Evaluation of modelling study shows limits of COVID-19 importing risk simulations in sub-Saharan Africa.


Journal

Epidemiology and infection
ISSN: 1469-4409
Titre abrégé: Epidemiol Infect
Pays: England
ID NLM: 8703737

Informations de publication

Date de publication:
09 06 2020
Historique:
pubmed: 10 6 2020
medline: 24 6 2020
entrez: 10 6 2020
Statut: epublish

Résumé

Mathematical modelling studies predicting the spread of the coronavirus disease 2019 (COVID-19) have been used worldwide, but precisions are limited. Thus, continuous evaluation of the modelling studies is crucial. We investigated situations of virus importation in sub-Saharan Africa (SSA) to assess effectiveness of a modelling study by Haider N et al. titled 'Passengers' destinations from China: low risk of novel coronavirus (2019-nCoV) transmission into Africa and South America'. We obtained epidemiological data of 2417 COVID-19 cases reported by 40 countries in SSA within 30 days of the first case confirmed in Nigeria on 27 February. Out of 442 cases which had travel history available, only one (0.2%) had a travel history to China. These findings underline the result of the model. However, the fact that there were numbers of imported cases from other regions shows the limits of the model. The limits could be attributed to the characteristics of the COVID-19 which is infectious even when the patients do not express any symptoms. Therefore, there is a profound need for all modelling researchers to take asymptomatic cases into account when they establish modelling studies.

Identifiants

pubmed: 32513346
doi: 10.1017/S095026882000120X
pii: S095026882000120X
pmc: PMC7303472
doi:

Types de publication

Journal Article Comment

Langues

eng

Sous-ensembles de citation

IM

Pagination

e113

Commentaires et corrections

Type : CommentOn
Type : CommentIn

Références

Epidemiol Infect. 2020 Feb 26;148:e41
pubmed: 32100667
Nature. 2020 Apr;580(7803):316-318
pubmed: 32242115
N Engl J Med. 2020 Apr 17;:
pubmed: 32302075
N Engl J Med. 2020 May 28;382(22):2158-2160
pubmed: 32329972

Auteurs

T Miyachi (T)

Medical Governance Research Institute, Tokyo, Japan.

T Tanimoto (T)

Medical Governance Research Institute, Tokyo, Japan.

M Kami (M)

Medical Governance Research Institute, Tokyo, Japan.

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Classifications MeSH