Early detection of SARS-CoV-2 variants through dynamic co-mutation network surveillance

Abstract

BackgroundPrecise public health and clinical interventions for the COVID-19 pandemic has spurred a global rush on SARS-CoV-2 variant tracking, but current approaches to variant tracking are challenged by the flood of viral genome sequences leading to a loss of timeliness, accuracy, and reliability. Here, we devised a new co-mutation network framework, aiming to tackle these difficulties in variant surveillance.MethodsTo avoid simultaneous input and modeling of the whole large-scale data, we dynamically investigate the nucleotide covarying pattern of weekly sequences. The community detection algorithm is applied to a co-occurring genomic alteration network constructed from mutation corpora of weekly collected data. Co-mutation communities are identified, extracted, and characterized as variant markers. They contribute to the creation and weekly updates of a community-based variant dictionary tree representing SARS-CoV-2 evolution, where highly similar ones between weeks have been merged to represent the same variants. Emerging communities imply the presence of novel viral variants or new branches of existing variants. This process was benchmarked with worldwide GISAID data and validated using national level data from six COVID-19 hotspot countries.ResultsA total of 235 co-mutation communities were identified after a 120 weeks' investigation of worldwide sequence data, from March 2020 to mid-June 2022. The dictionary tree progressively developed from these communities perfectly recorded the time course of SARS-CoV-2 branching, coinciding with GISAID clades. The time-varying prevalence of these communities in the viral population showed a good match with the emergence and circulation of the variants they represented. All these benchmark results not only exhibited the methodology features but also demonstrated high efficiency in detection of the pandemic variants. When it was applied to regional variant surveillance, our method displayed significantly earlier identification of feature communities of major WHO-named SARS-CoV-2 variants in contrast with Pangolin's monitoring.ConclusionAn efficient genomic surveillance framework built from weekly co-mutation networks and a dynamic community-based variant dictionary tree enables early detection and continuous investigation of SARS-CoV-2 variants overcoming genomic data flood, aiding in the response to the COVID-19 pandemic.

Current Progress in Genomic and Genetic Research on Human Viral Diseases

“…Sathyaseelan et al (2023) developed a CoVe-tracker (SARS-CoV-2 evolution tracker) 7 for quick surveillance of newly emerging mutations/variants/lineages to facilitate the understanding of viral evolution, transmission, and disease epidemiology. Huang et al (2023) developed a genomic surveillance framework and a dynamic community-based variant dictionary tree, which enables early detection and continuous investigation of SARS-CoV-2 variants. Outbreak.…”

Synergizing AI, IoT, and Blockchain for Diagnosing Pandemic Diseases in Smart Cities: Challenges and Opportunities

“…Through real-time monitoring of IoT devices and sensors in smart cities, authorities can act swiftly when responding to disease outbreaks. To achieve this, cities must establish platforms for integrated data fusion and advanced analytics that support coordination across various sources such as healthcare records, social media insights, and environmental sensor observations [88]. Using social media discussions alongside other sources helps identify potential indicators or geographical locations poised for an outbreak before it occurs.…”

“…This real-time data enables quick identification and isolation of potentially infected individuals, helping to contain the spread of the virus. Additionally, South Korea's smart city efforts have been highly effective in monitoring and managing the COVID-19 outbreak [86][87][88][89]. The country established an extensive testing and surveillance system that integrates data from various sources, including healthcare records, mobile phone tracking, and credit card transactions.…”

“…Furthermore, AI techniques such as natural language processing and sentiment analysis can be applied to blockchain data to monitor public sentiment, identify misinformation, and facilitate effective communication strategies during a pandemic. The combination of AI and blockchain in pandemic control systems can enhance transparency, trust, and the overall effectiveness of smart city initiatives [85][86][87][88].…”

The effects of amino acid substitution of spike protein and genomic recombination on the evolution of SARS-CoV-2

“… Sathyaseelan et al (2023) developed a CoVe-tracker (SARS-CoV-2 evolution tracker) 7 for quick surveillance of newly emerging mutations/variants/lineages to facilitate the understanding of viral evolution, transmission, and disease epidemiology. Huang et al (2023) developed a genomic surveillance framework and a dynamic community-based variant dictionary tree, which enables early detection and continuous investigation of SARS-CoV-2 variants. Outbreak.info is a platform for scalable and dynamic surveillance of SARS-CoV-2 variants and mutations, and it relies on shared virus sequences from the GISAID Initiative ( Gangavarapu et al, 2023 ; Tsueng et al, 2023 ).…”