Genomic epidemiology of SARS-CoV-2 infections in The Gambia: an analysis of routinely collected surveillance data between March, 2020, and January, 2022

Background: COVID-19, caused by SARS-CoV-2, is one of the deadliest pandemics of the past 100 years. Genomic sequencing has an important role in monitoring of the evolution of the virus, including the detection of new viral variants. We aimed to describe the genomic epidemiology of SARS-CoV-2 infections in The Gambia. Methods: Nasopharyngeal or oropharyngeal swabs collected from people with suspected cases of COVID-19 and international travellers were tested for SARS-CoV-2 with standard RT-PCR methods. SARS-CoV-2-positive samples were sequenced according to standard library preparation and sequencing protocols. Bioinformatic analysis was done using ARTIC pipelines and Pangolin was used to assign lineages. To construct phylogenetic trees, sequences were first stratified into different COVID-19 waves (waves 1–4) and aligned. Clustering analysis was done and phylogenetic trees constructed. Findings: Between March, 2020, and January, 2022, 11 911 confirmed cases of COVID-19 were recorded in The Gambia, and 1638 SARS-CoV-2 genomes were sequenced. Cases were broadly distributed into four waves, with more cases during the waves that coincided with the rainy season (July–October). Each wave occurred after the introduction of new viral variants or lineages, or both, generally those already established in Europe or in other African countries. Local transmission was higher during the first and third waves (ie, those that corresponded with the rainy season), in which the B.1.416 lineage and delta (AY.34.1) were dominant, respectively. The second wave was driven by the alpha and eta variants and the B.1.1.420 lineage. The fourth wave was driven by the omicron variant and was predominantly associated with the BA.1.1 lineage. Interpretation: More cases of SARS-CoV-2 infection were recorded in The Gambia during peaks of the pandemic that coincided with the rainy season, in line with transmission patterns for other respiratory viruses. The introduction of new lineages or variants preceded epidemic waves, highlighting the importance of implementing well structured genomic surveillance at a national level to detect and monitor emerging and circulating variants. Funding: Medical Research Unit The Gambia at London School of Hygiene & Tropical Medicine, UK Research and Innovation, WHO

The ASOS Surgical Risk Calculator: development and validation of a tool for identifying African surgical patients at risk of severe postoperative complications

Background: The African Surgical Outcomes Study (ASOS) showed that surgical patients in Africa have a mortality twice the global average. Existing risk assessment tools are not valid for use in this population because the pattern of risk for poor outcomes differs from high-income countries. The objective of this study was to derive and validate a simple, preoperative risk stratification tool to identify African surgical patients at risk for in-hospital postoperative mortality and severe complications. Methods: ASOS was a 7-day prospective cohort study of adult patients undergoing surgery in Africa. The ASOS Surgical Risk Calculator was constructed with a multivariable logistic regression model for the outcome of in-hospital mortality and severe postoperative complications. The following preoperative risk factors were entered into the model; age, sex, smoking status, ASA physical status, preoperative chronic comorbid conditions, indication for surgery, urgency, severity, and type of surgery. Results: The model was derived from 8799 patients from 168 African hospitals. The composite outcome of severe postoperative complications and death occurred in 423/8799 (4.8%) patients. The ASOS Surgical Risk Calculator includes the following risk factors: age, ASA physical status, indication for surgery, urgency, severity, and type of surgery. The model showed good discrimination with an area under the receiver operating characteristic curve of 0.805 and good calibration with c-statistic corrected for optimism of 0.784. Conclusions: This simple preoperative risk calculator could be used to identify high-risk surgical patients in African hospitals and facilitate increased postoperative surveillance. © 2018 British Journal of Anaesthesia. Published by Elsevier Ltd. All rights reserved.Medical Research Council of South Africa gran