SARS-CoV-2 Genomic Epidemiology: Data and Sequencing Infrastructure

Merhi, Georgi; Koweyes, Jad; Salloum, Tamara; Khoury, Charbel Al; Haidar, Siwar; Tokajian, Sima

doi:10.2217/fmb-2021-0207

Cited by 11 publications

(10 citation statements)

References 15 publications

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“…These gains require scaling up in sample transport networks, access to sequencing machinery, trained personnel and/or increases in numbers of sequenced samples to make the most efficient use of each sequencing run 11 . Furthermore, LMICs also often face high costs and extended delivery delays of laboratory reagents and consumables that were sometimes further exacerbated by recurring travel bans during the acute phase of the pandemic 5 , 12 , 13 .…”

Section: Discussionmentioning

confidence: 99%

SARS-CoV-2 diagnostic testing rates determine the sensitivity of genomic surveillance programs

et al. 2023

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The first step in SARS-CoV-2 genomic surveillance is testing to identify people who are infected. However, global testing rates are falling as we emerge from the acute health emergency and remain low in many low- and middle-income countries (mean = 27 tests per 100,000 people per day). We simulated COVID-19 epidemics in a prototypical low- and middle-income country to investigate how testing rates, sampling strategies and sequencing proportions jointly impact surveillance outcomes, and showed that low testing rates and spatiotemporal biases delay time to detection of new variants by weeks to months and can lead to unreliable estimates of variant prevalence, even when the proportion of samples sequenced is increased. Accordingly, investments in wider access to diagnostics to support testing rates of approximately 100 tests per 100,000 people per day could enable more timely detection of new variants and reliable estimates of variant prevalence. The performance of global SARS-CoV-2 genomic surveillance programs is fundamentally limited by access to diagnostic testing.

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Section: Discussionmentioning

confidence: 99%

SARS-CoV-2 diagnostic testing rates determine the sensitivity of genomic surveillance programs

et al. 2023

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“…Second, the large gap of sequencing rate between low-and middle-income countries and developed countries may affect the accuracy of some features in HiRisk-Predictor, such as node size, GIE, etc. [27] The delay time may come from the recessive transmission of the variant in under-developed countries with limited sequencing and genomic surveillance capabilities, which is critical in monitoring SARS-CoV-2. Third, the collection to submission time (CST) lag [18] may also increase the delay time due to the inability to obtain realtime data.…”

Section: Discussionmentioning

confidence: 99%

Machine learning detection of SARS-CoV-2 high-risk variants

et al. 2023

Preprint

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The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has evolved many high-risk variants, resulting in repeated COVID-19 waves of pandemic during the past years. Therefore, accurate early-warning of high-risk variants is vital for epidemic prevention and control. Here we construct a machine learning model to predict high-risk variants of SARS-CoV-2 by LightGBM algorithm based on several important haplotype network features. As demonstrated on a series of different retrospective testing datasets, our model achieves accurate prediction of all variants of concern (VOC) and most variants of interest (AUC=0.96). Prediction based on the latest sequences shows that the newly emerging lineage BA.5 has the highest risk score and spreads rapidly to become a major epidemic lineage in multiple countries, suggesting that BA.5 bears great potential to be a VOC. In sum, our machine learning model is capable to early predict high-risk variants soon after their emergence, thus greatly improving public health preparedness against the evolving virus.

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“…These gains require scale up in sample transport networks, access to sequencing machinery, trained personnel, and/or increases in numbers of sequenced samples to make the most efficient use of each sequencing run 11 . Furthermore, LMICs also often face high costs and extended delivery delays of laboratory reagents and consumables that were sometimes further exacerbated by recurring travel bans during the acute phase of the pandemic 5,12,13 .…”

Section: Discussionmentioning

confidence: 99%

SARS-CoV-2 diagnostic testing rates determine the sensitivity of genomic surveillance programs

Han

Toporowski

Sacks

et al. 2022

Preprint

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Background Genomic surveillance is essential for monitoring the emergence and spread of SARS-CoV-2 variants. SARS-CoV-2 diagnostic testing is the starting point for SARS-CoV-2 genomic sequencing. However, testing rates in many low- and middle-income countries (LMICs) are low (mean = 27 tests/100,000 people/day) and global testing rates are falling in the post-crisis phase of the pandemic, leading to spatiotemporal biases in sample collection. Various public health agencies and academic groups have produced recommendations on sample sizes and sequencing strategies for effective genomic surveillance. However, these recommendations assume very high volumes of diagnostic testing that are currently well beyond reach in most LMICs. Methods To investigate how testing rates, sequencing strategies and the degree of spatiotemporal bias in sample collection impact variant detection and monitoring outcomes, we used an individual-based model to simulate COVID-19 epidemics in a prototypical LMIC. Within the model, we simulated a range of testing rates, accounted for likely testing demand and applied various genomic surveillance strategies, including sentinel surveillance. Findings Diagnostic testing rates play a substantially larger role in monitoring the prevalence and emergence of new variants than the proportion of samples sequenced. To enable timely detection and monitoring of emerging variants, programs should achieve average testing rates of at least 100 tests/100,000 people/day and sequence 5-10% of test-positive specimens, which may be accomplished through sentinel or other routine surveillance systems. Under realistic assumptions, this averages to ~10 samples for sequencing/1,000,000 people/week. Interpretation For countries where testing capacities are low and sample collection is spatiotemporally biased, surveillance programs should prioritize investments in wider access to diagnostic testing to enable more representative sampling, ahead of simply increasing quantities of sequenced samples. Funding European Research Council, the Rockefeller Foundation, and the Governments of Germany, Canada, UK, Australia, Norway, Saudi Arabia, Kuwait, Netherlands and Portugal.

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SARS-CoV-2 Genomic Epidemiology: Data and Sequencing Infrastructure

Cited by 11 publications

References 15 publications

SARS-CoV-2 diagnostic testing rates determine the sensitivity of genomic surveillance programs

SARS-CoV-2 diagnostic testing rates determine the sensitivity of genomic surveillance programs

Machine learning detection of SARS-CoV-2 high-risk variants

SARS-CoV-2 diagnostic testing rates determine the sensitivity of genomic surveillance programs

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