A laboratory framework for ongoing optimization of amplification-based genomic surveillance programs.
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ABSTRACT: Importance
This study provides a laboratory framework to ensure ongoing relevance and performance of amplification-based whole genome sequencing to strengthen public health surveillance during extended outbreaks or pandemics. The framework integrates regular reviews of the performance of a genomic surveillance system and highlights the importance of ongoing monitoring and the identification and implementation of improvements to whole genome sequencing methods to enhance public health responses to pathogen outbreaks.
SUBMITTER: Lam C
PROVIDER: S-EPMC10715188 | biostudies-literature | 2023 Nov
REPOSITORIES: biostudies-literature
Publications
A laboratory framework for ongoing optimization of amplification-based genomic surveillance programs.
Lam Connie C Johnson-Mackinnon Jessica J Basile Kerri K Fong Winkie W Suster Carl J E CJ Gall Mailie M Agius Jessica J Chandra Shona S Draper Jenny J Martinez Elena E Drew Alexander A Wang Qinning Q Chen Sharon C SC Kok Jen J Dwyer Dominic E DE Sintchenko Vitali V Rockett Rebecca J RJ
Microbiology spectrum 20231115 6
<h4>Importance</h4>This study provides a laboratory framework to ensure ongoing relevance and performance of amplification-based whole genome sequencing to strengthen public health surveillance during extended outbreaks or pandemics. The framework integrates regular reviews of the performance of a genomic surveillance system and highlights the importance of ongoing monitoring and the identification and implementation of improvements to whole genome sequencing methods to enhance public health res ...[more]
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Project description:BackgroundMass vaccination is a cornerstone of public health emergency preparedness and response. However, injudicious placement of vaccination sites can lead to the formation of long waiting lines or queues, which discourages individuals from waiting to be vaccinated and may thus jeopardize the achievement of public health targets. Queueing theory offers a framework for modeling queue formation at vaccination sites and its effect on vaccine uptake.MethodsWe developed an algorithm that integrates queueing theory within a spatial optimization framework to optimize the placement of mass vaccination sites. The algorithm was built and tested using data from a mass canine rabies vaccination campaign in Arequipa, Peru. We compared expected vaccination coverage and losses from queueing (i.e., attrition) for sites optimized with our queue-conscious algorithm to those obtained from a queue-naive version of the same algorithm.ResultsSites placed by the queue-conscious algorithm resulted in 9-19% less attrition and 1-2% higher vaccination coverage compared to sites placed by the queue-naive algorithm. Compared to the queue-naive algorithm, the queue-conscious algorithm favored placing more sites in densely populated areas to offset high arrival volumes, thereby reducing losses due to excessive queueing. These results were not sensitive to misspecification of queueing parameters or relaxation of the constant arrival rate assumption.ConclusionOne should consider losses from queueing to optimally place mass vaccination sites, even when empirically derived queueing parameters are not available. Due to the negative impacts of excessive wait times on participant satisfaction, reducing queueing attrition is also expected to yield downstream benefits and improve vaccination coverage in subsequent mass vaccination campaigns.
| S-EPMC11213063 | biostudies-literature
Project description:This SuperSeries is composed of the following subset Series: GSE3557: Effect of the amount of input total RNA on T7 amplification GSE3558: Effect of in vitro transcription time on the fidelity of T7-based RNA linear amplification GSE3559: Variation in cDNA microarray analysis of gene expression using unamplified poly(A)+ RNA GSE3560: Effects of template switching (TS) primer and cDNA cleanup columns on T7 based RNA linear amplification GSE3561: Effect of ligase on T7 based RNA linear amplification GSE3562: Effect of column cleanup on T7 based RNA linear amplification GSE3563: Correlation between expression levels of different tumors measured by poly(A)+RNA and aRNA Abstract: BACKGROUND: T7 based linear amplification of RNA is used to obtain sufficient antisense RNA for microarray expression profiling. We optimized and systematically evaluated the fidelity and reproducibility of different amplification protocols using total RNA obtained from primary human breast carcinomas and high-density cDNA microarrays. RESULTS: Using an optimized protocol, the average correlation coefficient of gene expression of 11,123 cDNA clones between amplified and unamplified samples is 0.82 (0.85 when a virtual array was created using repeatedly amplified samples to minimize experimental variation). Less than 4% of genes show changes in expression level by 2-fold or greater after amplification compared to unamplified samples. Most changes due to amplification are not systematic both within one tumor sample and between different tumors. Amplification appears to dampen the variation of gene expression for some genes when compared to unamplified poly(A)+ RNA. The reproducibility between repeatedly amplified samples is 0.97 when performed on the same day, but drops to 0.90 when performed weeks apart. The fidelity and reproducibility of amplification is not affected by decreasing the amount of input total RNA in the 0.3-3 micrograms range. Adding template-switching primer, DNA ligase, or column purification of double-stranded cDNA does not improve the fidelity of amplification. The correlation coefficient between amplified and unamplified samples is higher when total RNA is used as template for both experimental and reference RNA amplification. CONCLUSION: T7 based linear amplification reproducibly generates amplified RNA that closely approximates original sample for gene expression profiling using cDNA microarrays. Refer to individual Series
2006-03-06 | E-GEOD-4349 | biostudies-arrayexpress
Project description:Abstract: BACKGROUND: T7 based linear amplification of RNA is used to obtain sufficient antisense RNA for microarray expression profiling. We optimized and systematically evaluated the fidelity and reproducibility of different amplification protocols using total RNA obtained from primary human breast carcinomas and high-density cDNA microarrays. RESULTS: Using an optimized protocol, the average correlation coefficient of gene expression of 11,123 cDNA clones between amplified and unamplified samples is 0.82 (0.85 when a virtual array was created using repeatedly amplified samples to minimize experimental variation). Less than 4% of genes show changes in expression level by 2-fold or greater after amplification compared to unamplified samples. Most changes due to amplification are not systematic both within one tumor sample and between different tumors. Amplification appears to dampen the variation of gene expression for some genes when compared to unamplified poly(A)+ RNA. The reproducibility between repeatedly amplified samples is 0.97 when performed on the same day, but drops to 0.90 when performed weeks apart. The fidelity and reproducibility of amplification is not affected by decreasing the amount of input total RNA in the 0.3-3 micrograms range. Adding template-switching primer, DNA ligase, or column purification of double-stranded cDNA does not improve the fidelity of amplification. The correlation coefficient between amplified and unamplified samples is higher when total RNA is used as template for both experimental and reference RNA amplification. CONCLUSION: T7 based linear amplification reproducibly generates amplified RNA that closely approximates original sample for gene expression profiling using cDNA microarrays. This SuperSeries is composed of the SubSeries listed below.
2006-03-07 | GSE4349 | GEO
Project description: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.
| S-EPMC9839449 | biostudies-literature
Project description:Global efforts are underway to combat antimicrobial resistance (AMR). A key target in this intervention is surveillance for local and national action. Data on AMR in Ghana are limited, and monitoring of AMR is nonexistent. We sought to generate baseline data on AMR, and to assess the readiness of Ghana in laboratory-based surveillance. Biomedical scientists in laboratories across Ghana with capacity to perform bacteriological culture were selected and trained. In-house standard operating protocols were used to perform microbiological investigations on clinical specimens. Additional microbiological tests and data analyses were performed at a centralized laboratory. Surveillance data were stored and analyzed using WHONET program files. A total of 24 laboratories participated in the training, and 1,598 data sets were included in the final analysis. A majority of the bacterial species were isolated from outpatients (963 isolates; 60.3%). Urine (617 isolates; 38.6%) was the most common clinical specimen cultured, compared to blood (100 isolates; 6.3%). Ten of 18 laboratories performed blood culture. Bacteria isolated included Escherichia coli (27.5%), Pseudomonas spp. (14.0%), Staphylococcus aureus (11.5%), Streptococcus spp. (2.3%), and Salmonella enterica serovar Typhi (0.6%). Most of the isolates were multidrug-resistant, and over 80% of them were extended-spectrum beta-lactamases-producing. Minimum inhibitory concentration levels at 50% and at 90% for ciprofloxacin, ceftriaxone, and amikacin on selected multidrug-resistant bacteria species ranged between 2 µg/mL and >256 µg/mL. A range of clinical bacterial isolates were resistant to important commonly used antimicrobials in the country, necessitating an effective surveillance to continuously monitor AMR in Ghana. With local and international support, Ghana can participate in global AMR surveillance.
| S-EPMC4655947 | biostudies-literature
Project description:BackgroundRespiratory viruses have clinical and epidemiological importance. With the COVID-19 pandemic, interest has focused on SARS-CoV-2, but as a result, the number of samples available for the differential diagnosis of other respiratory viruses has increased.Study designCross-sectional study.ObjectiveTo describe the epidemiological behavior of respiratory viruses based on a laboratory-based epidemiological surveillance system using data from 2017 to 2023.MethodsUnivariate, bivariate and multivariate analyses of data from a laboratory database of respiratory viruses detected by multiplex RT‒qPCR were performed.ResultsA total of 4,632 samples with positive results for at least 1 respiratory virus, not including influenza or SARS-CoV-2, were analyzed. The most common virus detected was respiratory syncytial virus in 1,467 (26.3%) samples, followed by rhinovirus in 1,384 (24.8%) samples. Most of the samples were from children under 5 years of age. The age-adjusted odds ratio (OR) of death for patients infected with parainfluenza virus 4 was 4.1 (95% confidence interval [95% CI] 2.0-8.2).ConclusionRespiratory syncytial virus and rhinovirus had the highest frequency and proportion of coinfections, whereas parainfluenza virus 4 was associated with an increased risk of death.
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Project description:Isolates obtained from Hospital Genomic Surveillance
| PRJEB83276 | ENA
Project description:Infectious disease surveillance systems provide vital data for guiding disease prevention and control policies, yet the formalization of methods to optimize surveillance networks has largely been overlooked. Decisions surrounding surveillance design parameters-such as the number and placement of surveillance sites, target populations, and case definitions-are often determined by expert opinion or deference to operational considerations, without formal analysis of the influence of design parameters on surveillance objectives. Here we propose a simulation framework to guide evidence-based surveillance network design to better achieve specific surveillance goals with limited resources. We define evidence-based surveillance design as an optimization problem, acknowledging the many operational constraints under which surveillance systems operate, the many dimensions of surveillance system design, the multiple and competing goals of surveillance, and the complex and dynamic nature of disease systems. We describe an analytical framework-the Disease Surveillance Informatics Optimization and Simulation (DIOS) framework-for the identification of optimal surveillance designs through mathematical representations of disease and surveillance processes, definition of objective functions, and numerical optimization. We then apply the framework to the problem of selecting candidate sites to expand an existing surveillance network under alternative objectives of: (1) improving spatial prediction of disease prevalence at unmonitored sites; or (2) estimating the observed effect of a risk factor on disease. Results of this demonstration illustrate how optimal designs are sensitive to both surveillance goals and the underlying spatial pattern of the target disease. The findings affirm the value of designing surveillance systems through quantitative and adaptive analysis of network characteristics and performance. The framework can be applied to the design of surveillance systems tailored to setting-specific disease transmission dynamics and surveillance needs, and can yield improved understanding of tradeoffs between network architectures.
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Project description:A decentralized surveillance system to identify local outbreaks and monitor SARS-CoV-2 Variants of Concern is one of the primary strategies for the pandemic's containment. Although next-generation sequencing (NGS) is a gold standard for genomic surveillance and variant discovery, the technology is still cost-prohibitive for decentralized sequencing, particularly in small independent labs with limited resources. We have optimized the Illumina COVIDSeq™ protocol for the Illumina MiniSeq instrument to reduce cost without compromising accuracy. We slashed the library preparation cost by half by using 50% of recommended reagents at each step and normalizing the libraries before pooling to achieve uniform coverage. Reagent-only cost (∼$43.27/sample) for SARS-CoV-2 variant analysis with this normalized input protocol on MiniSeq instruments is comparable to what is achieved on high throughput instruments such as NextSeq and NovaSeq. Using this modified protocol, we tested 153 clinical samples, and 90% of genomic coverage was achieved for 142/153 samples analyzed in this study. The lineage was correctly assigned to all samples (152/153) except for one. This modified protocol can help laboratories with constrained resources to contribute in decentralized COVID-19 surveillance in the post-vaccination era.
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Project description:This study aimed to develop a framework for combining community wastewater surveillance with state clinical surveillance for the confirmation of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants within the community and to provide recommendations on how to expand on such research and apply the findings in public health responses. Wastewater samples were collected weekly from 17 geographically resolved locations in Louisville/Jefferson County, Kentucky (USA), from February 10 to December 13, 2021. Genomic surveillance and quantitative reverse transcription PCR (RT-qPCR) platforms were used to screen for SARS-CoV-2 in wastewater, and state clinical surveillance was used for confirmation. The study results highlighted an increased epidemiological value of combining community wastewater genomic surveillance and RT-qPCR with conventional case-auditing methods. The spatial scale and temporal frequency of wastewater sampling provided promising sensitivity and specificity for gaining public health screening insights about SARS-CoV-2 emergence, seeding, and spread in communities. Improved national surveillance systems are needed against future pathogens and variants, and wastewater-based genomic surveillance exhibits great potential when coupled with clinical testing. This paper presents evidence that complementary wastewater and clinical testing are cost-effectively enhanced when used in combination, as they provide a strong tool for a joint public health framework. Future pathogens of interest may be examined in either a targeted fashion or using a more global approach where all pathogens are monitored. This study has also provided novel insights developed from evidence-based public health practices.
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