Genomic Epidemiology Data Infrastructure Needs for SARS-CoV-2 Modernizing Pandemic Response Strategies (2020) / Chapter Skim
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4 Framework to Track and Correlate Viral Genome Sequences with Clinical and Epidemiological Data
4 Framework to Track and Correlate Viral Genome Sequences with Clinical and Epidemiological Data
Pages 47-68
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From page 47... ...
but will also be reliant on clinical and epidemiological data to understand the evolution of SARS-CoV-2 and the implications for transmission and clinical manifestations. The collection of clinical data is exceedingly important but also one of the biggest hurdles.
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From page 48... ...
Adequate representation should go beyond geographical considerations, and should also include gender, race, ethnicity, living situation, and occupation. Table 4-1 briefly outlines how viral genome sequence data, when combined with other types of data, can be used to inform questions related to transmission, evolution, and clinical disease.
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From page 49... ...
Is resistance to Changes in viral Hospital or health care antiviral drugs or genome associated center data on patients other treatments with failure to who do not respond to changing? respond to treatment therapy or show failure of treatment Is there altered escape Changes in viral Hospital data on from the host immune genome associated patients who show response/within host with persistence prolonged shedding evolution?
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same community/ viral load, treatment, family with and and response without MIS-C NOTE: ICU = intensive care unit; MIS-C = multisystem inflammatory syndrome in children; R0 = basic reproductive number; RT-PCR = reverse transcription polymerase chain reaction. a The committee recognizes that clinical and epidemiological data often come from very dif ferent data collection sources and efforts, but for the purposes of this table these data needs have been incorporated into one column.
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Of particular epidemiological importance for SARS-CoV-2 is identification of route of transmission, asymptomatic spread, and super-spreading events. Virus sequence data can help identify transmission via different pathways, both expected and unexpected (Holmes et al., 2017)
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Consequently, fundamental insights into the evolutionary trade-offs and genetic relationships between SARS-CoV-2 evolution, virulence, and transmissibility may better inform global preparedness efforts, designed to minimize the impact of consequential coronavirus disease outbreaks of the future (Messenger et al., 1999)
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From page 53... ...
Nevertheless, even if the mutation rate of SARS-CoV-2 remains unchanged, the short generation times of the v irus -- coupled with the very large number of infected human hosts -- create ample opportunity for rare spontaneous mutations to arise and spread over short periods of time, indicating enormous virus evolutionary potential. Is Virus Transmissibility Changing?
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, a highly relevant clinical concern for COVID-19. Therefore, prolonged s ocial distancing could select for SARS-CoV-2 variants with increased particle stability that may or may not affect viral load during infection.
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Nonetheless, identification of virus strains with different clinical features would provide insights into disease pathogenesis and potentially identify patients requiring specific interventions. Linking virus sequence data with data on patient demographics, hospitalization, duration of hospitalization, clinical complications, intensive care unit (ICU)
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Given the uncertainty as to how the current or future pandemics might progress, however, it will also be important to build flexibility and expansion capability in the resultant data management system in order to accommodate additional sources and types of data. A national system for integrating genomic, clinical, and epidemiological data collected during an infectious disease outbreak would receive large volumes of data coming in from multiple sources, including federal, state, and local public health agencies; health care networks; and public health and clinical laboratories.
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For example, a hospital laboratory would submit a comprehensive package of clinical and diagnostic data, while a commercial clinical laboratory would submit a larger population-based data package lacking clinical details. State and local public health agencies would likely have a variety of data types.
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B ecause data accessible in the N3C are a limited dataset under terms preventing re-identification, important epidemiological activities such as contact tracing are not supported; nonetheless, inclusion of SARS-CoV-2 genomic data into N3C would represent a clinically phenotyped collection of viral genomic sequences that could scale to the U.S. population.
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Data from clinical laboratories provide useful information on the timing and intensity of influenza activity from respiratory specimens largely obtained for diagnostic purposes. Public health laboratories provide data useful to understand what influenza virus types, subtypes, and lineages are circulating and the age groups being affected as test specimens are collected primarily for the purposes of surveillance.
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. Although this national network is still in formation, certified electronic medical record vendors and health information exchanges across the country could be leveraged today to facilitate the sharing of clinical metadata that will help public health departments and r esearchers answer critical questions related to SARS-CoV-2 and C OVID-19.
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Most of these systems collect epidemiological data that are provided to public health authorities and research institutions and used to analyze trends and broaden surveillance beyond the traditional, sentinel surveillance approach. For instance, participatory surveillance provides a mechanism to collect information on influenza in the community at large.
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Furthermore, partnerships can help to ensure these data are presented in a way that is beneficial to the end user. For instance, genomic data are of great value for many purposes, but clinical and epidemiological data may be relevant and applicable to patient care and public health outcomes.
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The U.S. Department of Health and Human Services should develop and invest in a national data infrastructure sys tem that constructively builds on existing programmatic infrastructure with the ability to accurately, efficiently, and safely link genomic data, clinical data, epidemiological data, and other relevant data across
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infrastructure to assess the annual risk of seasonal influenza, work could improve usability and coverage of health information exchanges, and other initiatives) , and ensure inclusion of entities with supporting functions across scales -- including private health care systems that provide data or state and local public health laboratories that collect data -- in ongoing system development and evaluation.
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Nature Reviews Genetics 19(12)
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non-federal acute care hospitals in 2017. The Office of the National Coordinator for Health Information Technology.
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2020. National COVID Cohort Collaborative (N3C)
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2020. The D614G mutation in the SARS-CoV-2 spike protein reduces S1 shedding and increases infectivity.
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