SARS-CoV-2 variants-associated outbreaks of COVID-19 in a tertiary institution, North-Central Nigeria: Implications for epidemic control.

The COVID-19 global pandemic is being driven by evolving SARS-CoV-2 variants with consequential implications on virus transmissibility, host immunity, and disease severity. Continuous molecular and genomic surveillance of the SARS-CoV-2 variants is therefore necessary for public health interventions toward the management of the pandemic. This study is a retrospective analysis of COVID-19 cases reported in a Nigerian tertiary institution from July to December 2021. In total, 705 suspected COVID-19 cases that comprised 547 students and 158 non-students were investigated by real time PCR (RT-PCR); of which 372 (~52.8%) tested positive for COVID-19. Using a set of selection criteria, 74 (~19.9%) COVID-19 positive samples were selected for next generation sequencing. Data showed that there were two outbreaks of COVID-19 within the university community over the study period, during which more females (56.8%) tested positive than males (47.8%) (p<0.05). Clinical data together with phylogenetic analysis suggested community transmission of SARS-CoV-2 through mostly asymptomatic and/or pre-symptomatic individuals. Confirmed COVID-19 cases were mostly mild, however, SARS-CoV-2 delta (77%) and omicron (4.1%) variants were implicated as major drivers of respective waves of infections during the study period. This study highlights the importance of integrated surveillance of communicable disease during outbreaks

The evolving SARS-CoV-2 epidemic in Africa: Insights from rapidly expanding genomic surveillance.

Investment in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) sequencing in Africa over the past year has led to a major increase in the number of sequences that have been generated and used to track the pandemic on the continent, a number that now exceeds 100,000 genomes. Our results show an increase in the number of African countries that are able to sequence domestically and highlight that local sequencing enables faster turnaround times and more-regular routine surveillance. Despite limitations of low testing proportions, findings from this genomic surveillance study underscore the heterogeneous nature of the pandemic and illuminate the distinct dispersal dynamics of variants of concern-particularly Alpha, Beta, Delta, and Omicron-on the continent. Sustained investment for diagnostics and genomic surveillance in Africa is needed as the virus continues to evolve while the continent faces many emerging and reemerging infectious disease threats. These investments are crucial for pandemic preparedness and response and will serve the health of the continent well into the 21st century

The evolving SARS-CoV-2 epidemic in Africa: Insights from rapidly expanding genomic surveillance

INTRODUCTION Investment in Africa over the past year with regard to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) sequencing has led to a massive increase in the number of sequences, which, to date, exceeds 100,000 sequences generated to track the pandemic on the continent. These sequences have profoundly affected how public health officials in Africa have navigated the COVID-19 pandemic. RATIONALE We demonstrate how the first 100,000 SARS-CoV-2 sequences from Africa have helped monitor the epidemic on the continent, how genomic surveillance expanded over the course of the pandemic, and how we adapted our sequencing methods to deal with an evolving virus. Finally, we also examine how viral lineages have spread across the continent in a phylogeographic framework to gain insights into the underlying temporal and spatial transmission dynamics for several variants of concern (VOCs). RESULTS Our results indicate that the number of countries in Africa that can sequence the virus within their own borders is growing and that this is coupled with a shorter turnaround time from the time of sampling to sequence submission. Ongoing evolution necessitated the continual updating of primer sets, and, as a result, eight primer sets were designed in tandem with viral evolution and used to ensure effective sequencing of the virus. The pandemic unfolded through multiple waves of infection that were each driven by distinct genetic lineages, with B.1-like ancestral strains associated with the first pandemic wave of infections in 2020. Successive waves on the continent were fueled by different VOCs, with Alpha and Beta cocirculating in distinct spatial patterns during the second wave and Delta and Omicron affecting the whole continent during the third and fourth waves, respectively. Phylogeographic reconstruction points toward distinct differences in viral importation and exportation patterns associated with the Alpha, Beta, Delta, and Omicron variants and subvariants, when considering both Africa versus the rest of the world and viral dissemination within the continent. Our epidemiological and phylogenetic inferences therefore underscore the heterogeneous nature of the pandemic on the continent and highlight key insights and challenges, for instance, recognizing the limitations of low testing proportions. We also highlight the early warning capacity that genomic surveillance in Africa has had for the rest of the world with the detection of new lineages and variants, the most recent being the characterization of various Omicron subvariants. CONCLUSION Sustained investment for diagnostics and genomic surveillance in Africa is needed as the virus continues to evolve. This is important not only to help combat SARS-CoV-2 on the continent but also because it can be used as a platform to help address the many emerging and reemerging infectious disease threats in Africa. In particular, capacity building for local sequencing within countries or within the continent should be prioritized because this is generally associated with shorter turnaround times, providing the most benefit to local public health authorities tasked with pandemic response and mitigation and allowing for the fastest reaction to localized outbreaks. These investments are crucial for pandemic preparedness and response and will serve the health of the continent well into the 21st century

Prevalence of chronic kidney disease and risk factors in North-Central Nigeria: a population-based survey

Abstract Background Chronic kidney disease (CKD) is a growing challenge in low- and middle-income countries, particularly in sub-Saharan Africa. There is insufficient population-based data on CKD in Nigeria that is required to estimate its true burden, and to design prevention and management strategies. The study aims to determine the prevalence of CKD and its risk factors in Nigeria. Methods We studied 8 urban communities in Kwara State, North-Central zone of Nigeria. Blood pressure, fasting blood sugar, urinalysis, weight, height, waist circumference and hip circumference were obtained. Albuminuria and kidney length were measured by ultrasound while estimated glomerular filtration rate (eGFR) was derived from serum creatinine, using chronic disease epidemiology collaboration (CKD-EPI) equation. Associations of risk factors with CKD were determined by multivariate logistic regression and expressed as adjusted odds ratio (aOR) with corresponding 95% confidence intervals. Results One thousand three hundred and fifty-three adults ≥18 years (44% males) with mean age of 44.3 ± 14.4 years, were screened. Mean kidney lengths were: right, 93.5 ± 7.0 cm and left, 93.4 ± 7.5 cm. The age-adjusted prevalence of hypertension was 24%; diabetes 4%; obesity 8.7%; albuminuria of &gt; 30 mg/L 7%; and dipstick proteinuria 13%. The age-adjusted prevalence of CKD by estimated GFR &lt; 60 ml/min/1.73m2 and/or Proteinuria was 12%. Diabetes (aOR 6.41, 95%CI = 3.50–11.73, P = 0.001), obesity (aOR 1.50, 95%CI = 1.10–2.05, P = 0.011), proteinuria (aOR 2.07, 95%CI = 1.05–4.08, P = 0.035); female sex (aOR 1.67, 95%CI = 1.47–1.89, P = 0.001); and age (aOR 1.89, 95%CI = 1.13–3.17, P = 0.015) were the identified predictors of CKD. Conclusions CKD and its risk factors are prevalent among middle-aged urban populations in North-Central Nigeria. It is common among women, fueled by diabetes, ageing, obesity, and albuminuria. These data add to existing regional studies of burden of CKD that may serve as template for a national prevention framework for CKD in Nigeria. One of the limitations of the study is that the participants were voluntary community dwellers and as such not representative for the community. The sample may thus have been subjected to selection bias possibly resulting in overestimation of CKD risk factors. </jats:sec

SARS-CoV-2 variants-associated outbreaks of COVID-19 in a tertiary institution, North-Central Nigeria: Implications for epidemic control

The COVID-19 global pandemic is being driven by evolving SARS-CoV-2 variants with consequential implications on virus transmissibility, host immunity, and disease severity. Continuous molecular and genomic surveillance of the SARS-CoV-2 variants is therefore necessary for public health interventions toward the management of the pandemic. This study is a retrospective analysis of COVID-19 cases reported in a Nigerian tertiary institution from July to December 2021. In total, 705 suspected COVID-19 cases that comprised 547 students and 158 non-students were investigated by real time PCR (RT-PCR); of which 372 (~52.8%) tested positive for COVID-19. Using a set of selection criteria, 74 (~19.9%) COVID-19 positive samples were selected for next generation sequencing. Data showed that there were two outbreaks of COVID-19 within the university community over the study period, during which more females (56.8%) tested positive than males (47.8%) (p&lt;0.05). Clinical data together with phylogenetic analysis suggested community transmission of SARS-CoV-2 through mostly asymptomatic and/or pre-symptomatic individuals. Confirmed COVID-19 cases were mostly mild, however, SARS-CoV-2 delta (77%) and omicron (4.1%) variants were implicated as major drivers of respective waves of infections during the study period. This study highlights the importance of integrated surveillance of communicable disease during outbreaks.</jats:p

SARS-CoV-2 variants-associated outbreaks of COVID-19 in a tertiary institution, North-Central Nigeria: Implications for epidemic control. S1 Data

Data for the PLOS ONE paper, "SARS-CoV-2 variants-associated outbreaks of COVID-19 in a tertiary institution, North-Central Nigeria: Implications for epidemic control"

SARS-CoV-2 variants-associated outbreaks of COVID-19 in a tertiary institution, North-Central Nigeria: Implications for epidemic control.

The COVID-19 global pandemic is being driven by evolving SARS-CoV-2 variants with consequential implications on virus transmissibility, host immunity, and disease severity. Continuous molecular and genomic surveillance of the SARS-CoV-2 variants is therefore necessary for public health interventions toward the management of the pandemic. This study is a retrospective analysis of COVID-19 cases reported in a Nigerian tertiary institution from July to December 2021. In total, 705 suspected COVID-19 cases that comprised 547 students and 158 non-students were investigated by real time PCR (RT-PCR); of which 372 (~52.8%) tested positive for COVID-19. Using a set of selection criteria, 74 (~19.9%) COVID-19 positive samples were selected for next generation sequencing. Data showed that there were two outbreaks of COVID-19 within the university community over the study period, during which more females (56.8%) tested positive than males (47.8%) (p<0.05). Clinical data together with phylogenetic analysis suggested community transmission of SARS-CoV-2 through mostly asymptomatic and/or pre-symptomatic individuals. Confirmed COVID-19 cases were mostly mild, however, SARS-CoV-2 delta (77%) and omicron (4.1%) variants were implicated as major drivers of respective waves of infections during the study period. This study highlights the importance of integrated surveillance of communicable disease during outbreaks

Symptomatology of COVID-19 cases.

(A) COVID-19 cases are plotted on the Y-axis, while symptoms of suspected and confirmed cases are plotted on the x-axis. Bars represent percentages of students (grey) and non-students (white). Figure shows χ2 tests between student and non-student groups of suspected COVID-19 cases (n = 705): flu-like (χ2 = 85.566, df = 1, p = 0.0000), gastrointestinal (χ2 = 7.487, df = 1, p = 0.006), non-specific (χ2 = 84.532, df = 1, p = 0.000), chemosensory symptoms (χ2 = 8.438, df = 1, p = 0.004) and asymptomatic (χ2 = 12.489, df = 1, p = 0.000) (ns = not significant, ***p2 test between student and non-student groups of confirmed COVID-19 cases (n = 372): flu-like symptoms (χ2 = 12.906, df = 1, p = 0.0000), gastrointestinal disorders (χ2 = 1.368, df = 1, p = 0.242), non-specific (χ2 = 12.344, df = 1, p = 0.0000), chemosensory symptoms (χ2 = 2.647, df = 1, p = 0.104), and asymptomatic (χ2 = 4.399, df = 1, p = 0.036) (ns = non-significant, *p<0.05, ****p<0.0001). (B) The RT-PCR Ct values of the SARS-CoV-2 RNA are shown in reverse order on the y-axis; while the number of days, post-onset of symptoms was plotted on the x-axis against the. Scatter plot shows data points represented as grey circles; the average Ct values are shown as thick short bars; error bars represent standard deviation; dashed line represents negative Ct cut-off. Pearson’s correlation (r) of confirmed COVID-19 cases against the duration of symptoms was non-linear (r = 0.05, p = 0.471, N = 249).</p

Fig 2 -

(A) Weekly positive COVID-19 samples showing distribution of variants in circulation during outbreaks. Figure shows the non-VOC/VOI (dark grey), Eta (pink), Delta (magenta) and Omicron (red) and other VOC/VOI (green). Non-sequenced samples termed not applicable (N/A) are also shown (light grey). (B) Percentage VOC/VOI Epi-weekly. non-VOC/VOI (dark grey), Eta (pink), Delta (magenta) and Omicron (red), and other VOC/VOI (green). (C) The maximum likelihood phylogenetic tree inferred from SARS-CoV-2 genomic isolates from the University community. Tips are labelled with specimen’s identification numbers corresponding with GISAID assigned accession numbers in S1 Table. The heatmap from first to third columns (left to right) indicates the accommodation type, variants of concern (VOC), and severity of infection respectively. Variant column was coloured with the major variant assigned by GISAID. The scale bar indicates distance substitution per site.</p

Sociodemographic characteristics of study participants.

Sociodemographic characteristics of study participants.</p