Defining the risk of SARS-CoV-2 variants on immune protection.

The global emergence of many severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants jeopardizes the protective antiviral immunity induced after infection or vaccination. To address the public health threat caused by the increasing SARS-CoV-2 genomic diversity, the National Institute of Allergy and Infectious Diseases within the National Institutes of Health established the SARS-CoV-2 Assessment of Viral Evolution (SAVE) programme. This effort was designed to provide a real-time risk assessment of SARS-CoV-2 variants that could potentially affect the transmission, virulence, and resistance to infection- and vaccine-induced immunity. The SAVE programme is a critical data-generating component of the US Government SARS-CoV-2 Interagency Group to assess implications of SARS-CoV-2 variants on diagnostics, vaccines and therapeutics, and for communicating public health risk. Here we describe the coordinated approach used to identify and curate data about emerging variants, their impact on immunity and effects on vaccine protection using animal models. We report the development of reagents, methodologies, models and notable findings facilitated by this collaborative approach and identify future challenges. This programme is a template for the response to rapidly evolving pathogens with pandemic potential by monitoring viral evolution in the human population to identify variants that could reduce the effectiveness of countermeasures.

NIAID/SMB Workshop on Multiscale Modeling of Infectious and Immune-Mediated Diseases.

On July 19th, 2023, the National Institute of Allergy and Infectious Diseases co-organized a workshop with the Society of Mathematical Biology, with the authors of this paper as the organizing committee. The workshop, "Bridging multiscale modeling and practical clinical applications in infectious diseases" sought to create an environment for mathematical modelers, statisticians, and infectious disease researchers and clinicians to exchange ideas and perspectives.

The Landscape of NIAID-funded Observational COVID-19 Cohort Studies.

The spread of SARS-CoV-2 since late 2019 represented an unprecedented public health emergency, which included a need to fully understand COVID-19 disease across all ages and populations. In response, the US National Institute of Allergy and Infectious Diseases (NIAID) rapidly funded epidemiology studies that monitored COVID-19. However, the diversity and breadth of the populations studied in NIAID-funded COVID-19 observational cohorts were not easy to extrapolate because of siloed approaches to collect and report data within NIAID. Here, we describe the effort to develop a harmonized cohort study reporting tool that includes common epidemiological data elements as well as NIAID priorities. We report its implementation to analyze metadata from 58 COVID-19 cohort studies funded February 2020 to June 2021, visualize key metadata including geographic distribution, study duration, participant demographics, sample types collected, and scientific priorities addressed. A bibliographic analysis highlights the scientific publications and citations across these funded studies and demonstrates their enormous impact on the COVID-19 field. These analyses highlight how common data elements and reporting tools can assist funding agencies to capture the landscape and potential gaps during public health responses and how they can assist in decision making.

Developing a standardized but extendable framework to increase the findability of infectious disease datasets.

Biomedical datasets are increasing in size, stored in many repositories, and face challenges in FAIRness (findability, accessibility, interoperability, reusability). As a Consortium of infectious disease researchers from 15 Centers, we aim to adopt open science practices to promote transparency, encourage reproducibility, and accelerate research advances through data reuse. To improve FAIRness of our datasets and computational tools, we evaluated metadata standards across established biomedical data repositories. The vast majority do not adhere to a single standard, such as Schema.org, which is widely-adopted by generalist repositories. Consequently, datasets in these repositories are not findable in aggregation projects like Google Dataset Search. We alleviated this gap by creating a reusable metadata schema based on Schema.org and catalogued nearly 400 datasets and computational tools we collected. The approach is easily reusable to create schemas interoperable with community standards, but customized to a particular context. Our approach enabled data discovery, increased the reusability of datasets from a large research consortium, and accelerated research. Lastly, we discuss ongoing challenges with FAIRness beyond discoverability.

COVID-19 SeroHub, an online repository of SARS-CoV-2 seroprevalence studies in the United States.

Seroprevalence studies provide useful information about the proportion of the population either vaccinated against SARS-CoV-2, previously infected with the virus, or both. Numerous studies have been conducted in the United States, but differ substantially by dates of enrollment, target population, geographic location, age distribution, and assays used. This can make it challenging to identify and synthesize available seroprevalence data by geographic region or to compare infection-induced versus combined infection- and vaccination-induced seroprevalence. To facilitate public access and understanding, the National Institutes of Health and the Centers for Disease Control and Prevention developed the COVID-19 Seroprevalence Studies Hub (COVID-19 SeroHub, https://covid19serohub.nih.gov/ ), a data repository in which seroprevalence studies are systematically identified, extracted using a standard format, and summarized through an interactive interface. Within COVID-19 SeroHub, users can explore and download data from 178 studies as of September 1, 2022. Tools allow users to filter results and visualize trends over time, geography, population, age, and antigen target. Because COVID-19 remains an ongoing pandemic, we will continue to identify and include future studies.

Parsing Immune Correlates of Protection Against SARS-CoV-2 from Biomedical Literature.

After the emergence of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) in 2019, identification of immune correlates of protection (CoPs) have become increasingly important to understand the immune response to SARS-CoV-2. The vast amount of preprint and published literature related to COVID-19 makes it challenging for researchers to stay up to date on research results regarding CoPs against SARS-CoV-2. To address this problem, we developed a machine learning classifier to identify papers relevant to CoPs and a customized named entity recognition (NER) model to extract terms of interest, including CoPs, vaccines, assays, and animal models. A user-friendly visualization tool was populated with the extracted and normalized NER results and associated publication information including links to full-text articles and clinical trial information where available. The goal of this pilot project is to provide a basis for developing real-time informatics platforms that can inform researchers with scientific insights from emerging research.

Accelerating Biomedical Discoveries through Rigor and Transparency.

Difficulties in reproducing published research findings have garnered a lot of press in recent years. As a funder of biomedical research, the National Institutes of Health (NIH) has taken measures to address underlying causes of low reproducibility. Extensive deliberations resulted in a policy, released in 2015, to enhance reproducibility through rigor and transparency. We briefly explain what led to the policy, describe its elements, provide examples and resources for the biomedical research community, and discuss the potential impact of the policy on translatability with a focus on research using animal models. Importantly, while increased attention to rigor and transparency may lead to an increase in the number of laboratory animals used in the near term, it will lead to more efficient and productive use of such resources in the long run. The translational value of animal studies will be improved through more rigorous assessment of experimental variables and data, leading to better assessments of the translational potential of animal models, for the benefit of the research community and society.