Epidemiology and genetic diversity of SARS-CoV-2 lineages circulating in Africa

Okoh, Olayinka S.; Nii-Trebi, Nicholas Israel; Jakkari, Abdulrokeeb; Olaniran, Tosin Titus; Senbadejo, Tosin Yetunde; Kafintu-kwashie, Anna Aba; Ganiyu, Tajudeen Oladunni; Akaninyene, Ifiokakaninyene Ekpo; Ezediuno, Louis Odinakaose; Adeosun, Idowu Jesulayomi; Ockiya, Michael Asebake; Jimah, Esther Moradeyo; Spiro, David; Oladipo, Elijah Kolawole; Trovão, Nídia Sequeira

doi:10.1016/j.isci.2022.103880

Cited by 7 publications

(3 citation statements)

References 79 publications

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“…Here, we have observed that a more comprehensive subsampling strategy that includes as many traits as possible (date, location, PGRHA) yields the best result in retaining the original dataset properties, as demonstrated by the high similarities of the transmission networks between the HIV-1 subtype C full and pol, and the fullCP and polCP datasets, respectively (Figure 4). Furthermore, studies that take into account sampling bias are often limited to a single replicate of a particular subsampling method (11, 13, 42, 43). We have demonstrated that this likely does not have harmful implications to the interpretation of the results as there is little variation of the overall tree topology across subsampling replicates (Figure 3), as well as in the ancestral trait reconstruction (Figure 4).…”

Section: Discussionmentioning

confidence: 99%

Multiple-trait subsampling for optimized ancestral trait reconstruction

Trovão

Wertheim

et al. 2022

Preprint

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Large datasets along with sampling bias represent a challenge for phylodynamic reconstructions, particularly when the study data are obtained from various heterogeneous sources and/or through convenience sampling. In this study, we evaluate the presence of unbalanced sampled distribution by collection date, location, and risk group of HIV-1 subtype C using a comprehensive subsampling strategy, and assess their impact on the reconstruction of the viral spatial and risk group dynamics using phylogenetic comparative methods. Our study shows that the most suitable dataset for ancestral trait reconstruction can be obtained through subsampling by collection date, location, and risk group, particularly using multigene datasets. We also demonstrate that sampling bias is inflated when considerable information for a given trait is unavailable or of poor quality, as we observed for the risk group in the analysis of HIV-1 subtype C. In conclusion, we suggest that, even if traits are not well recorded, including them deliberately optimizes the representativeness of the original dataset rather than completely excluding them. Therefore, we advise the inclusion of as many traits as possible with the aid of subsampling approaches in order to optimize the dataset for phylodynamic analysis while reducing the computational burden. This will benefit research communities investigating the evolutionary and spatiotemporal patterns of infectious diseases.

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Section: Discussionmentioning

confidence: 99%

Multiple-trait subsampling for optimized ancestral trait reconstruction

Trovão

Wertheim

et al. 2022

Preprint

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“…The use of genomic data to identify which SARS-CoV-2 strains predominated in Nigeria's first and second waves would have aided the understanding of these findings. However, African countries, including Nigeria, have contributed few SARS-CoV-2 genomic data towards the global pool [24].…”

Section: Summary and Interpretation Of Key Findingsmentioning

confidence: 99%

COVID-19 mortality rate and its associated factors during the first and second waves in Nigeria

Elimian

Musah

King

et al. 2022

PLOS Glob Public Health

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COVID-19 mortality rate has not been formally assessed in Nigeria. Thus, we aimed to address this gap and identify associated mortality risk factors during the first and second waves in Nigeria. This was a retrospective analysis of national surveillance data from all 37 States in Nigeria between February 27, 2020, and April 3, 2021. The outcome variable was mortality amongst persons who tested positive for SARS-CoV-2 by Reverse-Transcriptase Polymerase Chain Reaction. Incidence rates of COVID-19 mortality was calculated by dividing the number of deaths by total person-time (in days) contributed by the entire study population and presented per 100,000 person-days with 95% Confidence Intervals (95% CI). Adjusted negative binomial regression was used to identify factors associated with COVID-19 mortality. Findings are presented as adjusted Incidence Rate Ratios (aIRR) with 95% CI. The first wave included 65,790 COVID-19 patients, of whom 994 (1∙51%) died; the second wave included 91,089 patients, of whom 513 (0∙56%) died. The incidence rate of COVID-19 mortality was higher in the first wave [54∙25 (95% CI: 50∙98–57∙73)] than in the second wave [19∙19 (17∙60–20∙93)]. Factors independently associated with increased risk of COVID-19 mortality in both waves were: age ≥45 years, male gender [first wave aIRR 1∙65 (1∙35–2∙02) and second wave 1∙52 (1∙11–2∙06)], being symptomatic [aIRR 3∙17 (2∙59–3∙89) and 3∙04 (2∙20–4∙21)], and being hospitalised [aIRR 4∙19 (3∙26–5∙39) and 7∙84 (4∙90–12∙54)]. Relative to South-West, residency in the South-South and North-West was associated with an increased risk of COVID-19 mortality in both waves. In conclusion, the rate of COVID-19 mortality in Nigeria was higher in the first wave than in the second wave, suggesting an improvement in public health response and clinical care in the second wave. However, this needs to be interpreted with caution given the inherent limitations of the country’s surveillance system during the study.

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“…Real-time genomic surveillance of circulating SARS-CoV-2 in different parts of the world has been critical for monitoring the evolution of the virus ( 2 , 3 ). Phylogenetic analyses of SARS-CoV-2 genomes have identified emerging variants over the course of the pandemic with shifting dominance over time ( 4 – 6 ). Some of these variants have been classified as variants of interest (VOIs) and variants of concern (VOCs) based on their potential for rapid spread, ability to cause severe disease, capacity to evade detection, and natural-/vaccine-related immunity, as well as their ability to evade therapeutics ( 7 ).…”

Section: Introductionmentioning

confidence: 99%

Evolutionary and spatiotemporal analyses reveal multiple introductions and cryptic transmission of SARS-CoV-2 VOC/VOI in Malta

Trovao,

Pan,

Goel

et al. 2023

Microbiol Spectr

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Genomic surveillance and epidemiology have shed light on the viral diversity driving coronavirus disease 2019 (COVID-19) outbreaks and are important during waves of highly transmissible and immune-escaping variants of interest or of concern (VOCs). We analyzed the epidemiological data of the understudied country of Malta and related the patterns observed with viral genetic sequences obtained through the surveillance system headed by the Mater Dei Hospital and the University of Malta. We reconstructed the evolutionary history and spatiotemporal dynamics of Maltese severe acute respiratory syndrome coronavirus 2 viruses using a phylodynamics framework. Our findings suggest that the number of cases associated with B.1.1.7/Alpha, B.1.617.2.X/Delta, and B.1.1.529.X/Omicron VOCs was nine times higher than those associated with wild-type variants. The positivity rates in Malta remained low to moderate (<10%). A combination of public health interventions appeared to have allowed Malta to mitigate the impact of COVID-19. Our phylodynamic reconstruction traced most of the 173 viral introductions inferred to countries in Northern Europe, which is consistent with flight connectivity patterns. We also observed prolonged periods of cryptic transmission (median = 102 days) until expansion into larger outbreaks. These larger outbreaks were more easily detected by the intermittent genomic surveillance in Malta, characterized by periods of sequencing hiatus. Our study demonstrates that integrating epidemiological and genomic data are crucial for uncovering the COVID-19 dynamics of understudied locations, particularly when genomic surveillance is suboptimal. Accordingly, strengthening the genomic surveillance system in Malta should help in the earlier detection of introductions and minimize viral expansion in the country while informing public health interventions. IMPORTANCE Our study provides insights into the evolution of the coronavirus disease 2019 (COVID-19) pandemic in Malta, a highly connected and understudied country. We combined epidemiological and phylodynamic analyses to analyze trends in the number of new cases, deaths, tests, positivity rates, and evolutionary and dispersal patterns from August 2020 to January 2022. Our reconstructions inferred 173 independent severe acute respiratory syndrome coronavirus 2 introductions into Malta from various global regions. Our study demonstrates that characterizing epidemiological trends coupled with phylodynamic modeling can inform the implementation of public health interventions to help control COVID-19 transmission in the community.

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Epidemiology and genetic diversity of SARS-CoV-2 lineages circulating in Africa

Cited by 7 publications

References 79 publications

Multiple-trait subsampling for optimized ancestral trait reconstruction

Multiple-trait subsampling for optimized ancestral trait reconstruction

COVID-19 mortality rate and its associated factors during the first and second waves in Nigeria

Evolutionary and spatiotemporal analyses reveal multiple introductions and cryptic transmission of SARS-CoV-2 VOC/VOI in Malta

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