Browse
Submit Data
Databases
API
Help

Dataset Information

19 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Combining genomic and epidemiological data to compare the transmissibility of SARS-CoV-2 lineages.

ABSTRACT: Emerging SARS-CoV-2 variants have shaped the second year of the COVID-19 pandemic and the public health discourse around effective control measures. Evaluating the public health threat posed by a new variant is essential for appropriately adapting response efforts when community transmission is detected. However, this assessment requires that a true comparison can be made between the new variant and its predecessors because factors other than the virus genotype may influence spread and transmission. In this study, we develop a framework that integrates genomic surveillance data to estimate the relative effective reproduction number (R _t ) of co-circulating lineages. We use Connecticut, a state in the northeastern United States in which the SARS-CoV-2 variants B.1.1.7 and B.1.526 co-circulated in early 2021, as a case study for implementing this framework. We find that the R _t of B.1.1.7 was 6-10% larger than that of B.1.526 in Connecticut in the midst of a COVID-19 vaccination campaign. To assess the generalizability of this framework, we apply it to genomic surveillance data from New York City and observe the same trend. Finally, we use discrete phylogeography to demonstrate that while both variants were introduced into Connecticut at comparable frequencies, clades that resulted from introductions of B.1.1.7 were larger than those resulting from B.1.526 introductions. Our framework, which uses open-source methods requiring minimal computational resources, may be used to monitor near real-time variant dynamics in a myriad of settings.

SUBMITTER: Petrone ME

PROVIDER: S-EPMC8259915 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Similar Datasets

Combining genomic and epidemiological data to compare the transmissibility of SARS-CoV-2 variants Alpha and Iota.

Project description:SARS-CoV-2 variants shaped the second year of the COVID-19 pandemic and the discourse around effective control measures. Evaluating the threat posed by a new variant is essential for adapting response efforts when community transmission is detected. In this study, we compare the dynamics of two variants, Alpha and Iota, by integrating genomic surveillance data to estimate the effective reproduction number (Rt) of the variants. We use Connecticut, United States, in which Alpha and Iota co-circulated in 2021. We find that the Rt of these variants were up to 50% larger than that of other variants. We then use phylogeography to show that while both variants were introduced into Connecticut at comparable frequencies, clades that resulted from introductions of Alpha were larger than those resulting from Iota introductions. By monitoring the dynamics of individual variants throughout our study period, we demonstrate the importance of routine surveillance in the response to COVID-19.

| S-EPMC9095641 | biostudies-literature

Epidemiological drivers of transmissibility and severity of SARS-CoV-2 in England.

Project description:As the SARS-CoV-2 pandemic progressed, distinct variants emerged and dominated in England. These variants, Wildtype, Alpha, Delta, and Omicron were characterized by variations in transmissibility and severity. We used a robust mathematical model and Bayesian inference framework to analyse epidemiological surveillance data from England. We quantified the impact of non-pharmaceutical interventions (NPIs), therapeutics, and vaccination on virus transmission and severity. Each successive variant had a higher intrinsic transmissibility. Omicron (BA.1) had the highest basic reproduction number at 8.3 (95% credible interval (CrI) 7.7-8.8). Varying levels of NPIs were crucial in controlling virus transmission until population immunity accumulated. Immune escape properties of Omicron decreased effective levels of immunity in the population by a third. Furthermore, in contrast to previous studies, we found Alpha had the highest basic infection fatality ratio (2.9%, 95% CrI 2.7-3.2), followed by Delta (2.2%, 95% CrI 2.0-2.4), Wildtype (1.2%, 95% CrI 1.1-1.2), and Omicron (0.7%, 95% CrI 0.6-0.8). Our findings highlight the importance of continued surveillance. Long-term strategies for monitoring and maintaining effective immunity against SARS-CoV-2 are critical to inform the role of NPIs to effectively manage future variants with potentially higher intrinsic transmissibility and severe outcomes.

| S-EPMC10352350 | biostudies-literature

Rapid feedback on hospital onset SARS-CoV-2 infections combining epidemiological and sequencing data.

Project description:Rapid identification and investigation of healthcare-associated infections (HCAIs) is important for suppression of SARS-CoV-2, but the infection source for hospital onset COVID-19 infections (HOCIs) cannot always be readily identified based only on epidemiological data. Viral sequencing data provides additional information regarding potential transmission clusters, but the low mutation rate of SARS-CoV-2 can make interpretation using standard phylogenetic methods difficult. We developed a novel statistical method and sequence reporting tool (SRT) that combines epidemiological and sequence data in order to provide a rapid assessment of the probability of HCAI among HOCI cases (defined as first positive test >48 hr following admission) and to identify infections that could plausibly constitute outbreak events. The method is designed for prospective use, but was validated using retrospective datasets from hospitals in Glasgow and Sheffield collected February-May 2020. We analysed data from 326 HOCIs. Among HOCIs with time from admission ≥8 days, the SRT algorithm identified close sequence matches from the same ward for 160/244 (65.6%) and in the remainder 68/84 (81.0%) had at least one similar sequence elsewhere in the hospital, resulting in high estimated probabilities of within-ward and within-hospital transmission. For HOCIs with time from admission 3-7 days, the SRT probability of healthcare acquisition was >0.5 in 33/82 (40.2%). The methodology developed can provide rapid feedback on HOCIs that could be useful for infection prevention and control teams, and warrants further prospective evaluation. The integration of epidemiological and sequence data is important given the low mutation rate of SARS-CoV-2 and its variable incubation period. COG-UK HOCI funded by COG-UK consortium, supported by funding from UK Research and Innovation, National Institute of Health Research and Wellcome Sanger Institute.

| S-EPMC8285103 | biostudies-literature

Estimates of early outbreak-specific SARS-CoV-2 epidemiological parameters from genomic data.

Project description:We estimate the basic reproductive number and case counts for 15 distinct Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) outbreaks, distributed across 11 populations (10 countries and one cruise ship), based solely on phylodynamic analyses of genomic data. Our results indicate that, prior to significant public health interventions, the reproductive numbers for 10 (out of 15) of these outbreaks are similar, with median posterior estimates ranging between 1.4 and 2.8. These estimates provide a view which is complementary to that provided by those based on traditional line listing data. The genomic-based view is arguably less susceptible to biases resulting from differences in testing protocols, testing intensity, and import of cases into the community of interest. In the analyses reported here, the genomic data primarily provide information regarding which samples belong to a particular outbreak. We observe that once these outbreaks are identified, the sampling dates carry the majority of the information regarding the reproductive number. Finally, we provide genome-based estimates of the cumulative number of infections for each outbreak. For 7 out of 11 of the populations studied, the number of confirmed cases is much bigger than the cumulative number of infections estimated from the sequence data, a possible explanation being the presence of unsequenced outbreaks in these populations.

| S-EPMC10786264 | biostudies-literature

Author Correction: Epidemiological drivers of transmissibility and severity of SARS-CoV-2 in England.

Project description: Not available

| S-EPMC10703916 | biostudies-literature

Epidemiological associations with genomic variation in SARS-CoV-2.

Project description:SARS-CoV-2 (CoV) is the etiological agent of the COVID-19 pandemic and evolves to evade both host immune systems and intervention strategies. We divided the CoV genome into 29 constituent regions and applied novel analytical approaches to identify associations between CoV genomic features and epidemiological metadata. Our results show that nonstructural protein 3 (nsp3) and Spike protein (S) have the highest variation and greatest correlation with the viral whole-genome variation. S protein variation is correlated with nsp3, nsp6, and 3'-to-5' exonuclease variation. Country of origin and time since the start of the pandemic were the most influential metadata associated with genomic variation, while host sex and age were the least influential. We define a novel statistic-coherence-and show its utility in identifying geographic regions (populations) with unusually high (many new variants) or low (isolated) viral phylogenetic diversity. Interestingly, at both global and regional scales, we identify geographic locations with high coherence neighboring regions of low coherence; this emphasizes the utility of this metric to inform public health measures for disease spread. Our results provide a direction to prioritize genes associated with outcome predictors (e.g., health, therapeutic, and vaccine outcomes) and to improve DNA tests for predicting disease status.

| S-EPMC8626494 | biostudies-literature

Genomic epidemiological analysis of SARS-CoV-2 household transmission.

Project description:Family clusters have contributed significantly to the onward spread of SARS-CoV-2. However, the dynamics of viral transmission in this setting remain incompletely understood. We describe the clinical and viral-phylogenetic characteristics of a family cluster of SARS-CoV-2 infections with a high attack rate, and explore how whole-genome sequencing (WGS) can inform outbreak investigations in this context. In this cluster, the first symptomatic case was a 22-month-old infant who developed rhinorrhoea and sneezing 2 days prior to attending a family gathering. Subsequently, seven family members in attendance at this event were diagnosed with SARS-CoV-2 infections, including the infant described. WGS revealed indistinguishable SARS-CoV-2 genomes recovered from the adults at the gathering, which were closely related genetically to B.1 lineage viruses circulating in the local community. However, a divergent viral sub-lineage was recovered from the infant and another child, each harbouring a distinguishing spike substitution (N30S). This suggested that the infant was unlikely to be the primary case, despite displaying symptoms first, and additional analysis of her nasopharyngeal swab revealed a picornavirus co-infection to account for her early symptoms. Our findings demonstrate how WGS can elucidate the transmission dynamics of SARS-CoV-2 infections within household clusters and provide useful information to support outbreak investigations. Additionally, our description of SARS-CoV-2 viral lineages and notable variants circulating in Ireland to date provides an important genomic-epidemiological baseline in the context of vaccine introduction.

| S-EPMC8479960 | biostudies-literature

Genomic epidemiology of SARS-CoV-2 transmission lineages in Ecuador.

Project description:Characterisation of SARS-CoV-2 genetic diversity through space and time can reveal trends in virus importation and domestic circulation, and permit the exploration of questions regarding the early transmission dynamics. Here we present a detailed description of SARS-CoV-2 genomic epidemiology in Ecuador, one of the hardest hit countries during the early stages of the COVID-19 pandemic. We generate and analyse 160 whole genome sequences sampled from all provinces of Ecuador in 2020. Molecular clock and phylgeographic analysis of these sequences in the context of global SARS-CoV-2 diversity enable us to identify and characterise individual transmission lineages within Ecuador, explore their spatiotemporal distributions, and consider their introduction and domestic circulation. Our results reveal a pattern of multiple international importations across the country, with apparent differences between key provinces. Transmission lineages were mostly introduced before the implementation of non-pharmaceutical interventions (NPIs), with differential degrees of persistence and national dissemination.

| S-EPMC8043474 | biostudies-literature

Genomic epidemiology of SARS-CoV-2 transmission lineages in Ecuador.

Project description:Characterisation of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) genetic diversity through space and time can reveal trends in virus importation and domestic circulation and permit the exploration of questions regarding the early transmission dynamics. Here, we present a detailed description of SARS-CoV-2 genomic epidemiology in Ecuador, one of the hardest hit countries during the early stages of the coronavirus-19 pandemic. We generated and analysed 160 whole genome sequences sampled from all provinces of Ecuador in 2020. Molecular clock and phylogeographic analysis of these sequences in the context of global SARS-CoV-2 diversity enable us to identify and characterise individual transmission lineages within Ecuador, explore their spatiotemporal distributions, and consider their introduction and domestic circulation. Our results reveal a pattern of multiple international importations across the country, with apparent differences between key provinces. Transmission lineages were mostly introduced before the implementation of non-pharmaceutical interventions, with differential degrees of persistence and national dissemination.

| S-EPMC8244811 | biostudies-literature

Genomic and epidemiological characteristics of SARS-CoV-2 in Africa.

Project description:Since late 2019, the coronavirus disease 2019 (COVID-19) outbreak, caused by SARS-CoV-2, has rapidly evolved to become a global pandemic. Each country was affected but with a varying number of infected cases and mortality rates. Africa was hit late by the pandemic but the number of cases rose sharply. In this study, we investigated 224 SARS-CoV-2 genome sequences from the Global Initiative on Sharing Avian Influenza Data (GISAID) in the early part of the outbreak, of which 69 were from Africa. We analyzed a total of 550 mutations by comparing them with the reference SARS-CoV-2 sequence from Wuhan. We classified the mutations observed based on country and region, and afterwards analyzed common and unique mutations on the African continent as a whole. Correlation analyses showed that the duo variants ORF1ab/RdRp 4715L and S protein 614G variants, which are strongly linked to fatality rate, were not significantly and positively correlated with fatality rates (r = -0.03757, P = 0.5331 and r = -0.2876, P = 0.6389, respectively), although increased number of cases correlated with number of deaths (r = 0.997, P = 0.0002). Furthermore, most cases in Africa were mainly imported from American and European countries, except one isolate with no mutation and was similar to the original isolate from Wuhan. Moreover, unique mutations specific to countries were identified in the early phase of the outbreak but these mutations were not regional-specific. There were common mutations in all isolates across the continent as well as similar isolate-specific mutations in different regions. Our findings suggest that mutation is rapid in SARS-CoV-2 in Africa and although these mutations spread across the continent, the duo variants could not possibly be the sole cause of COVID-19 deaths in Africa in the early phase of the outbreak.

| S-EPMC8101992 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data