Genomic epidemiology of SARS-CoV-2 in Cambodia, January 2020 to February 2021.

The first case of coronavirus disease 2019 (COVID-19) in Cambodia was confirmed on 27 January 2020 in a traveller from Wuhan. Cambodia subsequently implemented strict travel restrictions, and although intermittent cases were reported during the first year of the COVID-19 pandemic, no apparent widespread community transmission was detected. Investigating the routes of severe acute respiratory coronavirus 2 (SARS-CoV-2) introduction into the country was critical for evaluating the implementation of public health interventions and assessing the effectiveness of social control measures. Genomic sequencing technologies have enabled rapid detection and monitoring of emerging variants of SARS-CoV-2. Here, we detected 478 confirmed COVID-19 cases in Cambodia between 27 January 2020 and 14 February 2021, 81.3 per cent in imported cases. Among them, fifty-four SARS-CoV-2 genomes were sequenced and analysed along with representative global lineages. Despite the low number of confirmed cases, we found a high diversity of Cambodian viruses that belonged to at least seventeen distinct PANGO lineages. Phylogenetic inference of SARS-CoV-2 revealed that the genetic diversity of Cambodian viruses resulted from multiple independent introductions from diverse regions, predominantly, Eastern Asia, Europe, and Southeast Asia. Most cases were quickly isolated, limiting community spread, although there was an A.23.1 variant cluster in Phnom Penh in November 2020 that resulted in a small-scale local transmission. The overall low incidence of COVID-19 infections suggests that Cambodia's early containment strategies, including travel restrictions, aggressive testing and strict quarantine measures, were effective in preventing large community outbreaks of COVID-19.

Metagenomic Pathogen Sequencing in Resource-Scarce Settings: Lessons Learned and the Road Ahead

Metagenomic next-generation sequencing (mNGS) is the process of sequencing all genetic material in a biological sample. The technique is growing in popularity with myriad applications including outbreak investigation, biosurveillance, and pathogen detection in clinical samples. However, mNGS programs are costly to build and maintain, and additional obstacles faced by low- and middle-income countries (LMICs) may further widen global inequities in mNGS capacity. Over the past two decades, several important infectious disease outbreaks have highlighted the importance of establishing widespread sequencing capacity to support rapid disease detection and containment at the source. Using lessons learned from the COVID-19 pandemic, LMICs can leverage current momentum to design and build sustainable mNGS programs, which would form part of a global surveillance network crucial to the elimination of infectious diseases.

SARS-CoV-2 Cross-Reactivity in Prepandemic Serum from Rural Malaria-Infected Persons, Cambodia.

Inhabitants of the Greater Mekong Subregion in Cambodia are exposed to pathogens that might influence serologic cross-reactivity with severe acute respiratory syndrome coronavirus 2. A prepandemic serosurvey of 528 malaria-infected persons demonstrated higher-than-expected positivity of nonneutralizing IgG to spike and receptor-binding domain antigens. These findings could affect interpretation of large-scale serosurveys.

IDseq-An open source cloud-based pipeline and analysis service for metagenomic pathogen detection and monitoring.

BACKGROUND: Metagenomic next-generation sequencing (mNGS) has enabled the rapid, unbiased detection and identification of microbes without pathogen-specific reagents, culturing, or a priori knowledge of the microbial landscape. mNGS data analysis requires a series of computationally intensive processing steps to accurately determine the microbial composition of a sample. Existing mNGS data analysis tools typically require bioinformatics expertise and access to local server-class hardware resources. For many research laboratories, this presents an obstacle, especially in resource-limited environments. FINDINGS: We present IDseq, an open source cloud-based metagenomics pipeline and service for global pathogen detection and monitoring (https://idseq.net). The IDseq Portal accepts raw mNGS data, performs host and quality filtration steps, then executes an assembly-based alignment pipeline, which results in the assignment of reads and contigs to taxonomic categories. The taxonomic relative abundances are reported and visualized in an easy-to-use web application to facilitate data interpretation and hypothesis generation. Furthermore, IDseq supports environmental background model generation and automatic internal spike-in control recognition, providing statistics that are critical for data interpretation. IDseq was designed with the specific intent of detecting novel pathogens. Here, we benchmark novel virus detection capability using both synthetically evolved viral sequences and real-world samples, including IDseq analysis of a nasopharyngeal swab sample acquired and processed locally in Cambodia from a tourist from Wuhan, China, infected with the recently emergent SARS-CoV-2. CONCLUSION: The IDseq Portal reduces the barrier to entry for mNGS data analysis and enables bench scientists, clinicians, and bioinformaticians to gain insight from mNGS datasets for both known and novel pathogens.