About: Seeding COVID-19 across sub-Saharan Africa: an analysis of reported importation events across 40 countries

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About: Seeding COVID-19 across sub-Saharan Africa: an analysis of reported importation events across 40 countries Goto Sponge NotDistinct Permalink

An Entity of Type : schema:ScholarlyArticle, within Data Space : covidontheweb.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	Seeding COVID-19 across sub-Saharan Africa: an analysis of reported importation events across 40 countries
Creator	Althouse, Benjamin Bedson, Jamie Hagedorn, Brittany Lin Ouédraogo, Andre Mistry, Dina Noori, Navideh Selvaraj, Prashanth Skrip, Laura
Source	MedRxiv
abstract	Background: The first case of COVID-19 in sub-Saharan Africa (SSA) was reported by Nigeria on February 27, 2020. While case counts in the entire region remain considerably less than those being reported by individual countries in Europe, Asia, and the Americas, SSA countries remain vulnerable to significant COVID morbidity and mortality due to systemic healthcare weaknesses, less financial resources and infrastructure to address the new crisis, and untreated comorbidities. Variation in preparedness and response capacity as well as in data availability has raised concerns about undetected transmission events. Methods: Confirmed cases reported by SSA countries were line-listed to capture epidemiological details related to early transmission events into and within countries. Data were retrieved from publicly available sources, including institutional websites, situation reports, press releases, and social media accounts, with supplementary details obtained from news articles. A data availability score was calculated for each imported case in terms of how many indicators (sex, age, travel history, date of arrival in country, reporting date of confirmation, and how detected) could be identified. We assessed the relationship between time to first importation and overall Global Health Security Index (GHSI) using Cox regression. K-means clustering grouped countries according to healthcare capacity and health and demographic risk factors. Results: A total of 2417 confirmed cases of COVID-19 were reported by 40 countries in sub-Saharan Africa during the 30 days after the first known introduction to the region. Out of the 876 cases for which information was publicly available, 677 (77.3%) were considered importation events. At the regional level, imported cases tended to be male (67.3%), were a median 43.0 years old (Range: 6 weeks - 88 years), and most frequently had recent travel history from Europe (43.3%). The median time to reporting an introduction was 19 days; a country's time to report its first importation was not related to GHSI. Mean data availability scores were lowest for countries that had, on average, the highest case fatality rates, lowest healthcare capacity, and highest probability of premature death due to non-communicable diseases. Conclusion: Countries with systemic, demographic, and pre-existing health vulnerabilities to severe COVID-related morbidity and mortality are less likely to report any cases or are reporting with limited public availability of information. Reporting of information on COVID detection and response efforts, as well as on trends in non-COVID illness and care-seeking behavior, is critical to assessing direct and indirect consequences and capacity needs in resource-constrained settings. Such assessments aid in the ability to make data-driven decisions about interventions, country priorities, and risk assessment.
has issue date	2020-04-06(xsd:dateTime)
bibo:doi	10.1101/2020.04.01.20050203
has license	medrxiv
sha1sum (hex)	a8e64e868372614e2c7adb12a9c8a319830a5500
schema:url	https://doi.org/10.1101/2020.04.01.20050203
resource representing a document's title	Seeding COVID-19 across sub-Saharan Africa: an analysis of reported importation events across 40 countries
resource representing a document's body	covid:a8e64e868372614e2c7adb12a9c8a319830a5500#body_text
is schema:about of	named entity 'morbidity' named entity 'response' named entity 'ANALYSIS' covid:arg/a8e64e868372614e2c7adb12a9c8a319830a5500 named entity 'CRISIS' named entity 'SEEDING' named entity 'HEALTHCARE' named entity 'ADDRESS' named entity 'MORBIDITY AND MORTALITY' named entity 'DATA' named entity 'BEING' named entity 'BACKGROUND' named entity 'NEW' named entity 'ACROSS' named entity 'ASIA' named entity 'EUROPE' named entity 'COUNTRIES' named entity 'SUB-SAHARAN AFRICA' named entity 'REPORTED' named entity 'IMPORTATION' named entity 'VARIATION' named entity 'FEBRUARY' named entity 'COUNTS' named entity 'VULNERABLE' named entity 'AMERICAS' named entity 'TRANSMISSION' named entity 'COMORBIDITIES' named entity 'COVID-19' named entity 'AVAILABILITY' named entity 'UNTREATED' named entity 'SUB-SAHARAN AFRICA' named entity 'SIGNIFICANT' named entity 'REPORTED BY' named entity 'DUE TO' named entity 'PREPAREDNESS' named entity 'CAPACITY' named entity 'RAISED' named entity 'INDIVIDUAL' named entity 'LESS THAN' named entity 'EVENTS' named entity 'RESPONSE' named entity 'ENTIRE' named entity 'CASE' named entity 'ABOUT' named entity 'NIGERIA' named entity 'CONCERNS' named entity 'COVID-19' named entity 'SSA' named entity 'SYSTEMIC' named entity 'REGION' named entity 'FINANCIAL RESOURCES' named entity 'WEAKNESSES' named entity 'EVENTS' named entity 'INFRASTRUCTURE' named entity 'COUNTRIES' named entity 'Americas' named entity 'capacity' named entity 'Nigeria' named entity 'data availability' named entity 'Nigeria' named entity 'infectious disease' named entity 'SSA' named entity 'COVID' named entity 'COVID' named entity 'peer-reviewed' named entity 'preprint' named entity 'public health' named entity 'epidemiological' named entity 'community spread' named entity 'SARS-CoV' named entity 'Rwanda' named entity 'share data' named entity 'morbidity' named entity 'preprint' named entity 'preprint' named entity 'generalized linear model' named entity 'reported cases' named entity 'preprint'

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