Evaluation of undetected cases during the COVID-19 epidemic in Austria.
Agent-based modelling
COVID-19
Undetected cases
Journal
BMC infectious diseases
ISSN: 1471-2334
Titre abrégé: BMC Infect Dis
Pays: England
ID NLM: 100968551
Informations de publication
Date de publication:
13 Jan 2021
13 Jan 2021
Historique:
received:
05
10
2020
accepted:
26
12
2020
entrez:
14
1
2021
pubmed:
15
1
2021
medline:
26
1
2021
Statut:
epublish
Résumé
Knowing the number of undetected cases of COVID-19 is important for a better understanding of the spread of the disease. This study analyses the temporal dynamic of detected vs. undetected cases to provide guidance for the interpretation of prevalence studies performed with PCR or antibody tests to estimate the detection rate. We used an agent-based model to evaluate assumptions on the detection probability ranging from 0.1 to 0.9. For each general detection probability, we derived age-dependent detection probabilities and calibrated the model to reproduce the epidemic wave of COVID-19 in Austria from March 2020 to June 2020. We categorized infected individuals into presymptomatic, symptomatic unconfirmed, confirmed and never detected to observe the simulated dynamic of the detected and undetected cases. The calculation of the age-dependent detection probability ruled values lower than 0.4 as most likely. Furthermore, the proportion of undetected cases depends strongly on the dynamic of the epidemic wave: during the initial upswing, the undetected cases account for a major part of all infected individuals, whereas their share decreases around the peak of the confirmed cases. The results of prevalence studies performed to determine the detection rate of COVID-19 patients should always be interpreted with regard to the current dynamic of the epidemic wave. Applying the method proposed in our analysis, the prevalence study performed in Austria in April 2020 could indicate a detection rate of 0.13, instead of the prevalent ratio of 0.29 between detected and estimated undetected cases at that time.
Sections du résumé
BACKGROUND
BACKGROUND
Knowing the number of undetected cases of COVID-19 is important for a better understanding of the spread of the disease. This study analyses the temporal dynamic of detected vs. undetected cases to provide guidance for the interpretation of prevalence studies performed with PCR or antibody tests to estimate the detection rate.
METHODS
METHODS
We used an agent-based model to evaluate assumptions on the detection probability ranging from 0.1 to 0.9. For each general detection probability, we derived age-dependent detection probabilities and calibrated the model to reproduce the epidemic wave of COVID-19 in Austria from March 2020 to June 2020. We categorized infected individuals into presymptomatic, symptomatic unconfirmed, confirmed and never detected to observe the simulated dynamic of the detected and undetected cases.
RESULTS
RESULTS
The calculation of the age-dependent detection probability ruled values lower than 0.4 as most likely. Furthermore, the proportion of undetected cases depends strongly on the dynamic of the epidemic wave: during the initial upswing, the undetected cases account for a major part of all infected individuals, whereas their share decreases around the peak of the confirmed cases.
CONCLUSIONS
CONCLUSIONS
The results of prevalence studies performed to determine the detection rate of COVID-19 patients should always be interpreted with regard to the current dynamic of the epidemic wave. Applying the method proposed in our analysis, the prevalence study performed in Austria in April 2020 could indicate a detection rate of 0.13, instead of the prevalent ratio of 0.29 between detected and estimated undetected cases at that time.
Identifiants
pubmed: 33441091
doi: 10.1186/s12879-020-05737-6
pii: 10.1186/s12879-020-05737-6
pmc: PMC7805565
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
70Subventions
Organisme : Österreichische Forschungsförderungsgesellschaft
ID : 881665/35755806
Références
N Engl J Med. 2020 Jun 11;382(24):2302-2315
pubmed: 32289214
Lancet Glob Health. 2020 Apr;8(4):e488-e496
pubmed: 32119825
Science. 2020 May 1;368(6490):489-493
pubmed: 32179701
Nature. 2020 Aug;584(7821):420-424
pubmed: 32674112
Ann Intern Med. 2020 May 5;172(9):577-582
pubmed: 32150748
Euro Surveill. 2020 Mar;25(10):
pubmed: 32183930
Value Health. 2012 Sep-Oct;15(6):796-803
pubmed: 22999128
Acta Paediatr. 2020 Jun;109(6):1088-1095
pubmed: 32202343