Hostname: page-component-77f85d65b8-g4pgd Total loading time: 0 Render date: 2026-03-30T06:57:13.166Z Has data issue: false hasContentIssue false

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

Published online by Cambridge University Press:  09 June 2020

T. Miyachi*
Affiliation:
Medical Governance Research Institute, Tokyo, Japan
T. Tanimoto
Affiliation:
Medical Governance Research Institute, Tokyo, Japan
M. Kami
Affiliation:
Medical Governance Research Institute, Tokyo, Japan
*
Author for correspondence: T. Miyachi, E-mail: takashi.miyachi02@gmail.com
Rights & Permissions [Opens in a new window]

Abstract

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.

Information

Type
Letter to the Editor
Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.
Copyright
Copyright © The Author(s), 2020. Published by Cambridge University Press