Toward a global virus genomic surveillance network

Cell Host & Microbe, Год журнала: 2023, Номер 31(6), С. 861 - 873

Опубликована: Март 6, 2023

Язык: Английский

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Global SARS-CoV-2 genomic surveillance: What we have learned (so far)

Infection Genetics and Evolution, Год журнала: 2023, Номер 108, С. 105405 - 105405

Опубликована: Янв. 18, 2023

The COVID-19 pandemic has brought significant challenges for genomic surveillance strategies in public health systems worldwide. During the past thirty-four months, many countries faced several epidemic waves of SARS-CoV-2 infections, driven mainly by emergence and spread novel variants. In that line, been a crucial toolkit to study real-time evolution, assessment optimization diagnostic assays, improve efficacy existing vaccines. pandemic, identification emerging lineages carrying lineage-specific mutations (particularly those Receptor Binding domain) showed how these might significantly impact viral transmissibility, protection from reinfection vaccination. So far, an unprecedented number genomes released databases (i.e., GISAID, NCBI), achieving 14 million genome sequences available as early-November 2022. present review, we summarise global landscape during first months circulation evolution. It demonstrates urgency importance sustained investment timely identify any potential pathogen or associated variants, which turn is key preparedness.

Язык: Английский

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Real-time genomic surveillance for enhanced control of infectious diseases and antimicrobial resistance

Frontiers in Science, Год журнала: 2024, Номер 2

Опубликована: Апрель 25, 2024

This article advocates for mobilizing pathogen genomic surveillance to contain and mitigate health threats from infectious diseases antimicrobial resistance (AMR), building upon successes achieved by large-scale genome sequencing analysis of SARS-CoV-2 variants in guiding COVID-19 monitoring public responses adopting a One Health approach. Capabilities laboratory-based epidemic alert systems should be enhanced fostering (i) universal access real-time whole sequence (WGS) data pathogens inform clinical practice, infection control, policies, vaccine drug research development; (ii) integration diagnostic microbiology data, testing asymptomatic individuals, epidemiological into programs; (iii) stronger cross-sectorial collaborations between healthcare, health, animal environmental using approaches, toward understanding the ecology transmission pathways AMR across ecosystems; (iv) international collaboration interconnection networks, harmonization laboratory methods, standardization methods global reporting, including on variant or strain nomenclature; (v) responsible sharing databases, platforms according FAIR (findability, accessibility, interoperability, reusability) principles; (vi) system implementation its cost-effectiveness different settings. Regional policies governance initiatives foster concerted development efficient utilization protect humans, animals, environment.

Язык: Английский

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Incorporating variant frequencies data into short-term forecasting for COVID-19 cases and deaths in the USA: a deep learning approach

EBioMedicine, Год журнала: 2023, Номер 89, С. 104482 - 104482

Опубликована: Фев. 22, 2023

Язык: Английский

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The genetic architecture of protein stability

Nature, Год журнала: 2024, Номер unknown

Опубликована: Сен. 25, 2024

Язык: Английский

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Genomic surveillance as a scalable framework for precision phage therapy against antibiotic-resistant pathogens

Cell, Год журнала: 2024, Номер unknown

Опубликована: Сен. 1, 2024

Язык: Английский

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Phylogenomic early warning signals for SARS-CoV-2 epidemic waves

EBioMedicine, Год журнала: 2024, Номер 100, С. 104939 - 104939

Опубликована: Янв. 8, 2024

Epidemic waves of coronavirus disease 2019 (COVID-19) infections have often been associated with the emergence novel severe acute respiratory syndrome 2 (SARS-CoV-2) variants. Rapid detection growing genomic variants can therefore serve as a predictor future waves, enabling timely implementation countermeasures such non-pharmaceutical interventions (social distancing), additional vaccination (booster campaigns), or healthcare capacity adjustments. The large amount SARS-CoV-2 sequence data produced during pandemic has provided unique opportunity to explore utility these for generating early warning signals (EWS).

Язык: Английский

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Pathogen genomic surveillance status among lower resource settings in Asia

Nature Microbiology, Год журнала: 2024, Номер unknown

Опубликована: Сен. 24, 2024

Язык: Английский

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The genetic architecture of protein stability

bioRxiv (Cold Spring Harbor Laboratory), Год журнала: 2023, Номер unknown

Опубликована: Окт. 27, 2023

Abstract There are more ways to synthesize a 100 amino acid protein (20 ) than atoms in the universe. Only miniscule fraction of such vast sequence space can ever be experimentally or computationally surveyed. Deep neural networks increasingly being used navigate high-dimensional spaces. However, these models extremely complicated and provide little insight into fundamental genetic architecture proteins. Here, by exploring spaces >10 10 , we show that at least some proteins is remarkably simple, allowing accurate prediction with fully interpretable biophysical models. These capture non-linear relationships between free energies phenotypes but otherwise consist additive energy changes small contribution from pairwise energetic couplings. couplings sparse caused structural contacts backbone propagations. Our results suggest artificial intelligence may vastly they modeling genetics actually both simple intelligible.

Язык: Английский

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Advancing Real-time Pandemic Forecasting Using Large Language Models: A COVID-19 Case Study

Research Square (Research Square), Год журнала: 2024, Номер unknown

Опубликована: Май 3, 2024

Forecasting the short-term spread of an ongoing disease outbreak is a formidable challenge due to complexity contributing factors, some which can be characterized through interlinked, multi-modality variables such as epidemiological time series data, viral biology, population demographics, and intersection public policy human behavior. Existing forecasting model frameworks struggle with multifaceted nature relevant data robust results translation, hinders their performances provision actionable insights for health decision-makers. Our work introduces PandemicLLM, novel framework multi-modal Large Language Models (LLMs) that reformulates real-time text reasoning problem, ability incorporate real-time, complex, non-numerical information -- textual policies genomic surveillance previously unattainable in traditional models. This approach, unique AI-human cooperative prompt design representation learning, encodes LLMs. By redefining process ordinal classification task, PandemicLLM yields more trustworthy predictions, facilitating decision-making. The applied COVID-19 pandemic, trained utilize policies, surveillance, spatial, subsequently tested across all 50 states U.S. duration 16 weeks. Empirically, shown high-performing pandemic effectively captures impact emerging variants provide timely accurate predictions. proposed opens avenues incorporating various pandemic-related heterogeneous formats exhibits performance benefits over existing study illuminates potential adapting LLMs learning enhance forecasting, illustrating how AI innovations strengthen responses crisis management future.

Язык: Английский

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