BackgroundThe Expanded Special Project for the Elimination of Neglected Tropical Diseases (ESPEN) was launched in 2019 by the World Health Organization and African nations to combat Neglected Tropical Diseases (NTDs), including Soil-transmitted helminths (STH), which still affect over 1.5 billion people globally. In this study, we present a comprehensive geostatistical analysis of publicly available STH survey data from ESPEN to delineate inter-country disparities in STH prevalence and its environmental drivers while highlighting the strengths and limitations that arise from the use of the ESPEN data. To achieve this, we also propose the use of calibration validation methods to assess the suitability of geostatistical models for disease mapping at the national scale.MethodsWe analysed the most recent survey data with at least 50 geo-referenced observations, and modelled each STH species data (hookworm, roundworm, whipworm) separately. Binomial geostatistical models were developed for each country, exploring associations between STH and environmental covariates, and were validated using the non-randomized probability integral transform. We produced pixel-, subnational-, and country-level prevalence maps for successfully calibrated countries. All the results were made publicly available through an R Shiny application.ResultsAmong 35 countries with STH data that met our inclusion criteria, the reported data years ranged from 2004 to 2018. Models from 25 countries were found to be well-calibrated. Spatial patterns exhibited significant variation in STH species distribution and heterogeneity in spatial correlation scale (1.14 km to 3,027.44 km) and residual spatial variation variance across countries.ConclusionThis study highlights the utility of ESPEN data in assessing spatial variations in STH prevalence across countries using model-based geostatistics. Despite the challenges posed by data sparsity which limit the application of geostatistical models, the insights gained remain crucial for directing focused interventions and shaping future STH assessment strategies within national control programs.

Florida’s COVID-19 Data and Surveillance Dashboard provides clear communication as well as the data that drives decision-making.Key TakeawaysFlorida Department of Health’s dashboard is the public face of the state’s COVID-19 response effort.Opening data to researchers allows many experts to run models and offer insights.Close collaboration fostered in past disasters improves response efforts._Communities around the world are taking strides in mitigating the threat that COVID-19 (coronavirus) poses. Geography and location analysis have a crucial role in better understanding this evolving pandemic.When you need help quickly, Esri can provide data, software, configurable applications, and technical support for your emergency GIS operations. Use GIS to rapidly access and visualize mission-critical information. Get the information you need quickly, in a way that’s easy to understand, to make better decisions during a crisis.Esri’s Disaster Response Program (DRP) assists with disasters worldwide as part of our corporate citizenship. We support response and relief efforts with GIS technology and expertise.More information...

OMOP2OBO Condition Occurrence Mappings V1.0

These mappings were created by the OMOP2OBO mapping algorithm (see links below). OMOP2OBO - the first health system-wide, disease-agnostic mappings between standardized clinical terminologies and eight Open Biomedical Ontology (OBO) Foundry ontologies spanning diseases, phenotypes, anatomical entities, cell types, organisms, chemicals, vaccines, and proteins. These mappings are also the first to be explicitly created using standard terminologies in the Observational Medical Outcomes (OMOP) common data model (CDM), ensuring both semantic and clinical interoperability across a space of N conditions (and N relationships curated in these ontologies).

The mappings in this repository were created between OMOP standard condition occurrence concepts (i.e., SNOMED CT) to the Human Phenotype Ontology (HPO) and the (Mondo). The National Library of Medicine's Unified Medical Language System (UMLS) Semantic Types are first used to filter out all concepts that did not have a biological origin (accidents, injuries, external complications, and findings without clear interpretations). Then, the Semantic Type was used to prioritize the mapping of HPO concepts to findings and symptoms and Mondo to Semantic Types indicative of disease. For these OMOP domains, owl:intersectionOf (“and”), and owl:unionOf (“or”) constructors were used to construct semantically expressive mappings.

Mapping Details Mappings included in this set were generated automatically using OMOP2OBO or through the use of a Bag-of-words embedding model using TF-IDF. Cosine similarity is used to compute similarity scores between all pairwise combinations of OMOP and OBO concepts and ancestor concepts. To improve the efficiency of this process, the algorithm searches only the top 𝑛 most similar results and keeps the top 75th percentile among all pairs with scores >= 0.25. Manually created mappings are also included.

Mapping Categories

Automatic One-to-One Concept: Exact label or synonym, dbXRef, or expert validated mapping @ concept-level; 1:1

Automatic One-to-One Ancestor: Exact label or synonym, dbXRef, or expert validated mapping @ concept ancestor-level; 1:1

Automatic One-to-Many Concept: Exact label or synonym, dbXRef, cosine similarity, or expert validated mapping @ concept-level; 1:Many

Automatic One-to-Many Ancestor: Exact label or synonym, dbXRef, cosine similarity, or expert validated mapping @ concept-level; 1:Many

Manual One-to-One: Hand mapping created using expert suggested resources; 1:1

Manual One-to-Many: Hand mapping created using expert suggested resources; 1:Many

Cosine Similarity: score suggested mapping -- manually verified

UnMapped: No suitable mapping or not mapped type

Mapping Statistics Additional statistics have been provided for the mappings and are shown in the table below. This table presents the counts of OMOP concepts by mapping category and ontology:

    Mapping Category
    HPO
    Mondo




    Automatic One-to-One Concept
    4767
    9097


    Automatic One-to-Many Concept
    150
    885


    Cosine Similarity
    1375
    667


    Automatic One-to-One Ancestor
    13595
    8911


    Automatic One-to-Many Ancestor 
    38080
    40224


    Manual
    5131
    755


    Manual One-to-Many
    10326
    2835


    Unmapped
    36301
    46345

Provenance and Versioning: The V1.0 deposited mappings were created by OMOP2OBO v1.0.0 on October 2022 using the OMOP Common Data Model V5.0 and OBO Foundry ontologies downloaded on September 14, 2020.

Caveats: The deposited files only contain the mappings that were generated automatically by the algorithm. The manually generated mappings will be deposited with the official preprint manuscript. Please note that these are the original mappings that were created for the preprint. They have not been updated to current versions of the ontologies. In our experience, this should result in very few errors, but we do suggest that you check the ontology concepts used against current versions of each ontology before using them.

Important Resources and Documentation

GitHub: OMOP2OBO

Project Wiki: OMOP2OBO - wiki

Zenodo Community: OMOP2OBO

Preprint Manuscript: 10.5281/zenodo.5716421

OMOP2OBO Drug Exposure Ingredient Mappings V1.0

These mappings were created by the OMOP2OBO mapping algorithm (see links below). OMOP2OBO - the first health system-wide, disease-agnostic mappings between standardized clinical terminologies and eight Open Biomedical Ontology (OBO) Foundry ontologies spanning diseases, phenotypes, anatomical entities, cell types, organisms, chemicals, vaccines, and proteins. These mappings are also the first to be explicitly created using standard terminologies in the Observational Medical Outcomes (OMOP) common data model (CDM), ensuring both semantic and clinical interoperability across a space of N conditions [and N relationships curated in these ontologies].

The mappings in this repository were created between OMOP standard drug exposure concepts at the ingredient-level (i.e., RxNorm) to the Chemical Entities of Biological Interest (ChEBI), the National Center for Biotechnology Information Taxon Ontology (NCBITaxon), the Protein Ontology (PRO), and the Vaccine Ontology (VO). All concepts were aligned to at least one ChEBI concept and the remaining ontologies (NCBITaxon, PR, and VO) were mapped by their drug class and/or type (e.g., biologics versus vaccines). For these OMOP domains, owl:intersectionOf (“and”), and owl:unionOf (“or”) constructors were used to construct semantically expressive mappings.

Mapping Details Mappings included in this set were generated automatically using OMOP2OBO or through the use of a Bag-of-words embedding model using TF-IDF. Cosine similarity is used to compute similarity scores between all pairwise combinations of OMOP and OBO concepts and ancestor concepts. To improve the efficiency of this process, the algorithm searches only the top 𝑛 most similar results and keeps the top 75th percentile among all pairs with scores >= 0.25. Manually created mappings are also included.

Mapping Categories

Automatic Exact - Concept: Exact label or synonym, dbXRef, or expert validated mapping @ concept-level; 1:1

Automatic Exact - Ancestor: Exact label or synonym, dbXRef, or expert validated mapping @ concept ancestor-level; 1:1

Automatic Constructor - Concept: Exact label or synonym, dbXRef, cosine similarity, or expert validated mapping @ concept-level; 1:Many

Automatic Constructor - Ancestor: Exact label or synonym, dbXRef, cosine similarity, or expert validated mapping @ concept-level; 1:Many

Manual: Hand mapping created using expert suggested resources; 1:1

Manual Constructor: Hand mapping created using expert suggested resources; 1:Many

Concept Similarity: score suggested mapping -- manually verified

UnMapped: No suitable mapping or not mapped type

Mapping Statistics Additional statistics have been provided for the mappings and are shown in the table below. This table presents the counts of OMOP concepts by mapping category and ontology:

    Mapping category
    ChEBI
    NCBITaxon
    PRO
    VO




    Automatic Exact - Concept
    3151
    155
    43
    108


    Automatic Constructor - Constructor
    404
    1
    1
    0


    Automatic Exact - Ancestor
    147
    17
    20
    4


    Automatic Constructor - Ancestor
    210
    3
    2
    2


    Concept Similarity
    109
    4241
    18
    17


    Manual
    322
    230
    157
    21


    Manual Constructor
    72
    14
    8
    2


    UnMapped
    7392
    7146
    11558
    11653

Provenance and Versioning: The V1.0 deposited mappings were created by OMOP2OBO v1.0.0 on October 2022 using the OMOP Common Data Model V5.0 and OBO Foundry ontologies downloaded on September 14, 2020.

Caveats: Please note that these are the original mappings that were created for the preprint. They have not been updated to current versions of the ontologies. In our experience, this should result in very few errors, but we do suggest that you check the ontology concepts used against current versions of each ontology before using them.

Important Resources and Documentation

GitHub: OMOP2OBO

Project Wiki: OMOP2OBO - wiki

Zenodo Community: OMOP2OBO

Preprint Manuscript: 10.5281/zenodo.5716421

The recent discovery of a new circovirus, named Circovirus parisii (C. parisii), in a French hepatitis patient is a notable development in virology and public health. Typically infecting animals, this finding challenges the existing belief that circoviruses do not infect humans. The virus was identified using advanced techniques like shotgun metagenomics and PCR, and it shares characteristics with other circoviruses, along with a unique gene of undetermined function.This discovery is significant in the context of zoonotic diseases, especially considering the global impact of diseases like COVID-19. Research indicates that circoviruses, such as PCV2, can infect human cells in culture and may interact with the human immune system. Concerns have been raised about circovirus contamination in human vaccines, emphasizing the need for strict safety protocols in vaccine production.Further research involving sequence comparison and in silico analysis of the virus's cap protein revealed significant amino acid mutations that could affect the virus's infectivity and pathogenicity. This analysis identified potential epitopes for vaccine development.The case underscores the need for comprehensive research into viruses, including circoviruses, that could infect humans. It highlights the importance of identifying vulnerable populations, developing effective vaccines and therapies, and continuing surveillance and research on emerging viruses to protect public health.

Country-level predicted prevalence estimates and associated 95% confidence intervals.

Additional file 2: Read pairs with one mate mapping to the Bos taurus genome and one mapping to BoRV CH15.

Monte Carlo maximum likelihood estimates and associated 95% confidence intervals for the model in Eq 2 for Rwanda.

List of diseases in the directed network with columns Phenocode: phenocode of disease, Disease: disease name, Prev: prevalence in HUNT, Dprev: mortality rate in HUNT (number of death due to this disease divided by the prevalence, note that one disease has mortality rate >1, as a result of participants dying of this disease before a registered visit to the hospital or general practitioner), Category: phenocode category, Sink: (0/1) if the disease is a sink node, and Level: the phenocode level of the disease. (TXT)

Background: Given the worldwide spread of the 2019 Novel Coronavirus (COVID-19), there is an urgent need to identify risk and protective factors and expose areas of insufficient understanding. Emerging tools, such as the Rapid Evidence Map (rEM), are being developed to systematically characterize large collections of scientific literature. We sought to generate an rEM of risk and protective factors to comprehensively inform areas that impact COVID-19 outcomes for different sub-populations in order to better protect the public.Methods: We developed a protocol that includes a study goal, study questions, a PECO statement, and a process for screening literature by combining semi-automated machine learning with the expertise of our review team. We applied this protocol to reports within the COVID-19 Open Research Dataset (CORD-19) that were published in early 2020. SWIFT-Active Screener was used to prioritize records according to pre-defined inclusion criteria. Relevant studies were categorized by risk and protective status; susceptibility category (Behavioral, Physiological, Demographic, and Environmental); and affected sub-populations. Using tagged studies, we created an rEM for COVID-19 susceptibility that reveals: (1) current lines of evidence; (2) knowledge gaps; and (3) areas that may benefit from systematic review.Results: We imported 4,330 titles and abstracts from CORD-19. After screening 3,521 of these to achieve 99% estimated recall, 217 relevant studies were identified. Most included studies concerned the impact of underlying comorbidities (Physiological); age and gender (Demographic); and social factors (Environmental) on COVID-19 outcomes. Among the relevant studies, older males with comorbidities were commonly reported to have the poorest outcomes. We noted a paucity of COVID-19 studies among children and susceptible sub-groups, including pregnant women, racial minorities, refugees/migrants, and healthcare workers, with few studies examining protective factors.Conclusion: Using rEM analysis, we synthesized the recent body of evidence related to COVID-19 risk and protective factors. The results provide a comprehensive tool for rapidly elucidating COVID-19 susceptibility patterns and identifying resource-rich/resource-poor areas of research that may benefit from future investigation as the pandemic evolves.

List of diseases in the PheNet with their phenocodes names displayed in the networks, disease categories, in which module they are located in the PheNet and in the HUNT sub-PheNet and the H-score in the PheNet and HUNT sub-PheNet. (TXT)

Integrated Geodatabase: The Global Catholic Foortprint of Care for the Vulnerable and ChildrenBurhans, Molly A., Mrowczynski, Jon M., Schweigel, Tayler C., and Burhans, Debra T., Wacta, Christine. The Catholic Foortprint of Care Around the World (1). GoodLands and GHR Foundation, 2019.Catholic Statistics Numbers:Annuarium Statisticum Ecclesiae – Statistical Yearbook of the Church: 1980 – 2018. LIBRERIA EDITRICE VATICAN.Historical Country Boundary Geodatabase:Weidmann, Nils B., Doreen Kuse, and Kristian Skrede Gleditsch. The Geography of the International System: The CShapes Dataset. International Interactions 36 (1). 2010.GoodLands created a significant new data set of important Church information regarding orphanages and sisters around the world as well as healthcare, welfare, and other child care institutions. The data was extracted from the gold standard of Church data, the Annuarium Statisticum Ecclesiae, published yearly by the Vatican. It is inevitable that raw data sources will contain errors. GoodLands and its partners are not responsible for misinformation within Vatican documents. We encourage error reporting to us at data@good-lands.org or directly to the Vatican.GoodLands worked with the GHR Foundation to map Catholic Healthcare around the world using data mined from the Annuarium Statisticum Eccleasiea.The workflows and data models developed for this project can be used to map any global, historical country-scale data in a time-series map while accounting for country boundary changes. GoodLands created proprietary software that enables mining the Annuarium Statisticum Eccleasiea (see Software and Program Library at the bottom of this page for details).

The recently proposed concept of molecular connectivity maps enables researchers to integrate experimental measurements of genes, proteins, metabolites, and drug compounds under similar biological conditions. The study of these maps provides opportunities for future toxicogenomics and drug discovery applications. We developed a computational framework to build disease-specific drug-protein connectivity maps. We integrated gene/protein and drug connectivity information based on protein interaction networks and literature mining, without requiring gene expression profile information derived from drug perturbation experiments on disease samples. We described the development and application of this computational framework using Alzheimer's Disease (AD) as a primary example in three steps. First, molecular interaction networks were incorporated to reduce bias and improve relevance of AD seed proteins. Second, PubMed abstracts were used to retrieve enriched drug terms that are indirectly associated with AD through molecular mechanistic studies. Third and lastly, a comprehensive AD connectivity map was created by relating enriched drugs and related proteins in literature. We showed that this molecular connectivity map development approach outperformed both curated drug target databases and conventional information retrieval systems. Our initial explorations of the AD connectivity map yielded a new hypothesis that diltiazem and quinidine may be investigated as candidate drugs for AD treatment. Molecular connectivity maps derived computationally can help study molecular signature differences between different classes of drugs in specific disease contexts. To achieve overall good data coverage and quality, a series of statistical methods have been developed to overcome high levels of data noise in biological networks and literature mining results. Further development of computational molecular connectivity maps to cover major disease areas will likely set up a new model for drug development, in which therapeutic/toxicological profiles of candidate drugs can be checked computationally before costly clinical trials begin.

An edgelist containing the disease pairs linked in the PheNet with columns From/To: the diseases, N: number of shared SNPs/LD-blocks, betaMerged: the β-score of the link, FromCode/ToCode: phenocode of the diseases, SNPs: which SNPs that are shared between the diseases. (TXT)

Mean H-score with corresponding Bonferroni adjusted p-values for each module.

Plant disease evaluation is crucial to pathogen management and plant breeding. Human field scouting has been widely used to monitor disease progress and provide qualitative and quantitative evaluation, which is costly, laborious, subjective, and often imprecise. To improve disease evaluation accuracy, throughput, and objectiveness, an image-based approach with a deep learning-based analysis pipeline was developed to calculate infection severity of grape foliar diseases. The image-based approach used a ground imaging system for field data acquisition, consisting of a custom stereo camera with strobe light for consistent illumination and real time kinematic (RTK) GPS for accurate localization. The deep learning-based pipeline used the hierarchical multiscale attention semantic segmentation (HMASS) model for disease infection segmentation, color filtering for grapevine canopy segmentation, and depth and location information for effective region masking. The resultant infection, canopy, and effective region masks were used to calculate the severity rate of disease infections in an image sequence collected in a given unit (e.g., grapevine panel). Fungicide trials for grape downy mildew (DM) and powdery mildew (PM) were used as case studies to evaluate the developed approach and pipeline. Experimental results showed that the HMASS model achieved acceptable to good segmentation accuracy of DM (mIoU > 0.84) and PM (mIoU > 0.74) infections in testing images, demonstrating the model capability for symptomatic disease segmentation. With the consistent image quality and multimodal metadata provided by the imaging system, the color filter and overlapping region removal could accurately and reliably segment grapevine canopies and identify repeatedly imaged regions between consecutive image frames, leading to critical information for infection severity calculation. Image-derived severity rates were highly correlated (r > 0.95) with human-assessed values, and had comparable statistical power in differentiating fungicide treatment efficacy in both case studies. Therefore, the developed approach and pipeline can be used as an effective and efficient tool to quantify the severity of foliar disease infections, enabling objective, high-throughput disease evaluation for fungicide trial evaluation, genetic mapping, and breeding programs.

Humanitarian crises, particularly in conflict zones, create cascading disruptions that impact every aspect of daily life, including health and disease outcomes. While international humanitarian frameworks categorize these crises into discrete operational clusters, affected populations experience them as interwoven, systemic failures. This study examines how conflict-induced disruptions transform a preventable and typically self-limiting disease—Hepatitis A—into a fatal outcome. Using a systems approach, we seek to characterize the architecture of interconnected disruptions leading to preventable deaths. This study employed the FAIR (Fairness, Agency, Inclusion, and Representation) Framework, a participatory methodology centering community epistemes, to analyze four pediatric cases of Hepatitis A that progressed to fulminant liver failure. Data were obtained through interviews with healthcare providers, caregivers, and community members, supplemented by medical chart reviews. A network-based Architecture of Systems (AoS) map was constructed to visualize interconnections between war-induced systemic disruptions and health outcomes. Network analysis identified key nodes and pathways within the systems map. The findings of this study reveal a complex system of war-driven factors including displacement, destruction of healthcare infrastructure, water scarcity, food deprivation, and fuel blockades that collectively reshaped disease trajectories. Network analysis of the AoS map identified 138 nodes and 231 edges, generating 34,458 pathways linking conflict-related disruptions to health outcomes. Women’s health emerged as a central mediator, with 97% of pathways intersecting with 25 key nodes including women’s roles in caregiving, resource acquisition, and psychological stability. The lack of access to food and clean water, combined with the destruction of healthcare facilities and restrictions on medical evacuation, created conditions where preventable, self-limiting diseases become fatal. This study highlights how conflict restructures health determinants, turning survival strategies into pathways of increasing morbidity and mortality. It also underscores the need for a systems-based humanitarian response that considers the intersecting pathways driving outcomes in crisis settings.

Main features of the molecular maps developed in P ×canadensis F1 population.