http://socictopen.socict.org/files/original/569eb4bd053f08809fab613d974dd1d1.pdf b36fd23f081e3c2314cef067d8bd8d73 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Coronavirus Description An account of the resource Dominio científico: Coronavirus Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource The potential effects of widespread community transmission of SARS-CoV-2 infection in the World Health Organization African Region: a predictive model. Creator An entity primarily responsible for making the resource Michel Yao, Joseph Waogodo Cabore, Humphrey Cyprian Karamagi, Hillary Kipruto, James Avoka Asamani, Benson Droti, Aminata Binetou Wahebine Seydi, Regina Titi-Ofei, Benido Impouma, Zabulon Yoti, Felicitas Zawaira, Prosper Tumusiime, Ambrose Talisuna, Francis Chisaka Kasolo, Matshidiso R Moeti Description An account of the resource The spread of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) has been unprecedented in its speed and effects. Interruption of its transmission to prevent widespread community transmission is critical because its effects go beyond the number of COVID-19 cases and deaths and affect the health system capacity to provide other essential services. Highlighting the implications of such a situation, the predictions presented here are derived using a Markov chain model, with the transition states and country specific probabilities derived based on currently available knowledge. A risk of exposure, and vulnerability index are used to make the probabilities country specific. The results predict a high risk of exposure in states of small size, together with Algeria, South Africa and Cameroon. Nigeria will have the largest number of infections, followed by Algeria and South Africa. Mauritania would have the fewest cases, followed by Seychelles and Eritrea. Per capita, Mauritius, Seychelles and Equatorial Guinea would have the highest proportion of their population affected, while Niger, Mauritania and Chad would have the lowest. Of the World Health Organization's 1 billion population in Africa, 22% (16%-26%) will be infected in the first year, with 37 (29 - 44) million symptomatic cases and 150 078 (82 735-189 579) deaths. There will be an estimated 4.6 (3.6-5.5) million COVID-19 hospitalisations, of which 139 521 (81 876-167 044) would be severe cases requiring oxygen, and 89 043 (52 253-106 599) critical cases requiring breathing support. The needed mitigation measures would significantly strain health system capacities, particularly for secondary and tertiary services, while many cases may pass undetected in primary care facilities due to weak diagnostic capacity and non-specific symptoms. The effect of avoiding widespread and sustained community transmission of SARS-CoV-2 is significant, and most likely outweighs any costs of preventing such a scenario. Effective containment measures should be promoted in all countries to best manage the COVID-19 pandemic. Date A point or period of time associated with an event in the lifecycle of the resource 2020 Subject The topic of the resource Epidemiology, Mathematical modelling, health systems Identifier An unambiguous reference to the resource within a given context DOI: 10.1136/bmjgh-2020-002647 Source A related resource from which the described resource is derived BMJ global health