Seroprevalence of SARS-CoV-2 in four states of Nigeria in October 2020: A population-based household survey

The observed epidemiology of SARS-CoV-2 in sub-Saharan Africa has varied greatly from that in Europe and the United States, with much lower reported incidence. Population-based studies are needed to estimate true cumulative incidence of SARS-CoV-2 to inform public health interventions. This study estimated SARS-CoV-2 seroprevalence in four selected states in Nigeria in October 2020. We implemented a two-stage cluster sample household survey in four Nigerian states (Enugu, Gombe, Lagos, and Nasarawa) to estimate age-stratified prevalence of SARS-CoV-2 antibodies. All individuals in sampled households were eligible for interview, blood draw, and nasal/oropharyngeal swab collection. We additionally tested participants for current/recent malaria infection. Seroprevalence estimates were calculated accounting for the complex survey design. Across all four states, 10,629 (96·5%) of 11,015 interviewed individuals provided blood samples. The seroprevalence of SARS-CoV-2 antibodies was 25·2% (95% CI 21·8–28·6) in Enugu State, 9·3% (95% CI 7·0–11·5) in Gombe State, 23·3% (95% CI 20·5–26·4) in Lagos State, and 18·0% (95% CI 14·4–21·6) in Nasarawa State. Prevalence of current/recent malaria infection ranged from 2·8% in Lagos to 45·8% in Gombe and was not significantly related to SARS-CoV-2 seroprevalence. The prevalence of active SARS-CoV-2 infection in the four states during the survey period was 0·2% (95% CI 0·1–0·4). Approximately eight months after the first reported COVID-19 case in Nigeria, seroprevalence indicated infection levels 194 times higher than the 24,198 officially reported COVID-19 cases across the four states;however, most of the population remained susceptible to COVID-19 in October 2020.

Comparison of one single-antigen assay and three multi-antigen SARS-CoV-2 IgG assays in Nigeria.

Objectives: Determining an accurate estimate of SARS-CoV-2 seroprevalence has been challenging in African countries where malaria and other pathogens are endemic. We compared the performance of one single-antigen assay and three multi-antigen SARS-CoV-2 IgG assays in a Nigerian population endemic for malaria. Methods: De-identified plasma specimens from SARS-CoV-2 RT-PCR positive, dried blood spot (DBS) SARS-CoV-2 RT-PCR positive, and pre-pandemic negatives were used to evaluate the performance of the four SARS-CoV-2 assays (Tetracore, SARS2MBA, RightSign, xMAP). Results: Results showed higher sensitivity with the multi-antigen (81% (Tetracore), 96% (SARS2MBA), 85% (xMAP)) versus the single-antigen (RightSign (64%)) SARS-CoV-2 assay. The overall specificities were 98% (Tetracore), 100% (SARS2MBA and RightSign), and 99% (xMAP). When stratified based on <15 days to ≥15 days post-RT-PCR confirmation, the sensitivities increased from 75% to 88.2% for Tetracore; from 93% to 100% for the SARS2MBA; from 58% to 73% for RightSign; and from 83% to 88% for xMAP. With DBS, there was no positive increase after 15-28 days for the three assays (Tetracore, SARS2MBA, and xMAP). Conclusion: Multi-antigen assays performed well in Nigeria, even with samples with known malaria reactivity, and might provide more accurate measures of COVID-19 seroprevalence and vaccine efficacy.

Seroprevalence of SARS-CoV-2 in four states of Nigeria in October 2020: A population-based household survey.

The observed epidemiology of SARS-CoV-2 in sub-Saharan Africa has varied greatly from that in Europe and the United States, with much lower reported incidence. Population-based studies are needed to estimate true cumulative incidence of SARS-CoV-2 to inform public health interventions. This study estimated SARS-CoV-2 seroprevalence in four selected states in Nigeria in October 2020. We implemented a two-stage cluster sample household survey in four Nigerian states (Enugu, Gombe, Lagos, and Nasarawa) to estimate age-stratified prevalence of SARS-CoV-2 antibodies. All individuals in sampled households were eligible for interview, blood draw, and nasal/oropharyngeal swab collection. We additionally tested participants for current/recent malaria infection. Seroprevalence estimates were calculated accounting for the complex survey design. Across all four states, 10,629 (96·5%) of 11,015 interviewed individuals provided blood samples. The seroprevalence of SARS-CoV-2 antibodies was 25·2% (95% CI 21·8-28·6) in Enugu State, 9·3% (95% CI 7·0-11·5) in Gombe State, 23·3% (95% CI 20·5-26·4) in Lagos State, and 18·0% (95% CI 14·4-21·6) in Nasarawa State. Prevalence of current/recent malaria infection ranged from 2·8% in Lagos to 45·8% in Gombe and was not significantly related to SARS-CoV-2 seroprevalence. The prevalence of active SARS-CoV-2 infection in the four states during the survey period was 0·2% (95% CI 0·1-0·4). Approximately eight months after the first reported COVID-19 case in Nigeria, seroprevalence indicated infection levels 194 times higher than the 24,198 officially reported COVID-19 cases across the four states; however, most of the population remained susceptible to COVID-19 in October 2020.

Validation of xMAP SARS-CoV-2 Multi-Antigen IgG assay in Nigeria.

OBJECTIVE: There is a need for reliable serological assays to determine accurate estimates of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) seroprevalence. Most single target antigen assays have shown some limitations in Africa. To assess the performance of a multi-antigen assay, we evaluated a commercially available SARS-CoV-2 Multi-Antigen IgG assay for human coronavirus disease 2019 (COVID-19) in Nigeria. METHODS: Validation of the xMAP SARS-CoV-2 Multi-Antigen IgG assay was carried out using well-characterized SARS-CoV-2 reverse transcription polymerase chain reactive positive (97) and pre-COVID-19 pandemic (86) plasma panels. Cross-reactivity was assessed using pre-COVID-19 pandemic plasma specimens (213) from the 2018 Nigeria HIV/AIDS Indicator and Impact Survey (NAIIS). RESULTS: The overall sensitivity of the xMAP SARS-CoV-2 Multi-Antigen IgG assay was 75.3% [95% CI: 65.8%- 82.8%] and specificity was 99.0% [95% CI: 96.8%- 99.7%]. The sensitivity estimate increased to 83.3% [95% CI: 70.4%- 91.3%] for specimens >14 days post-confirmation of diagnosis. However, using the NAIIS pre-pandemic specimens, the false positivity rate was 1.4% (3/213). CONCLUSIONS: Our results showed overall lower sensitivity and a comparable specificity with the manufacturer's validation. There appears to be less cross-reactivity with NAIIS pre-pandemic COVID-19 specimens using the xMAP SARS-CoV-2 Multi-Antigen IgG assay. In-country SARS-CoV-2 serology assay validation can help guide the best choice of assays in Africa.

Comparative performance of SARS-CoV-2 real-time PCR diagnostic assays on samples from Lagos, Nigeria.

A key element in containing the spread of the SARS-CoV-2 infection is quality diagnostics which is affected by several factors. We now report the comparative performance of five real-time diagnostic assays. Nasopharyngeal swab samples were obtained from persons seeking a diagnosis for SARS-CoV-2 infection in Lagos, Nigeria. The comparison was performed on the same negative, low, and high-positive sample set, with viral RNA extracted using the Qiagen Viral RNA Kit. All five assays are one-step reverse transcriptase real-time PCR assays. Testing was done according to each assay's manufacturer instructions for use using real-time PCR platforms. 63 samples were tested using the five qPCR assays, comprising of 15 negative samples, 15 positive samples (Ct = 16-30; one Ct = 35), and 33 samples with Tib MolBiol E-gene Ct value ranging from 36-41. All assays detected all high positive samples correctly. Three assays correctly identified all negative samples while two assays each failed to correctly identify one different negative sample. The consistent detection of positive samples at different Ct/Cq values gives an indication of when to repeat testing and/or establish more stringent in-house cut-off value. The varied performance of different diagnostic assays, mostly with emergency use approvals, for a novel virus is expected. Comparative assays' performance reported may guide laboratories to determine both their repeat testing Ct/Cq range and/or cut-off value.

Full length genomic sanger sequencing and phylogenetic analysis of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) in Nigeria.

In an outbreak, effective detection of the aetiological agent(s) involved using molecular techniques is key to efficient diagnosis, early prevention and management of the spread. However, sequencing is necessary for mutation monitoring and tracking of clusters of transmission, development of diagnostics and for vaccines and drug development. Many sequencing methods are fast evolving to reduce test turn-around-time and to increase through-put compared to Sanger sequencing method; however, Sanger sequencing remains the gold standard for clinical research sequencing with its 99.99% accuracy This study sought to generate sequence data of SARS-CoV-2 using Sanger sequencing method and to characterize them for possible site(s) of mutations. About 30 pairs of primers were designed, synthesized, and optimized using endpoint PCR to generate amplicons for the full length of the virus. Cycle sequencing using BigDye Terminator v.3.1 and capillary gel electrophoresis on ABI 3130xl genetic analyser were performed according to the manufacturers' instructions. The sequence data generated were assembled and analysed for variations using DNASTAR Lasergene 17 SeqMan Ultra. Total length of 29,760bp of SARS-CoV-2 was assembled from the sample analysed and deposited in GenBank with accession number: MT576584. Blast result of the sequence assembly shows a 99.97% identity with the reference sequence. Variations were noticed at positions: nt201, nt2997, nt14368, nt16535, nt20334, and nt28841-28843, which caused amino acid alterations at the S (aa614) and N (aa203-204) regions. The mutations observed at S and N-gene in this study may be indicative of a gradual changes in the genetic coding of the virus hence, the need for active surveillance of the viral genome.