Association of Public Health Laboratories

Abstract Background SARS-CoV-2 genomic surveillance data remain limited in most low and middle-income countries (LMICs), resulting in significant gaps in the understanding of variant circulation and evolution. Wastewater-based epidemiology (WBE) presents a non-invasive, cost-effective, and population-representative surveillance approach that can complement clinical testing, particularly in densely populated urban informal settlements with limited healthcare access. This study aimed to pilot wastewater-based genomic surveillance as a multifaceted public health tool in Kenya. Methods A prospective study was conducted using wastewater samples collected from two WHO-validated environmental surveillance sites — Eastleigh A (Kamukunji sub-county) and Mathare (Starehe sub-county) — in Nairobi, Kenya, between December 2022 and October 2023. A total of 272 samples were collected using Moore swabs at a frequency of two to three times per week. Samples were concentrated using Nanotrap® Magnetic Virus Particles, and nucleic acid was extracted using the Qiagen QIAamp Viral RNA Mini Kit. SARS-CoV-2 was detected using RT-PCR (TaqPath™ COVID-19 CE-IVD RT-PCR Kit). Library preparation for whole-genome sequencing was performed using the Illumina COVIDSeq kit, and sequencing was conducted on the Illumina MiSeq platform. Bioinformatic analysis was performed using Terra.bio and RStudio, and phylogenetic analysis included sequences abstracted from GISAID. Results Of 272 samples, 238 (87.5%) tested positive with a cycle threshold (Ct) value of less than 36. Genomic analysis of 181 sequences identified Omicron as the predominant circulating variant, detected in 59% of samples. Other variants included XBB (16%), XBB.2.3(10%), XBB.1.9.X (5%), and additional minor variants. These findings were concordant with clinical sequencing data from Kenya over the same period. Conclusions Wastewater-based genomic surveillance reliably reflected SARS-CoV-2 variant trends observed in clinical data. This approach provides early signals of variant emergence and evolution, offering a cost-effective complement to clinical surveillance in resource-limited settings.

Objectives. Vibrio cholerae remains a significant public health threat in Africa, with antimicrobial resistance (AMR) complicating treatment. This study leverages whole-genome sequencing (WGS) of V. cholerae isolates from Côte d'Ivoire, Ghana, Zambia and South Africa to assess genomic diversity, AMR profiles and virulence, demonstrating the utility of WGS for enhanced surveillance within the PulseNet Africa network.Methods. We analysed Vibrio isolates from clinical and environmental sources (2010-2024) using Oxford Nanopore sequencing and hybracter assembly. Phylogenetic analysis, MLST, virulence and AMR gene detection were performed using Terra, Pathogenwatch and Cloud Infrastructure for Microbial Bioinformatics platforms, with comparisons against 118 global reference genomes for broader genomic context.Results. Of 79 high-quality assemblies, 67 were confirmed as V. cholerae, with serogroup O1 accounting for the majority (43 out of 67, 67%). ST69 accounted for 60% (40 out of 67) of isolates, with 8 sequence types identified overall. Thirty-seven isolates formed distinct sub-clades within AFR12 and AFR15 O1 lineages, suggesting local clonal expansions. AMR gene analysis revealed genes associated with resistance to trimethoprim in 96% of isolates and genes associated with resistance to quinolones in 83%, while genes associated with resistance to azithromycin, rifampicin and tetracycline remained low (≤7%). A significant proportion of the serogroup O1 isolates (41 out of 43, 95%) harboured resistance genes in at least 3 antibiotic classes.Conclusions. This study highlights significant genetic diversity and AMR prevalence in African V. cholerae isolates, with expanding AFR12 and AFR15 clades in the region. The widespread presence of genes associated with resistance to trimethoprim and quinolones raises concerns for treatment efficacy, although azithromycin and tetracycline remain viable options. WGS enables precise identification of species and genotyping, reinforcing PulseNet Africa's pivotal role in advancing genomic surveillance and enabling timely public health responses to cholera outbreaks.

Whole genome sequencing (WGS) marks a turning point for outbreak investigations for microorganisms related to public health matters, like Legionella pneumophila (Lp). Here, we evaluated the available Lp WGS typing tools for isolates of previously documented Belgian outbreaks, as well as small groups of related and non-related isolates. One reference strain and 77 clinical and environmental isolates were evaluated. Seven isolates belong to a Sequence Type (ST) 36 outbreak in 1999 and sixteen (ten clinical, two matching environmental and four non-related controls) belong to another ST1 outbreak in 1985–1987. The remaining isolates belong to small groups of related and non-related isolates of diverse ST’s. WGS was performed and data were analysed using whole genome (wg) and core genome (cg) multilocus sequence typing (MLST) with “Ridom SeqSphere + ” (cgMLST), “Applied Maths-Bionumerics” (wgMLST) and the 50 loci cgMLST (CDC/ESGLI_ESCMID). Results of the three tools were concordant with the traditional Sequence Based Typing (SBT). The known outbreaks and small clusters could be detected and clear discrimination of ST1 non-related isolates was obtained. In addition, the 50 loci cgMLST allowed to classify the isolates into subtypes because almost all the 50 genes could be called in all the analysed isolates, which was not achieved by the other tools. This is a big advantage in terms of standardisation and comparison between laboratories for future epidemiological investigations. WGS allowed to analyse a large volume of samples and generated more accurate conclusions for outbreak investigations compared to other typing methods due to its higher discriminatory power and throughput.

The world has seen unprecedented gains in the global genomic surveillance capacities for pathogens with pandemic and epidemic potential within the last 4 years. To strengthen and sustain the gains made, WHO is working with countries and partners to implement the Global Genomic Surveillance Strategy for Pathogens with Pandemic and Epidemic Potential 2022-2032. A key technical product developed through these multi-agency collaborative efforts is a genomics costing tool (GCT), as sought by many countries. This tool was developed by five institutions - Association of Public Health Laboratories, FIND, The Global Fund to Fight AIDS, Tuberculosis and Malaria, UK Health Security Agency, and the World Health Organization. These institutions developed the GCT to support financial planning and budgeting for SARS-CoV-2 next-generation sequencing activities, including bioinformatic analysis. The tool costs infrastructure, consumables and reagents, human resources, facility and quality management. It is being used by countries to (1) obtain costs of routine sequencing and bioinformatics activities, (2) optimize available resources, and (3) build an investment case for the scale-up or establishment of sequencing and bioinformatics activities. The tool has been validated and is available in English and Russian at https://www.who.int/publications/i/item/9789240090866. This paper aims to highlight the rationale for developing the tool, describe the process of the collaborative effort in developing the tool, and describe the utility of the tool to countries.

Newborn screening (NBS) for Severe Combined Immunodeficiency (SCID) by measurement of T-cell receptor excision circles (TRECs) successfully identifies newborns with SCID and severe T-cell lymphopenia, as intended. At the same time, NBS programs face the challenge of false positive results, with a disproportionately high number in the premature newborn population. This study evaluates TREC values and SCID screening outcomes in premature newborns and elucidates evidence-based SCID screening practices that reduce unnecessary follow-up activities in this population. De-identified individual SCID newborn screening data and aggregate SCID screening data were obtained from seven states across the US for babies born between 2018 and 2020. Relevant statistics were performed on data pooled from these states to quantify screening performance metrics and clinical impact on various birth and gestational age categories of newborns. The data were normalized using multiples-of-the-median (MoM) values to allow for the aggregation of data across states. The aggregation of NBS data across a range of NBS programs highlighted the trajectory of TREC values over time, both between and within newborns, and provides evidence for improved SCID screening recommendations in the premature and low birth weight population.

Summary Human immunodeficiency virus (HIV) is the pathogen responsible for acquired immunodeficiency syndrome (AIDS) and continues to be a significant global public health issue. HIV can be managed with antiretroviral (ART) drug treatments by suppressing the viral replication of HIV within the infected individual. Unfortunately, due to the nature of the virus, drug resistance can occur, making the ART drug no longer effective. An amplicon-based assay, such as the Illumina COVIDSeqTM Assay (RUO), was adopted to sequence the entire genome of the HIV-1 virus. We modified theIllumina COVIDSeqTM Assay (RUO) protocol to prepare HIV-1 libraries and sequenced on the Illumina MiSeq. The sequencing data was analyzed by Terra.bio and Exatype NGS. This study demonstrated the utility of the assay to efficientlysequence near-complete genome of HIV-1 virus up to 488 viral copies and provide accurate information for drug resistance detection. Background According to the World Health Organization, an estimated 39.0 million [33.1–45.7 million] people live with HIV at the end of 2022 [1]. UNAIDS reported that 40.4 million [32.9 million–51.3 million] people have died from AIDS-related illnesses since the start of the epidemic [2]. HIV drug resistance testing is crucial for managing and preventing ARV failures for persons undergoing treatment or drug-naive individuals [3]. Traditionally, HIV drug resistance testing is performed by Sanger sequencing of specific regions within the HIV genome to identify drug resistance mutations. Next-generation sequencing (NGS) technologies have recently been implemented to improve sensitivity and reproducibility and reduce the cost per sample by multiplexing. In this study, the Oregon State Public Health Laboratory (OSPHL), in collaboration with the Association of Public Health Laboratory (APHL), evaluated the performance of the Illumina COVIDSeqTM Assay (RUO) for HIV complete genome sequencing (HIVSeq). We describe a step-by-step HIV-1 virus genome sequencing protocol that leverages the Illumina COVIDSeqTM Assay (RUO), with reagents remaining the same. The modified protocol utilizes HIV-1 primers designed through primal scheme. We demonstrate that whole genome sequencing of the HIV-1 virus can be achieved with extensive sequencing coverage for viral copies up to 488. Thereby expanding the use of the Illumina COVIDSeqTM Assay (RUO)beyond SARS-CoV-2. Results To evaluate the performance of the assay, HIV-1 strain: IIIB positive culture control from ZeptoMetrix (Part #v0801032CF) was extracted and serially diluted to the following concentration: 10ᶺ(-1), 10ᶺ(-2), 10ᶺ(-3), 10ᶺ(-4), 10ᶺ(-5), and 10ᶺ(-6). Library preparation of the diluted viral nucleic extracts was sequenced on the Illumina MiSeq 2 x 150 bp. Viral copies ranged from 48.8 to 4,880,000 viral RNA copies/ul. We evaluated the assay's performance for accuracy, precision,sensitivity, specificity, and limit of detection. HIV-1 primersdesigned with Primal Scheme: Complete Primers under MATERIALS section. To verify the coverage and detect drug-resistant mutations of all samples, we used FASTQ files exported from the MiSeq instrument. We uploaded them to the Terra platform, used the Illumina PE TheiaCov workflow developed by Theiagen, and mapped them to reference NC-001802.1 to generate run and quality metrics. FASTA files were analyzed for drug resistance mutations using Exatype NGS by Hyraxbio. All samples were run in triplicates. Quality metrics and coverage analysis are shown in Tables 1A and 1B. The average percent reference coverage for viral loads of 4,880,000, 488,000,48,800, 4,880, and 488 were >90%. Reference coverage for negative controls was undetected due to assembly failure. These results showed that the assay performed well in sequencing HIV-1-positive virus. To evaluate the accuracy of the assay, we leveraged the HIV-1 positive control’s mutations. The positive control has a non-polymorphic mutation F227L found in the non-nucleoside reverse transcription. We calculated the percent accuracy based on the ability of the assay to identify this mutation in all 17 samples that passed quality control metrics. Table 2 shows a100% consistent detection of the F227L mutation in all samples. We evaluated the analytical sensitivity of the assay based on the HIV-1 positive control drug-resistant profile within the NRTI – Nucleoside Reverse Transcriptase Inhibitors (susceptible), NNRTI - Non-Nucleoside Reverse Transcriptase Inhibitors (3 intermediate and F227L mutation), PI - Protease Inhibitors (susceptible), and INSTI - Integrase Inhibitors regions (susceptible). The assay detected concordant results in 64 out of 68 drug classes and their resistance calls in all the dilution series (64/68) x 100 = 94%). Data is shown in table 4. Within-run precision was calculated based on three replicates from each dilution series (4.880,000, 488,000, 48,000, 4,800, 480, and 48.8 copies) of the HIV-1 subtype B positive control and three replicates of a negative template control. There were expected 17 positive and eight negative (3 negative and five non-HIV specimens). The miscalculation rate was 0%. Also, Positive Predictive Value =100% and Negative Predictive Value = 100%. Figure 1 demonstrates that sequencing the HIV-1 genome using the COVIDSeqTM Assay (RUO) (on the Illumina MiSeq 2X150) produces >80% coverage of the complete HIV-1 genome with viral copy down to 488 copies/ul. Analytical specificity and coinfection for HIV-1 genome were evaluated using confirmed positive samples of HAV-Hepatitis A, HBV-Hepatitis B, HCV-Hepatitis C, and two TP-Syphilis. All samples were confirmed to be negative for HIV-1. QC metrics failed for all five samples due to genome assembly failure and no reads, as depicted in Table 6. Another evaluation of analytical specificity of the mutation detected was calculated based on the assay’s ability to detect themutation, F227C, a rare nonpolymorphic mutation at position 106, at NNRTI - Non-Nucleoside Reverse TranscriptaseInhibitors region in each of the 17 samples at the different viral loads of (4.880,000, 488,000, 48,000, 4,800,480, and 48.8). Table 5 shows (17/17) x 100 =100.00% analytical specificity to detect the mutation. We evaluated the Limit of Detection by analyzing all samples, their experimental/expected results, and the ability to detectmutations and drug-resistant calls in all four drug-resistant regions of the Pol Region (NRTI, NNRTI, PI, and INSTI). Table 6 shows a 100% concordance between expected and experimental results forviral copy number dilutions up to 10ᶺ (-5) or 488 copies. At 10ᶺ (-6) or 49 viral copies, sensitivity decreases to 8/12 = 67%. These results in Table 7 indicate that positive HIV-1 samples with viral loads up to 488 copies can reliably produce a drug resistance profile. Viral copies under 488 have inconsistent results and do not pass quality control metrics. *One sample from HIV_PC_1:10e4 (2) failed QC due to sample loss during library preparation. Analysis shows inclusion and omission metrics. Triplicate Average: Average mean calculation for dilutions (all dilutions were run in triplicate) Table 1B. HIV-1 Positive Control Quality Control Metrics (Raw Data) Analysis on Terra.bio Dilution Factor Assembly Length Unambiguous Assembly Mean Coverage Number of Ns Total Number % Reference Coverage HIV_PC_10_1 9040 6854.19 128 9175 98.46 HIV_PC_10_2 9040 5455.84 135 9182 98.46 HIV_PC_10_3 9079 11842.6 88 9174 98.89 HIV_PC_100_1 9040 6085.74 125 9172 98.46 HIV_PC_100_2 9041 6447.61 122 9169 98.48 HIV_PC_100_3 9040 4327.41 102 9149 98.46 HIV_PC_1000_1 9041 10369.2 105 9152 98.48 HIV_PC_1000_2 9040 4998.39 121 9168 98.46 HIV_PC_1000_3 9039 3504.9 105 9152 98.45 HIV_PC_10000_1 9044 14738.5 127 9174 98.51 HIV_PC_10000_2 5010 8504.21 3419 8432 54.57 HIV_PC_10000_3 8769 4044.16 375 9151 95.51 HIV_PC_100000_1 8739 5186.66 399 9142 95.19 HIV_PC_100000_2 8730 5353.17 411 9156 95.09 HIV_PC_100000_3 8129 3741.45 1014 9149 88.54 HIV_PC_1000000_1 6263 2370.05 2879 9147 68.22 HIV_PC_1000000_2 6784 2204.5 2361 9151 73.89 HIV_PC_1000000_3 6441 2068.95 2713 9156 70.16 Table 3. HIV Drug Resistance Mutations HIV-1 drug-resistant profiles from Exatype NGS, using the Stamford HIV drug-resistant database. Dilution Factor NRTI – Nucleoside Reverse Transcriptase Inhibitors NNRTI - Non-Nucleoside Reverse Transcriptase Inhibitors PI - Protease Inhibitors INSTI - Integrase Inhibitors HIV_PC_10_1 Susceptible 2-Susceptible, 3-Intermediate ETR : [RT] F227L Susceptible Susceptible HIV_PC_10_2 Susceptible 2-Susceptible, 3-Intermediate ETR : [RT] F227L Susceptible Susceptible HIV_PC_10_3 Susceptible 2-Susceptible, 3-Intermediate ETR : [RT] F227L Susceptible Susceptible HIV_PC_100_1 Susceptible 2-Susceptible, 3-Intermediate ETR : [RT] F227L Susceptible Susceptible HIV_PC_100_2 Susceptible 2-Susceptible, 3-Intermediate ETR : [RT] F227L Susceptible Susceptible HIV_PC_100_3 Susceptible 2-Susceptible, 3-Intermediate ETR : [RT] F227L Susceptible Susceptible HIV_PC_1000_1 Susceptible 2-Susceptible, 3-Intermediate ETR : [RT] F227L Susceptible Susceptible HI

BackgroundRapid identification of COVID-19 is crucial during the pandemic for the treatment and management of patients. Thus, early diagnosis of the disease using laboratory parameters can help in the rapid management of infected patients. This study aimed to investigate the correlation of viral load with hematological and biochemical parameters. This will ultimately help physicians to better understand the dynamics of this novel virus and aid in the management of patients.MethodologyLaboratory confirmation of SARS-CoV-2 was performed by reverse transcription-polymerase chain reaction (RT-PCR) at the Al-Buraimi Hospital Laboratory Department using oropharyngeal and nasopharyngeal swabs. Positive cases were collected from July 2020 to January 2021 to be enrolled in this study.ResultsIn this study, 264 confirmed positive patients were included initially and divided into three groups according to their cycle threshold (Ct) values obtained by PCR. Out of the total 264 patients, 174 (65.9%) were male, while 90 (34.1%) were female. However, the final sample was only 253 patients who met the inclusion criteria. With regard to Ct values, the study population was divided into the following three groups: Group 1 with Ct values of 9-20 (n = 87; 34.4%), group 2 with Ct values of 21-30 (n = 122; 47.8%), and group 3 with Ct values of 31-42 (n = 44; 17.4%).ConclusionsWe found that the proportion of male patients infected with COVID-19 was higher compared to females. In addition, the highest incidence was among patients in the age group of 51-70 years. The ferritin and alanine transaminase levels were highest in the initial stage of the infection (group 1) and decreased at the recovery stage. However, neutrophil, lymphocyte, alkaline phosphatase, and C-reactive protein showed an increasing trend from high viral load groups to low viral load groups. The values of the rest of the parameters, such as albumin, total bilirubin, lactate dehydrogenase, and D-dimer, were slightly higher in the initial stage of the infection but the decreasing trend was low; therefore, they were not considered helpful in predicting the disease severity reflected by their Ct value in the three different groups.

Accurate and timely diagnosis is essential in limiting the spread of SARS-CoV-2 infection. The reference standard, rRT-PCR, requires specialized laboratories, costly reagents, and a long turnaround time. Antigen RDTs provide a feasible alternative to rRT-PCR since they are quick, relatively inexpensive, and do not require a laboratory. The WHO requires that Ag RDTs have a sensitivity ≥80% and specificity ≥97%. This evaluation was conducted at 11 health facilities in Kenya between March and July 2021. We enrolled persons of any age with respiratory symptoms and asymptomatic contacts of confirmed COVID-19 cases. We collected demographic and clinical information and two nasopharyngeal specimens from each participant for Ag RDT testing and rRT-PCR. We calculated the diagnostic performance of the Panbio™ Ag RDT against the US Centers for Disease Control and Prevention's (CDC) rRT-PCR test. We evaluated the Ag RDT in 2,245 individuals where 551 (24.5%, 95% CI: 22.8-26.3%) tested positive by rRT-PCR. Overall sensitivity of the Ag RDT was 46.6% (95% CI: 42.4-50.9%), specificity 98.5% (95% CI: 97.8-99.0%), PPV 90.8% (95% CI: 86.8-93.9%) and NPV 85.0% (95% CI: 83.4-86.6%). Among symptomatic individuals, sensitivity was 60.6% (95% CI: 54.3-66.7%) and specificity was 98.1% (95% CI: 96.7-99.0%). Among asymptomatic individuals, sensitivity was 34.7% (95% CI 29.3-40.4%) and specificity was 98.7% (95% CI: 97.8-99.3%). In persons with onset of symptoms <5 days (594/876, 67.8%), sensitivity was 67.1% (95% CI: 59.2-74.3%), and 53.3% (95% CI: 40.0-66.3%) among those with onset of symptoms >7 days (157/876, 17.9%). The highest sensitivity was 87.0% (95% CI: 80.9-91.8%) in symptomatic individuals with cycle threshold (Ct) values ≤30. The overall sensitivity and NPV of the Panbio™ Ag RDT were much lower than expected. The specificity of the Ag RDT was high and satisfactory; therefore, a positive result may not require confirmation by rRT-PCR. The kit may be useful as a rapid screening tool only for symptomatic patients in high-risk settings with limited access to rRT-PCR. A negative result should be interpreted based on clinical and epidemiological information and may require retesting by rRT-PCR.