The relatively young and rural population may limit the spread and severity of COVID-19 in Africa: a modelling study.
Identifieur interne : 000396 ( Main/Exploration ); précédent : 000395; suivant : 000397The relatively young and rural population may limit the spread and severity of COVID-19 in Africa: a modelling study.
Auteurs : Binta Zahra Diop [Royaume-Uni] ; Marieme Ngom [États-Unis] ; Clémence Pougué Biyong [France] ; John N. Pougué Biyong [Royaume-Uni]Source :
- BMJ global health [ 2059-7908 ] ; 2020.
Descripteurs français
- KwdFr :
- MESH :
English descriptors
- KwdEn :
- MESH :
- geographic , epidemiology : Africa.
- epidemiology : Coronavirus Infections, Pneumonia, Viral.
- Betacoronavirus, Humans, Pandemics, Rural Population, Severity of Illness Index.
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.
DOI: 10.1136/bmjgh-2020-002699
PubMed: 32451367
PubMed Central: PMC7252974
Affiliations:
- France, Royaume-Uni, États-Unis
- Angleterre, Illinois, Oxfordshire, Île-de-France
- Oxford, Paris
- Université Paris 1 Panthéon-Sorbonne, Université d'Oxford
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Pougué Biyong</name><affiliation wicri:level="4"><nlm:affiliation>Mathematics, University of Oxford, Oxford, UK.</nlm:affiliation><country xml:lang="fr">Royaume-Uni</country><wicri:regionArea>Mathematics, University of Oxford, Oxford</wicri:regionArea><placeName><settlement type="city">Oxford</settlement><region type="country">Angleterre</region><region type="comté" nuts="2">Oxfordshire</region></placeName><orgName type="university">Université d'Oxford</orgName></affiliation></author></analytic><series><title level="j">BMJ global health</title><idno type="ISSN">2059-7908</idno><imprint><date when="2020" type="published">2020</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Africa (epidemiology)</term><term>Betacoronavirus (MeSH)</term><term>Coronavirus Infections (epidemiology)</term><term>Humans (MeSH)</term><term>Pandemics (MeSH)</term><term>Pneumonia, Viral (epidemiology)</term><term>Rural Population (MeSH)</term><term>Severity of Illness Index (MeSH)</term></keywords><keywords scheme="KwdFr" xml:lang="fr"><term>Afrique (épidémiologie)</term><term>Betacoronavirus (MeSH)</term><term>Humains (MeSH)</term><term>Indice de gravité de la maladie (MeSH)</term><term>Infections à coronavirus (épidémiologie)</term><term>Pandémies (MeSH)</term><term>Pneumopathie virale (épidémiologie)</term><term>Population rurale (MeSH)</term></keywords><keywords scheme="MESH" type="geographic" qualifier="epidemiology" xml:lang="en"><term>Africa</term></keywords><keywords scheme="MESH" qualifier="epidemiology" xml:lang="en"><term>Coronavirus Infections</term><term>Pneumonia, Viral</term></keywords><keywords scheme="MESH" qualifier="épidémiologie" xml:lang="fr"><term>Afrique</term><term>Infections à coronavirus</term><term>Pneumopathie virale</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Betacoronavirus</term><term>Humans</term><term>Pandemics</term><term>Rural Population</term><term>Severity of Illness Index</term></keywords><keywords scheme="MESH" xml:lang="fr"><term>Betacoronavirus</term><term>Humains</term><term>Indice de gravité de la maladie</term><term>Pandémies</term><term>Population rurale</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><p><b>INTRODUCTION</b></p><p>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.</p></div><div type="abstract" xml:lang="en"><p><b>METHODS</b></p><p>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).</p></div><div type="abstract" xml:lang="en"><p><b>RESULTS</b></p><p>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.</p></div><div type="abstract" xml:lang="en"><p><b>CONCLUSION</b></p><p>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.</p></div></front></TEI><pubmed><MedlineCitation Status="MEDLINE" Owner="NLM"><PMID Version="1">32451367</PMID><DateCompleted><Year>2020</Year><Month>06</Month><Day>01</Day></DateCompleted><DateRevised><Year>2020</Year><Month>06</Month><Day>23</Day></DateRevised><Article PubModel="Print"><Journal><ISSN IssnType="Print">2059-7908</ISSN><JournalIssue CitedMedium="Print"><Volume>5</Volume><Issue>5</Issue><PubDate><Year>2020</Year><Month>05</Month></PubDate></JournalIssue><Title>BMJ global health</Title><ISOAbbreviation>BMJ Glob Health</ISOAbbreviation></Journal><ArticleTitle>The relatively young and rural population may limit the spread and severity of COVID-19 in Africa: a modelling study.</ArticleTitle><ELocationID EIdType="pii" ValidYN="Y">e002699</ELocationID><ELocationID EIdType="doi" ValidYN="Y">10.1136/bmjgh-2020-002699</ELocationID><Abstract><AbstractText Label="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.</AbstractText><AbstractText Label="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).</AbstractText><AbstractText Label="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.</AbstractText><AbstractText Label="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.</AbstractText><CopyrightInformation>© Author(s) (or their employer(s)) 2020. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. 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N</ForeName><Initials>JN</Initials><AffiliationInfo><Affiliation>Mathematics, University of Oxford, Oxford, UK.</Affiliation></AffiliationInfo></Author></AuthorList><Language>eng</Language><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType></PublicationTypeList></Article><MedlineJournalInfo><Country>England</Country><MedlineTA>BMJ Glob Health</MedlineTA><NlmUniqueID>101685275</NlmUniqueID><ISSNLinking>2059-7908</ISSNLinking></MedlineJournalInfo><SupplMeshList><SupplMeshName Type="Disease" UI="C000657245">COVID-19</SupplMeshName><SupplMeshName Type="Organism" UI="C000656484">severe acute respiratory syndrome coronavirus 2</SupplMeshName></SupplMeshList><CitationSubset>H</CitationSubset><CitationSubset>IM</CitationSubset><CommentsCorrectionsList><CommentsCorrections RefType="ErratumIn"><RefSource>BMJ Glob Health. 2020 Jul;5(7):</RefSource><PMID 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IdType="pubmed">32171076</ArticleId></ArticleIdList></Reference></ReferenceList></PubmedData></pubmed><affiliations><list><country><li>France</li><li>Royaume-Uni</li><li>États-Unis</li></country><region><li>Angleterre</li><li>Illinois</li><li>Oxfordshire</li><li>Île-de-France</li></region><settlement><li>Oxford</li><li>Paris</li></settlement><orgName><li>Université Paris 1 Panthéon-Sorbonne</li><li>Université d'Oxford</li></orgName></list><tree><country name="Royaume-Uni"><region name="Angleterre"><name sortKey="Diop, Binta Zahra" sort="Diop, Binta Zahra" uniqKey="Diop B" first="Binta Zahra" last="Diop">Binta Zahra Diop</name></region><name sortKey="Pougue Biyong, John N" sort="Pougue Biyong, John N" uniqKey="Pougue Biyong J" first="John N" last="Pougué Biyong">John N. Pougué Biyong</name></country><country name="États-Unis"><region name="Illinois"><name sortKey="Ngom, Marieme" sort="Ngom, Marieme" uniqKey="Ngom M" first="Marieme" last="Ngom">Marieme Ngom</name></region></country><country name="France"><region name="Île-de-France"><name sortKey="Pougue Biyong, Clemence" sort="Pougue Biyong, Clemence" uniqKey="Pougue Biyong C" first="Clémence" last="Pougué Biyong">Clémence Pougué Biyong</name></region></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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