptions that all cases of the disease in travellers have been ascertained and that infection prevalence in travellers and residents is the same. However, findings indicate variation among locations in the capacity for detection of imported cases. Singapore has had very strong epidemiological surveillance and contact tracing capacity during previous infectious disease outbreaks and has consistently shown high sensitivity of case-detection during the COVID-19 outbreak. We used a Bayesian modelling approach to estimate the relative capacity for detection of imported cases of COVID-19 for 194 locations (excluding China) compared with that for Singapore. We also built a simple mathematical model of the point prevalence of infection in visitors to an epicentre relative to that in residents. The weighted global ability to detect Wuhan-to-location imported cases of COVID-19 was estimated to be 38% (95% highest posterior density interval [HPDI] 22-64) of Singapore's capacity. This value is equivalent to 2·8 (95% HPDI 1·5-4·4) times the current number of imported and reported cases that could have been detected if all locations had had the same detection capacity as Singapore. Using the second component of the Global Health Security index to stratify likely case-detection capacities, the ability to detect imported cases relative to Singapore was 40% (95% HPDI 22-67) among locations with high surveillance capacity, 37% (18-68) among locations with medium surveillance capacity, and 11% (0-42) among locations with low surveillance capacity. Treating all travellers as if they were residents (rather than accounting for the brief stay of some of these travellers in Wuhan) contributed modestly to underestimation of prevalence. Estimates of case counts in Wuhan based on assumptions of 100% detection in travellers could have been underestimated by several fold. Furthermore, severity estimates will be inflated several fold since they also rely on case count estimates. Finally, our model supports evidence that underdetected cases of COVID-19 have probably spread in most locations around the world, with greatest risk in locations of low detection capacity and high connectivity to the epicentre of the outbreak. US National Institute of General Medical Sciences, and Fellowship Foundation Ramon Areces.
Sections du résumé
BACKGROUND
The incidence of coronavirus disease 2019 (COVID-19) in Wuhan, China, has been estimated using imported case counts of international travellers, generally under the assumptions that all cases of the disease in travellers have been ascertained and that infection prevalence in travellers and residents is the same. However, findings indicate variation among locations in the capacity for detection of imported cases. Singapore has had very strong epidemiological surveillance and contact tracing capacity during previous infectious disease outbreaks and has consistently shown high sensitivity of case-detection during the COVID-19 outbreak.
METHODS
We used a Bayesian modelling approach to estimate the relative capacity for detection of imported cases of COVID-19 for 194 locations (excluding China) compared with that for Singapore. We also built a simple mathematical model of the point prevalence of infection in visitors to an epicentre relative to that in residents.
FINDINGS
The weighted global ability to detect Wuhan-to-location imported cases of COVID-19 was estimated to be 38% (95% highest posterior density interval [HPDI] 22-64) of Singapore's capacity. This value is equivalent to 2·8 (95% HPDI 1·5-4·4) times the current number of imported and reported cases that could have been detected if all locations had had the same detection capacity as Singapore. Using the second component of the Global Health Security index to stratify likely case-detection capacities, the ability to detect imported cases relative to Singapore was 40% (95% HPDI 22-67) among locations with high surveillance capacity, 37% (18-68) among locations with medium surveillance capacity, and 11% (0-42) among locations with low surveillance capacity. Treating all travellers as if they were residents (rather than accounting for the brief stay of some of these travellers in Wuhan) contributed modestly to underestimation of prevalence.
INTERPRETATION
Estimates of case counts in Wuhan based on assumptions of 100% detection in travellers could have been underestimated by several fold. Furthermore, severity estimates will be inflated several fold since they also rely on case count estimates. Finally, our model supports evidence that underdetected cases of COVID-19 have probably spread in most locations around the world, with greatest risk in locations of low detection capacity and high connectivity to the epicentre of the outbreak.
FUNDING
US National Institute of General Medical Sciences, and Fellowship Foundation Ramon Areces.
Identifiants
pubmed: 32246905
pii: S1473-3099(20)30229-2
doi: 10.1016/S1473-3099(20)30229-2
pmc: PMC7270516
mid: NIHMS1584042
pii:
doi:
Types de publication
Journal Article
Observational Study
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
803-808Subventions
Organisme : NIGMS NIH HHS
ID : R35 GM124715
Pays : United States
Organisme : NIGMS NIH HHS
ID : U54 GM088558
Pays : United States
Commentaires et corrections
Type : CommentIn
Informations de copyright
Copyright © 2020 Elsevier Ltd. All rights reserved.
Références
Science. 2009 Jun 19;324(5934):1557-61
pubmed: 19433588
Public Health. 2006 Jan;120(1):20-6
pubmed: 16297416
Lancet. 2020 Feb 29;395(10225):689-697
pubmed: 32014114
PLoS One. 2009 Sep 09;4(9):e6895
pubmed: 19742302
Nat Med. 2020 Apr;26(4):506-510
pubmed: 32284616