CoChat
Sign Up
Cited by 209COVID-19 epidemiological studiesMathematical and Theoretical Epidemiology and Ecology ModelsCOVID-19 Prevention and Impact

The DOI matches its Crossref record; 36 of the 36 references it cites were found in a registry or an index.

The relatively young and rural population may limit the spread and severity of COVID-19 in Africa: a modelling study

Open access
Diop, Binta Zahra, Ngom, Marieme, Pougué Biyong, Clémence, Pougué Biyong, John N·2020·BMJ Global Health
DOI 10.1136/bmjgh-2020-002699PMC PMC7252974

Published version of a preprint

Open this paper in CoChat — free account

Abstract

Introduction: A novel coronavirus disease 2019 (COVID-19) has spread to all regions of the world. There is great uncertainty regarding how countries’ characteristics will affect the spread of the epidemic; to date, there are few studies that attempt to predict the spread of the epidemic in African countries. In this paper, we investigate the role of demographic patterns, urbanisation and comorbidities on the possible trajectories of COVID-19 in Ghana, Kenya and Senegal. Methods: We use an augmented deterministic Susceptible-Infected-Recovered model to predict the true spread of the disease, under the containment measures taken so far. We disaggregate the infected compartment into asymptomatic, mildly symptomatic and severely symptomatic to match observed clinical development of COVID-19. We also account for age structures, urbanisation and comorbidities (HIV, tuberculosis, anaemia). Results: In our baseline model, we project that the peak of active cases will occur in July, subject to the effectiveness of policy measures. When accounting for the urbanisation, and factoring in comorbidities, the peak may occur between 2 June and 17 June (Ghana), 22 July and 29 August (Kenya) and, finally, 28 May and 15 June (Senegal). Successful containment policies could lead to lower rates of severe infections. While most cases will be mild, we project in the absence of policies further containing the spread, that between 0.78% and 1.03%, 0.61% and 1.22%, and 0.60% and 0.84% of individuals in Ghana, Kenya and Senegal, respectively, may develop severe symptoms at the time of the peak of the epidemic. Conclusion: Compared with Europe, Africa’s younger and rural population may modify the severity of the epidemic. The large youth population may lead to more infections but most of these infections will be asymptomatic or mild, and will probably go undetected. The higher prevalence of underlying conditions must be considered.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1136/bmjgh-2020-002699

Cites

36 of 36 references verified0 citation errors · 0 not checkable

Statistics

Figures

Figure 1 Benchmark, South Korea.
Figure 1 Benchmark, South Korea.CC BY-NC · PubMed Central
Figure 2 Timing of policies across countries.
Figure 2 Timing of policies across countries.CC BY-NC · PubMed Central
Figure 3 Projection of active infections.
Figure 3 Projection of active infections.CC BY-NC · PubMed Central
Figure 4 Projected active infections accounting for underlying conditions and rural areas. Y-axis is per cent of total population.
Figure 4 Projected active infections accounting for underlying conditions and rural areas. Y-axis is per cent of total population.CC BY-NC · PubMed Central
Figure 5 Projected severe active infections mirroring South Korean’s Rt.
Figure 5 Projected severe active infections mirroring South Korean’s Rt.CC BY-NC · PubMed Central

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 36 references

Research works printed but not linked: 3

  1. Lancet global burden of disease. Available: https://www.thelancet.com/gbd?source=post_page [Accessed 27 Apr 2020].
  2. Read J , Bridgen J , Cummings D , et al . Novel coronavirus 2019-nCoV: early estimation of epidemiological parameters and epidemic predictions. medRxiv 2020. · DOI
  3. Arenas A , Cota W , Gomez-Gardenes J , et al . A mathematical model for the spatiotemporal epidemic spreading of COVID19. MedRxiv 2020. · DOI

Cited by

Sorted by citations

Items 101–200 of 209

Related

See all

Metadata

Authors
Diop, B. Z., Ngom, M., Pougué Biyong, C., & Pougué Biyong, J. N.
Affiliations
  • University of Oxford (GB)
  • Mathematics and Computer Science, Argonne National Laboratory, Lemont, Illinois, USA
  • Economics, Université Paris 1 Panthéon-Sorbonne, Paris, Île-de-France, France
  • Mathematics, University of Oxford, Oxford, UK
Corresponding author
Diop, Binta Zahra
ORCID
Diop, Binta Zahra
Venue
BMJ Global Health
Publisher
BMJ
Published
2020-05
Volume
5
Issue
5
Pages
e002699
ISSN
2059-7908 (ISSN-L)
DOI
10.1136/bmjgh-2020-002699
PMC
PMC7252974
OpenAlex
W3027173470
Cited by
207
References
36
Citations by year
2026: 7 · 2025: 8 · 2024: 11 · 2023: 26 · 2022: 37 · 2021: 79 · 2020: 39
FWCI
7.23
Type
journal article
Language
en
Update notices
correction (2020-07-16)
Open access
Licence
Locations
  • BMJ Global Health (publishedVersion, cc-by-nc)
  • medRxiv (acceptedVersion, cc-by-nc-nd)
  • PubMed Central (publishedVersion, cc-by-nc)
  • PubMed (publishedVersion)
  • OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (submittedVersion, other-oa)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
  • U.S. Department of Energy (DE‐ AC02‐06CH11357, AC02‐06CH11357, 06CH11357, DE-AC02-)
  • Global Challenges Research Fund
  • Office of Science (AC02‐06CH11357, DE-AC02, 06CH11357, DE-AC02-06CH11357)
  • Engineering and Physical Sciences Research Council (DE-AC02-06CH11357)
Topics
  • Physical Sciences › Mathematics › Modeling and Simulation › COVID-19 epidemiological studies
  • Physical Sciences › Mathematics › Applied Mathematics › Mathematical and Theoretical Epidemiology and Ecology Models
  • Social Sciences › Sociology and Political Science › COVID-19 Prevention and Impact
Keywords
COVID-19 pandemic, epidemic projection, age structure, rural population, urbanization, comorbidities, HIV, tuberculosis, anemia, SARS-CoV-2 transmission, containment measures, disease severity, asymptomatic infection, Ghana, Kenya, Senegal
MeSH terms
Betacoronavirus, COVID-19, SARS-CoV-2, Africa, Humans, Pneumonia, Viral, Rural Population, Severity of Illness Index, Coronavirus Infections, Pandemics

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review