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Lin L, Dacal E, Díez N, Carmona C, Martin Ramirez A, Barón Argos L, Bermejo-Peláez D, Caballero C, Cuadrado D, Darias-Plasencia O, García-Villena J, Bakardjiev A, Postigo M, Recalde-Jaramillo E, Flores-Chavez M, Santos A, Ledesma-Carbayo MJ, Rubio JM, Luengo-Oroz M. Edge Artificial Intelligence (AI) for real-time automatic quantification of filariasis in mobile microscopy. PLoS Negl Trop Dis 2024; 18:e0012117. [PMID: 38630833 PMCID: PMC11057975 DOI: 10.1371/journal.pntd.0012117] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2023] [Revised: 04/29/2024] [Accepted: 03/29/2024] [Indexed: 04/19/2024] Open
Abstract
Filariasis, a neglected tropical disease caused by roundworms, is a significant public health concern in many tropical countries. Microscopic examination of blood samples can detect and differentiate parasite species, but it is time consuming and requires expert microscopists, a resource that is not always available. In this context, artificial intelligence (AI) can assist in the diagnosis of this disease by automatically detecting and differentiating microfilariae. In line with the target product profile for lymphatic filariasis as defined by the World Health Organization, we developed an edge AI system running on a smartphone whose camera is aligned with the ocular of an optical microscope that detects and differentiates filarias species in real time without the internet connection. Our object detection algorithm that uses the Single-Shot Detection (SSD) MobileNet V2 detection model was developed with 115 cases, 85 cases with 1903 fields of view and 3342 labels for model training, and 30 cases with 484 fields of view and 873 labels for model validation before clinical validation, is able to detect microfilariae at 10x magnification and distinguishes four species of them at 40x magnification: Loa loa, Mansonella perstans, Wuchereria bancrofti, and Brugia malayi. We validated our augmented microscopy system in the clinical environment by replicating the diagnostic workflow encompassed examinations at 10x and 40x with the assistance of the AI models analyzing 18 samples with the AI running on a middle range smartphone. It achieved an overall precision of 94.14%, recall of 91.90% and F1 score of 93.01% for the screening algorithm and 95.46%, 97.81% and 96.62% for the species differentiation algorithm respectively. This innovative solution has the potential to support filariasis diagnosis and monitoring, particularly in resource-limited settings where access to expert technicians and laboratory equipment is scarce.
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Affiliation(s)
- Lin Lin
- Spotlab, Madrid, Spain
- Biomedical Image Technologies, ETSI Telecomunicación, Universidad Politécnica de Madrid, Madrid, Spain
- CIBER de Bioingeniería, Biomateriales y Nanomedicina, Instituto de Salud Carlos III, Madrid, Spain
| | | | | | - Claudia Carmona
- Malaria and Emerging Parasitic Diseases Laboratory, National Microbiology Centre, Instituto de Salud Carlos III—Madrid, Madrid, Spain
| | - Alexandra Martin Ramirez
- Malaria and Emerging Parasitic Diseases Laboratory, National Microbiology Centre, Instituto de Salud Carlos III—Madrid, Madrid, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Infecciosas (CIBERINFEC) Instituto de Salud Carlos III—Madrid, Madrid, Spain
| | - Lourdes Barón Argos
- Malaria and Emerging Parasitic Diseases Laboratory, National Microbiology Centre, Instituto de Salud Carlos III—Madrid, Madrid, Spain
| | | | | | | | | | | | | | | | - Ethan Recalde-Jaramillo
- Spotlab, Madrid, Spain
- Biomedical Image Technologies, ETSI Telecomunicación, Universidad Politécnica de Madrid, Madrid, Spain
- CIBER de Bioingeniería, Biomateriales y Nanomedicina, Instituto de Salud Carlos III, Madrid, Spain
| | - Maria Flores-Chavez
- Malaria and Emerging Parasitic Diseases Laboratory, National Microbiology Centre, Instituto de Salud Carlos III—Madrid, Madrid, Spain
- Fundación Mundo Sano, Madrid, Spain
| | - Andrés Santos
- Biomedical Image Technologies, ETSI Telecomunicación, Universidad Politécnica de Madrid, Madrid, Spain
- CIBER de Bioingeniería, Biomateriales y Nanomedicina, Instituto de Salud Carlos III, Madrid, Spain
| | - María Jesús Ledesma-Carbayo
- Biomedical Image Technologies, ETSI Telecomunicación, Universidad Politécnica de Madrid, Madrid, Spain
- CIBER de Bioingeniería, Biomateriales y Nanomedicina, Instituto de Salud Carlos III, Madrid, Spain
| | - José M. Rubio
- Malaria and Emerging Parasitic Diseases Laboratory, National Microbiology Centre, Instituto de Salud Carlos III—Madrid, Madrid, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Infecciosas (CIBERINFEC) Instituto de Salud Carlos III—Madrid, Madrid, Spain
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2
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Bermejo-Peláez D, Rueda Charro S, García Roa M, Trelles-Martínez R, Bobes-Fernández A, Hidalgo Soto M, García-Vicente R, Morales ML, Rodríguez-García A, Ortiz-Ruiz A, Blanco Sánchez A, Mousa Urbina A, Álamo E, Lin L, Dacal E, Cuadrado D, Postigo M, Vladimirov A, Garcia-Villena J, Santos A, Ledesma-Carbayo MJ, Ayala R, Martínez-López J, Linares M, Luengo-Oroz M. Digital Microscopy Augmented by Artificial Intelligence to Interpret Bone Marrow Samples for Hematological Diseases. Microsc Microanal 2024; 30:151-159. [PMID: 38302194 DOI: 10.1093/micmic/ozad143] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/07/2023] [Revised: 11/15/2023] [Accepted: 12/22/2023] [Indexed: 02/03/2024]
Abstract
Analysis of bone marrow aspirates (BMAs) is an essential step in the diagnosis of hematological disorders. This analysis is usually performed based on a visual examination of samples under a conventional optical microscope, which involves a labor-intensive process, limited by clinical experience and subject to high observer variability. In this work, we present a comprehensive digital microscopy system that enables BMA analysis for cell type counting and differentiation in an efficient and objective manner. This system not only provides an accessible and simple method to digitize, store, and analyze BMA samples remotely but is also supported by an Artificial Intelligence (AI) pipeline that accelerates the differential cell counting process and reduces interobserver variability. It has been designed to integrate AI algorithms with the daily clinical routine and can be used in any regular hospital workflow.
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Affiliation(s)
| | | | - María García Roa
- Department of Hematology, Hospital Universitario Fundación Alcorcón, C. Budapest, 1, Alcorcón 28922, Madrid, Spain
| | - Roberto Trelles-Martínez
- Department of Hematology, Hospital Universitario Fundación Alcorcón, C. Budapest, 1, Alcorcón 28922, Madrid, Spain
| | - Alejandro Bobes-Fernández
- Department of Hematology, Hospital Universitario Fundación Alcorcón, C. Budapest, 1, Alcorcón 28922, Madrid, Spain
| | - Marta Hidalgo Soto
- Vall Hebron Institute of Oncology (VHIO), Carrer de Natzaret, 115-117, Horta-Guinardó, Barcelona 08035, Spain
| | - Roberto García-Vicente
- Department of Translational Hematology, Research Institute Hospital 12 de Octubre (imas12), Av. de Córdoba, s/n, Madrid 28041, Spain
- Hematological Malignancies Clinical Research Unit H120-CNIO, CIBERONC, C. de Melchor Fernández Almagro, 3, Madrid 28029, Spain
| | - María Luz Morales
- Department of Translational Hematology, Research Institute Hospital 12 de Octubre (imas12), Av. de Córdoba, s/n, Madrid 28041, Spain
- Hematological Malignancies Clinical Research Unit H120-CNIO, CIBERONC, C. de Melchor Fernández Almagro, 3, Madrid 28029, Spain
| | - Alba Rodríguez-García
- Department of Translational Hematology, Research Institute Hospital 12 de Octubre (imas12), Av. de Córdoba, s/n, Madrid 28041, Spain
- Hematological Malignancies Clinical Research Unit H120-CNIO, CIBERONC, C. de Melchor Fernández Almagro, 3, Madrid 28029, Spain
| | - Alejandra Ortiz-Ruiz
- Department of Translational Hematology, Research Institute Hospital 12 de Octubre (imas12), Av. de Córdoba, s/n, Madrid 28041, Spain
- Hematological Malignancies Clinical Research Unit H120-CNIO, CIBERONC, C. de Melchor Fernández Almagro, 3, Madrid 28029, Spain
| | - Alberto Blanco Sánchez
- Department of Hematology, Hospital Universitario 12 de Octubre, Av. de Córdoba, s/n, Madrid 28041, Spain
| | | | - Elisa Álamo
- Spotlab, P.º de Juan XXIII, 36B, Madrid 28040, Spain
| | - Lin Lin
- Spotlab, P.º de Juan XXIII, 36B, Madrid 28040, Spain
- Biomedical Image Technologies Laboratory, ETSI Telecomunicación, Universidad Politécnica de Madrid, Av. Complutense, 30, Madrid 28040, Spain
| | - Elena Dacal
- Spotlab, P.º de Juan XXIII, 36B, Madrid 28040, Spain
| | | | - María Postigo
- Spotlab, P.º de Juan XXIII, 36B, Madrid 28040, Spain
| | | | | | - Andrés Santos
- Biomedical Image Technologies Laboratory, ETSI Telecomunicación, Universidad Politécnica de Madrid, Av. Complutense, 30, Madrid 28040, Spain
- CIBER de Bioingeniería, Biomateriales y Nanomedicina, Instituto de Salud Carlos III, C. de Melchor Fernández Almagro, 3, Madrid 28029, Spain
| | - María Jesús Ledesma-Carbayo
- Biomedical Image Technologies Laboratory, ETSI Telecomunicación, Universidad Politécnica de Madrid, Av. Complutense, 30, Madrid 28040, Spain
- CIBER de Bioingeniería, Biomateriales y Nanomedicina, Instituto de Salud Carlos III, C. de Melchor Fernández Almagro, 3, Madrid 28029, Spain
| | - Rosa Ayala
- Department of Translational Hematology, Research Institute Hospital 12 de Octubre (imas12), Av. de Córdoba, s/n, Madrid 28041, Spain
- Hematological Malignancies Clinical Research Unit H120-CNIO, CIBERONC, C. de Melchor Fernández Almagro, 3, Madrid 28029, Spain
- Department of Hematology, Hospital Universitario 12 de Octubre, Av. de Córdoba, s/n, Madrid 28041, Spain
| | - Joaquín Martínez-López
- Department of Translational Hematology, Research Institute Hospital 12 de Octubre (imas12), Av. de Córdoba, s/n, Madrid 28041, Spain
- Hematological Malignancies Clinical Research Unit H120-CNIO, CIBERONC, C. de Melchor Fernández Almagro, 3, Madrid 28029, Spain
- Department of Hematology, Hospital Universitario 12 de Octubre, Av. de Córdoba, s/n, Madrid 28041, Spain
| | - María Linares
- Department of Translational Hematology, Research Institute Hospital 12 de Octubre (imas12), Av. de Córdoba, s/n, Madrid 28041, Spain
- Hematological Malignancies Clinical Research Unit H120-CNIO, CIBERONC, C. de Melchor Fernández Almagro, 3, Madrid 28029, Spain
- Department of Biochemistry and Molecular Biology, Pharmacy School, Universidad Complutense de Madrid, Pl. de Ramón y Cajal, s/n, Madrid 28040, Spain
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3
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Varo R, Postigo M, Bila R, Dacal E, Chiconela H, García-Villena J, Cuadrado D, Vladimirov A, Díez N, Vallés-López R, Sitoe A, Vitorino P, Mucasse C, Beltran-Agullo L, Pujol O, García V, Abdala M, Sallé L, Anton A, Santos A, Ledesma-Carbayo MJ, Luengo-Oroz M, Bassat Q. Evaluation of the Performance of a 3D-Printed Smartphone-Based Retinal Imaging Device as a Screening Tool for Retinal Pathology in Mozambique. Am J Trop Med Hyg 2023; 109:1192-1198. [PMID: 37918001 PMCID: PMC10622463 DOI: 10.4269/ajtmh.23-0378] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2023] [Accepted: 08/07/2023] [Indexed: 11/04/2023] Open
Abstract
Low-income countries carry approximately 90% of the global burden of visual impairment, and up to 80% of this could be prevented or cured. However, there are only a few studies on the prevalence of retinal disease in these countries. Easier access to retinal information would allow differential diagnosis and promote strategies to improve eye health, which are currently scarce. This pilot study aims to evaluate the functionality and usability of a tele-retinography system for the detection of retinal pathology, based on a low-cost portable retinal scanner, manufactured with 3D printing and controlled by a mobile phone with an application designed ad hoc. The study was conducted at the Manhiça Rural Hospital in Mozambique. General practitioners, with no specific knowledge of ophthalmology or previous use of retinography, performed digital retinographies on 104 hospitalized patients. The retinographies were acquired in video format, uploaded to a web platform, and reviewed centrally by two ophthalmologists, analyzing the image quality and the presence of retinal lesions. In our sample there was a high proportion of exudates and hemorrhages-8% and 4%, respectively. In addition, the presence of lesions was studied in patients with known underlying risk factors for retinal disease, such as HIV, diabetes, and/or hypertension. Our tele-retinography system based on a smartphone coupled with a simple and low-cost 3D printed device is easy to use by healthcare personnel without specialized ophthalmological knowledge and could be applied for the screening and initial diagnosis of retinal pathology.
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Affiliation(s)
- Rosauro Varo
- ISGlobal, Hospital Clínic—Universitat de Barcelona, Barcelona, Spain
- Centro de Investigação em Saúde de Manhiça, Maputo, Mozambique
| | | | - Rubao Bila
- Centro de Investigação em Saúde de Manhiça, Maputo, Mozambique
| | | | - Hélio Chiconela
- Centro de Investigação em Saúde de Manhiça, Maputo, Mozambique
| | | | | | | | | | | | - Antonio Sitoe
- Centro de Investigação em Saúde de Manhiça, Maputo, Mozambique
| | - Pio Vitorino
- Centro de Investigação em Saúde de Manhiça, Maputo, Mozambique
| | - Campos Mucasse
- Centro de Investigação em Saúde de Manhiça, Maputo, Mozambique
| | | | - Olivia Pujol
- Institut Català de Retina, Barcelona, Spain
- Hospital Vall d´Hebron, Barcelona, Spain
| | | | - Mariamo Abdala
- Departamento de Oftalmologia, Hospital Central de Maputo, Maputo, Mozambique
- Faculdade de Medicina, Universidade Eduardo Mondlane, Maputo, Mozambique
| | - Lucía Sallé
- Biomedical Image Technologies Group, Departamento de Ingeniería Electrónica, Escuela Técnica Superior de Ingenieros Telecomunicación, Universidad Politécnica de Madrid, Madrid, Spain
| | - Alfonso Anton
- Institut Català de Retina, Barcelona, Spain
- CIBER de Bioingeniería, Biomateriales y Nanomedicina, Instituto de Salud Carlos III, Madrid, Spain
| | - Andrés Santos
- Biomedical Image Technologies Group, Departamento de Ingeniería Electrónica, Escuela Técnica Superior de Ingenieros Telecomunicación, Universidad Politécnica de Madrid, Madrid, Spain
- CIBER de Bioingeniería, Biomateriales y Nanomedicina, Instituto de Salud Carlos III, Madrid, Spain
| | - María J. Ledesma-Carbayo
- Biomedical Image Technologies Group, Departamento de Ingeniería Electrónica, Escuela Técnica Superior de Ingenieros Telecomunicación, Universidad Politécnica de Madrid, Madrid, Spain
- CIBER de Bioingeniería, Biomateriales y Nanomedicina, Instituto de Salud Carlos III, Madrid, Spain
| | | | - Quique Bassat
- ISGlobal, Hospital Clínic—Universitat de Barcelona, Barcelona, Spain
- Centro de Investigação em Saúde de Manhiça, Maputo, Mozambique
- Institut Català de Recerca i Estudis Avançats, Barcelona, Spain
- Pediatrics Department, Hospital Sant Joan de Déu, Universitat de Barcelona, Esplugues, Barcelona, Spain
- CIBER de Epidemiología y Salud Pública, Instituto de Salud Carlos III, Madrid, Spain
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4
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Ng YMM, Hoffmann Pham K, Luengo-Oroz M. Exploring YouTube's Recommendation System in the Context of COVID-19 Vaccines: Computational and Comparative Analysis of Video Trajectories. J Med Internet Res 2023; 25:e49061. [PMID: 37713243 PMCID: PMC10506664 DOI: 10.2196/49061] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2023] [Revised: 07/07/2023] [Accepted: 07/20/2023] [Indexed: 09/16/2023] Open
Abstract
BACKGROUND Throughout the COVID-19 pandemic, there has been a concern that social media may contribute to vaccine hesitancy due to the wide availability of antivaccine content on social media platforms. YouTube has stated its commitment to removing content that contains misinformation on vaccination. Nevertheless, such claims are difficult to audit. There is a need for more empirical research to evaluate the actual prevalence of antivaccine sentiment on the internet. OBJECTIVE This study examines recommendations made by YouTube's algorithms in order to investigate whether the platform may facilitate the spread of antivaccine sentiment on the internet. We assess the prevalence of antivaccine sentiment in recommended videos and evaluate how real-world users' experiences are different from the personalized recommendations obtained by using synthetic data collection methods, which are often used to study YouTube's recommendation systems. METHODS We trace trajectories from a credible seed video posted by the World Health Organization to antivaccine videos, following only video links suggested by YouTube's recommendation system. First, we gamify the process by asking real-world participants to intentionally find an antivaccine video with as few clicks as possible. Having collected crowdsourced trajectory data from respondents from (1) the World Health Organization and United Nations system (nWHO/UN=33) and (2) Amazon Mechanical Turk (nAMT=80), we next compare the recommendations seen by these users to recommended videos that are obtained from (3) the YouTube application programming interface's RelatedToVideoID parameter (nRTV=40) and (4) from clean browsers without any identifying cookies (nCB=40), which serve as reference points. We develop machine learning methods to classify antivaccine content at scale, enabling us to automatically evaluate 27,074 video recommendations made by YouTube. RESULTS We found no evidence that YouTube promotes antivaccine content; the average share of antivaccine videos remained well below 6% at all steps in users' recommendation trajectories. However, the watch histories of users significantly affect video recommendations, suggesting that data from the application programming interface or from a clean browser do not offer an accurate picture of the recommendations that real users are seeing. Real users saw slightly more provaccine content as they advanced through their recommendation trajectories, whereas synthetic users were drawn toward irrelevant recommendations as they advanced. Rather than antivaccine content, videos recommended by YouTube are likely to contain health-related content that is not specifically related to vaccination. These videos are usually longer and contain more popular content. CONCLUSIONS Our findings suggest that the common perception that YouTube's recommendation system acts as a "rabbit hole" may be inaccurate and that YouTube may instead be following a "blockbuster" strategy that attempts to engage users by promoting other content that has been reliably successful across the platform.
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Affiliation(s)
- Yee Man Margaret Ng
- UN Global Pulse, New York, NY, United States
- Department of Journalism & Institute of Communications Research, University of Illinois at Urbana-Champaign, Champaign, IL, United States
| | | | - Miguel Luengo-Oroz
- UN Global Pulse, New York, NY, United States
- Biomedical Image Technologies, ETSI Telecomunicación, Universidad Politécnica de Madrid & CIBER-BBN, ISCIII, Madrid, Spain
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5
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Sanchez T, Mavragani A, Álamo E, Pérez-Panizo N, Mousa A, Dacal E, Lin L, Vladimirov A, Cuadrado D, Mateos-Nozal J, Galán JC, Romero-Hernandez B, Cantón R, Luengo-Oroz M, Rodriguez-Dominguez M. A Smartphone-Based Platform Assisted by Artificial Intelligence for Reading and Reporting Rapid Diagnostic Tests: Evaluation Study in SARS-CoV-2 Lateral Flow Immunoassays. JMIR Public Health Surveill 2022; 8:e38533. [PMID: 36265136 PMCID: PMC9840096 DOI: 10.2196/38533] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2022] [Revised: 09/16/2022] [Accepted: 10/13/2022] [Indexed: 12/31/2022] Open
Abstract
BACKGROUND Rapid diagnostic tests (RDTs) are being widely used to manage COVID-19 pandemic. However, many results remain unreported or unconfirmed, altering a correct epidemiological surveillance. OBJECTIVE Our aim was to evaluate an artificial intelligence-based smartphone app, connected to a cloud web platform, to automatically and objectively read RDT results and assess its impact on COVID-19 pandemic management. METHODS Overall, 252 human sera were used to inoculate a total of 1165 RDTs for training and validation purposes. We then conducted two field studies to assess the performance on real-world scenarios by testing 172 antibody RDTs at two nursing homes and 96 antigen RDTs at one hospital emergency department. RESULTS Field studies demonstrated high levels of sensitivity (100%) and specificity (94.4%, CI 92.8%-96.1%) for reading IgG band of COVID-19 antibody RDTs compared to visual readings from health workers. Sensitivity of detecting IgM test bands was 100%, and specificity was 95.8% (CI 94.3%-97.3%). All COVID-19 antigen RDTs were correctly read by the app. CONCLUSIONS The proposed reading system is automatic, reducing variability and uncertainty associated with RDTs interpretation and can be used to read different RDT brands. The web platform serves as a real-time epidemiological tracking tool and facilitates reporting of positive RDTs to relevant health authorities.
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Affiliation(s)
| | | | | | - Nuria Pérez-Panizo
- Servicio de Geriatría, Hospital Universitario Ramon y Cajal, Madrid, Spain.,Instituto Ramón y Cajal de Investigación Sanitaria (IRYCIS), Madrid, Spain
| | | | | | - Lin Lin
- Spotlab, Madrid, Spain.,Biomedical Image Technologies, ETSI Telecomunicación, Universidad Politécnica de Madrid, Madrid, Spain
| | | | | | - Jesús Mateos-Nozal
- Servicio de Geriatría, Hospital Universitario Ramon y Cajal, Madrid, Spain.,Instituto Ramón y Cajal de Investigación Sanitaria (IRYCIS), Madrid, Spain
| | - Juan Carlos Galán
- Servicio de Microbiología, Hospital Universitario Ramon y Cajal, Madrid, Spain.,Instituto Ramón y Cajal de Investigación Sanitaria (IRYCIS), Madrid, Spain.,CIBER en Epidemiología y Salud Pública (CIBERESP), Instituto de Salud Carlos III, Madrid, Spain
| | - Beatriz Romero-Hernandez
- Servicio de Microbiología, Hospital Universitario Ramon y Cajal, Madrid, Spain.,Instituto Ramón y Cajal de Investigación Sanitaria (IRYCIS), Madrid, Spain.,CIBER en Epidemiología y Salud Pública (CIBERESP), Instituto de Salud Carlos III, Madrid, Spain
| | - Rafael Cantón
- Servicio de Microbiología, Hospital Universitario Ramon y Cajal, Madrid, Spain.,Instituto Ramón y Cajal de Investigación Sanitaria (IRYCIS), Madrid, Spain.,CIBER en Enfermedades Infecciosas (CIBERINFEC), Instituto de Salud Carlos III, Madrid, Spain
| | | | - Mario Rodriguez-Dominguez
- Servicio de Microbiología, Hospital Universitario Ramon y Cajal, Madrid, Spain.,Instituto Ramón y Cajal de Investigación Sanitaria (IRYCIS), Madrid, Spain.,CIBER en Epidemiología y Salud Pública (CIBERESP), Instituto de Salud Carlos III, Madrid, Spain
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6
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Perrotta D, Frias-Martinez E, Pastore y Piontti A, Zhang Q, Luengo-Oroz M, Paolotti D, Tizzoni M, Vespignani A. Comparing sources of mobility for modelling the epidemic spread of Zika virus in Colombia. PLoS Negl Trop Dis 2022; 16:e0010565. [PMID: 35857744 PMCID: PMC9299334 DOI: 10.1371/journal.pntd.0010565] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2021] [Accepted: 06/06/2022] [Indexed: 11/19/2022] Open
Abstract
Timely, accurate, and comparative data on human mobility is of paramount importance for epidemic preparedness and response, but generally not available or easily accessible. Mobile phone metadata, typically in the form of Call Detail Records (CDRs), represents a powerful source of information on human movements at an unprecedented scale. In this work, we investigate the potential benefits of harnessing aggregated CDR-derived mobility to predict the 2015-2016 Zika virus (ZIKV) outbreak in Colombia, when compared to other traditional data sources. To simulate the spread of ZIKV at sub-national level in Colombia, we employ a stochastic metapopulation epidemic model for vector-borne diseases. Our model integrates detailed data on the key drivers of ZIKV spread, including the spatial heterogeneity of the mosquito abundance, and the exposure of the population to the virus due to environmental and socio-economic factors. Given the same modelling settings (i.e. initial conditions and epidemiological parameters), we perform in-silico simulations for each mobility network and assess their ability in reproducing the local outbreak as reported by the official surveillance data. We assess the performance of our epidemic modelling approach in capturing the ZIKV outbreak both nationally and sub-nationally. Our model estimates are strongly correlated with the surveillance data at the country level (Pearson’s r = 0.92 for the CDR-informed network). Moreover, we found strong performance of the model estimates generated by the CDR-informed mobility networks in reproducing the local outbreak observed at the sub-national level. Compared to the CDR-informed networks, the performance of the other mobility networks is either comparatively similar or substantially lower, with no added value in predicting the local epidemic. This suggests that mobile phone data captures a better picture of human mobility patterns. This work contributes to the ongoing discussion on the value of aggregated mobility estimates from CDRs data that, with appropriate data protection and privacy safeguards, can be used for social impact applications and humanitarian action.
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Affiliation(s)
- Daniela Perrotta
- Laboratory of Digital and Computational Demography, Max Planck Institute for Demographic Research, Rostock, Germany
- * E-mail:
| | | | - Ana Pastore y Piontti
- Laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Boston, Massachusetts, United States of America
| | - Qian Zhang
- Laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Boston, Massachusetts, United States of America
| | - Miguel Luengo-Oroz
- United Nations Global Pulse, New York, State of New York, United States of America
| | | | | | - Alessandro Vespignani
- Laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Boston, Massachusetts, United States of America
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7
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Bermejo-Peláez D, San José Estépar R, Fernández-Velilla M, Palacios Miras C, Gallardo Madueño G, Benegas M, Gotera Rivera C, Cuerpo S, Luengo-Oroz M, Sellarés J, Sánchez M, Bastarrika G, Peces Barba G, Seijo LM, Ledesma-Carbayo MJ. Deep learning-based lesion subtyping and prediction of clinical outcomes in COVID-19 pneumonia using chest CT. Sci Rep 2022; 12:9387. [PMID: 35672437 PMCID: PMC9172615 DOI: 10.1038/s41598-022-13298-8] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2021] [Accepted: 05/12/2022] [Indexed: 12/15/2022] Open
Abstract
The main objective of this work is to develop and evaluate an artificial intelligence system based on deep learning capable of automatically identifying, quantifying, and characterizing COVID-19 pneumonia patterns in order to assess disease severity and predict clinical outcomes, and to compare the prediction performance with respect to human reader severity assessment and whole lung radiomics. We propose a deep learning based scheme to automatically segment the different lesion subtypes in nonenhanced CT scans. The automatic lesion quantification was used to predict clinical outcomes. The proposed technique has been independently tested in a multicentric cohort of 103 patients, retrospectively collected between March and July of 2020. Segmentation of lesion subtypes was evaluated using both overlapping (Dice) and distance-based (Hausdorff and average surface) metrics, while the proposed system to predict clinically relevant outcomes was assessed using the area under the curve (AUC). Additionally, other metrics including sensitivity, specificity, positive predictive value and negative predictive value were estimated. 95% confidence intervals were properly calculated. The agreement between the automatic estimate of parenchymal damage (%) and the radiologists' severity scoring was strong, with a Spearman correlation coefficient (R) of 0.83. The automatic quantification of lesion subtypes was able to predict patient mortality, admission to the Intensive Care Units (ICU) and need for mechanical ventilation with an AUC of 0.87, 0.73 and 0.68 respectively. The proposed artificial intelligence system enabled a better prediction of those clinically relevant outcomes when compared to the radiologists' interpretation and to whole lung radiomics. In conclusion, deep learning lesion subtyping in COVID-19 pneumonia from noncontrast chest CT enables quantitative assessment of disease severity and better prediction of clinical outcomes with respect to whole lung radiomics or radiologists' severity score.
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Affiliation(s)
- David Bermejo-Peláez
- Biomedical Image Technologies, ETSI Telecomunicación, Universidad Politécnica de Madrid, Av Complutense 30, 28040, Madrid, Spain
- CIBER-BBN, Madrid, Spain
- , Spotlab, Madrid, Spain
| | | | | | | | | | | | | | - Sandra Cuerpo
- Hospital Clinic de Barcelona-IDIBPAS, Barcelona, Spain
- CIBER-ES, Madrid, Spain
| | | | - Jacobo Sellarés
- Hospital Clinic de Barcelona-IDIBPAS, Barcelona, Spain
- CIBER-ES, Madrid, Spain
- Universidad de Vic (UVIC), Vic, Spain
| | | | | | - German Peces Barba
- Hospital Universitario Fundación Jiménez Díaz, Madrid, Spain
- CIBER-ES, Madrid, Spain
| | - Luis M Seijo
- Clínica Universidad de Navarra, Pamplona, Spain
- CIBER-ES, Madrid, Spain
| | - María J Ledesma-Carbayo
- Biomedical Image Technologies, ETSI Telecomunicación, Universidad Politécnica de Madrid, Av Complutense 30, 28040, Madrid, Spain.
- CIBER-BBN, Madrid, Spain.
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8
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Aylett-Bullock J, Gilman RT, Hall I, Kennedy D, Evers ES, Katta A, Ahmed H, Fong K, Adib K, Al Ariqi L, Ardalan A, Nabeth P, von Harbou K, Hoffmann Pham K, Cuesta-Lazaro C, Quera-Bofarull A, Gidraf Kahindo Maina A, Valentijn T, Harlass S, Krauss F, Huang C, Moreno Jimenez R, Comes T, Gaanderse M, Milano L, Luengo-Oroz M. Epidemiological modelling in refugee and internally displaced people settlements: challenges and ways forward. BMJ Glob Health 2022; 7:bmjgh-2021-007822. [PMID: 35264317 PMCID: PMC8915287 DOI: 10.1136/bmjgh-2021-007822] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2021] [Accepted: 01/23/2022] [Indexed: 11/06/2022] Open
Abstract
The spread of infectious diseases such as COVID-19 presents many challenges to healthcare systems and infrastructures across the world, exacerbating inequalities and leaving the world’s most vulnerable populations at risk. Epidemiological modelling is vital to guiding evidence-informed or data-driven decision making. In forced displacement contexts, and in particular refugee and internally displaced people (IDP) settlements, it meets several challenges including data availability and quality, the applicability of existing models to those contexts, the accurate modelling of cultural differences or specificities of those operational settings, the communication of results and uncertainties, as well as the alignment of strategic goals between diverse partners in complex situations. In this paper, we systematically review the limited epidemiological modelling work applied to refugee and IDP settlements so far, and discuss challenges and identify lessons learnt from the process. With the likelihood of disease outbreaks expected to increase in the future as more people are displaced due to conflict and climate change, we call for the development of more approaches and models specifically designed to include the unique features and populations of refugee and IDP settlements. To strengthen collaboration between the modelling and the humanitarian public health communities, we propose a roadmap to encourage the development of systems and frameworks to share needs, build tools and coordinate responses in an efficient and scalable manner, both for this pandemic and for future outbreaks.
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Affiliation(s)
- Joseph Aylett-Bullock
- UN Global Pulse, United Nations, New York, New York, USA .,Institute for Data Science, Durham University, Durham, UK
| | - Robert Tucker Gilman
- Centre for Crisis Studies and Mitigation, The University of Manchester, Manchester, UK.,Department of Earth and Environmental Sciences, The University of Manchester, Manchester, UK
| | - Ian Hall
- Centre for Crisis Studies and Mitigation, The University of Manchester, Manchester, UK.,Division of Informatics, Imaging and Data Sciences, The University of Manchester, Manchester, UK.,Department of Mathematics, The University of Manchester, Manchester, UK
| | - David Kennedy
- UK Public Health Rapid Support Team, London School of Hygiene & Tropical Medicine/Public Health England, London, UK
| | - Egmond Samir Evers
- WHO Cox's Bazar Emergency Sub-Office, United Nations, Cox's Bazar, Bangladesh
| | - Anjali Katta
- UN Global Pulse, United Nations, New York, New York, USA
| | - Hussien Ahmed
- UNHCR Cox's Bazar Sub-Office, United Nations, Cox's Bazar, Bangladesh
| | - Kevin Fong
- Department of Science, Technology, Engineering and Public Policy, University College London, London, UK
| | - Keyrellous Adib
- WHO Eastern Mediterranean Regional Office, United Nations, Cairo, Egypt
| | - Lubna Al Ariqi
- WHO Eastern Mediterranean Regional Office, United Nations, Cairo, Egypt
| | - Ali Ardalan
- WHO Eastern Mediterranean Regional Office, United Nations, Cairo, Egypt
| | - Pierre Nabeth
- WHO Eastern Mediterranean Regional Office, United Nations, Cairo, Egypt
| | - Kai von Harbou
- WHO Cox's Bazar Emergency Sub-Office, United Nations, Cox's Bazar, Bangladesh
| | - Katherine Hoffmann Pham
- UN Global Pulse, United Nations, New York, New York, USA.,Stern School of Business, New York University, New York City, New York, USA
| | | | | | | | - Tinka Valentijn
- OCHA Centre for Humanitarian Data, United Nations, The Hague, The Netherlands
| | - Sandra Harlass
- UNHCR Public Health Unit, United Nations, Geneva, Switzerland
| | - Frank Krauss
- Institute for Data Science, Durham University, Durham, UK
| | - Chao Huang
- UNHCR Global Data Service, United Nations, Copenhagen, New York, USA
| | | | - Tina Comes
- Faculty of Technology, Policy, and Management, Department of Engineering Systems and Services, Delft University of Technology, Delft, The Netherlands
| | - Mariken Gaanderse
- Faculty of Technology, Policy, and Management, Department of Engineering Systems and Services, Delft University of Technology, Delft, The Netherlands
| | - Leonardo Milano
- OCHA Centre for Humanitarian Data, United Nations, The Hague, The Netherlands
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9
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Lin L, Bermejo-Pelaez D, Capellan-Martin D, Cuadrado D, Rodriguez C, Garcia L, Diez N, Tome R, Postigo M, Ledesma-Carbayo MJ, Luengo-Oroz M. Combining collective and artificial intelligence for global health diseases diagnosis using crowdsourced annotated medical images. Annu Int Conf IEEE Eng Med Biol Soc 2021; 2021:3344-3348. [PMID: 34891956 DOI: 10.1109/embc46164.2021.9630868] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
Visual inspection of microscopic samples is still the gold standard diagnostic methodology for many global health diseases. Soil-transmitted helminth infection affects 1.5 billion people worldwide, and is the most prevalent disease among the Neglected Tropical Diseases. It is diagnosed by manual examination of stool samples by microscopy, which is a time-consuming task and requires trained personnel and high specialization. Artificial intelligence could automate this task making the diagnosis more accessible. Still, it needs a large amount of annotated training data coming from experts.In this work, we proposed the use of crowdsourced annotated medical images to train AI models (neural networks) for the detection of soil-transmitted helminthiasis in microscopy images from stool samples leveraging non-expert knowledge collected through playing a video game. We collected annotations made by both school-age children and adults, and we showed that, although the quality of crowdsourced annotations made by school-age children are sightly inferior than the ones made by adults, AI models trained on these crowdsourced annotations perform similarly (AUC of 0.928 and 0.939 respectively), and reach similar performance to the AI model trained on expert annotations (AUC of 0.932). We also showed the impact of the training sample size and continuous training on the performance of the AI models.In conclusion, the workflow proposed in this work combined collective and artificial intelligence for detecting soil-transmitted helminthiasis. Embedded within a digital health platform can be applied to any other medical image analysis task and contribute to reduce the burden of disease.
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10
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Aylett-Bullock J, Cuesta-Lazaro C, Quera-Bofarull A, Katta A, Hoffmann Pham K, Hoover B, Strobelt H, Moreno Jimenez R, Sedgewick A, Samir Evers E, Kennedy D, Harlass S, Gidraf Kahindo Maina A, Hussien A, Luengo-Oroz M. Operational response simulation tool for epidemics within refugee and IDP settlements: A scenario-based case study of the Cox's Bazar settlement. PLoS Comput Biol 2021; 17:e1009360. [PMID: 34710090 PMCID: PMC8553081 DOI: 10.1371/journal.pcbi.1009360] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2021] [Accepted: 08/18/2021] [Indexed: 12/21/2022] Open
Abstract
The spread of infectious diseases such as COVID-19 presents many challenges to healthcare systems and infrastructures across the world, exacerbating inequalities and leaving the world's most vulnerable populations most affected. Given their density and available infrastructure, refugee and internally displaced person (IDP) settlements can be particularly susceptible to disease spread. In this paper we present an agent-based modeling approach to simulating the spread of disease in refugee and IDP settlements under various non-pharmaceutical intervention strategies. The model, based on the June open-source framework, is informed by data on geography, demographics, comorbidities, physical infrastructure and other parameters obtained from real-world observations and previous literature. The development and testing of this approach focuses on the Cox's Bazar refugee settlement in Bangladesh, although our model is designed to be generalizable to other informal settings. Our findings suggest the encouraging self-isolation at home of mild to severe symptomatic patients, as opposed to the isolation of all positive cases in purpose-built isolation and treatment centers, does not increase the risk of secondary infection meaning the centers can be used to provide hospital support to the most intense cases of COVID-19. Secondly we find that mask wearing in all indoor communal areas can be effective at dampening viral spread, even with low mask efficacy and compliance rates. Finally, we model the effects of reopening learning centers in the settlement under various mitigation strategies. For example, a combination of mask wearing in the classroom, halving attendance regularity to enable physical distancing, and better ventilation can almost completely mitigate the increased risk of infection which keeping the learning centers open may cause. These modeling efforts are being incorporated into decision making processes to inform future planning, and further exercises should be carried out in similar geographies to help protect those most vulnerable.
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Affiliation(s)
- Joseph Aylett-Bullock
- United Nations Global Pulse, New York, New York, United States of America
- Institute for Data Science, Durham University, Durham, United Kingdom
| | | | | | - Anjali Katta
- United Nations Global Pulse, New York, New York, United States of America
| | - Katherine Hoffmann Pham
- United Nations Global Pulse, New York, New York, United States of America
- New York University Stern School of Business, New York, New York, United States of America
| | - Benjamin Hoover
- MIT-IBM Watson AI Lab, Cambridge, Massachusetts, United States of America
| | - Hendrik Strobelt
- MIT-IBM Watson AI Lab, Cambridge, Massachusetts, United States of America
| | | | - Aidan Sedgewick
- Institute for Data Science, Durham University, Durham, United Kingdom
| | | | - David Kennedy
- UK Public Health Rapid Support Team, Public Health England/London School of Hygiene and Tropical Medicine, London, United Kingdom
| | | | | | - Ahmad Hussien
- UNHCR Information Management Unit, Cox’s Bazar, Bangladesh
| | - Miguel Luengo-Oroz
- United Nations Global Pulse, New York, New York, United States of America
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11
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Dacal E, Bermejo-Peláez D, Lin L, Álamo E, Cuadrado D, Martínez Á, Mousa A, Postigo M, Soto A, Sukosd E, Vladimirov A, Mwandawiro C, Gichuki P, Williams NA, Muñoz J, Kepha S, Luengo-Oroz M. Mobile microscopy and telemedicine platform assisted by deep learning for the quantification of Trichuris trichiura infection. PLoS Negl Trop Dis 2021; 15:e0009677. [PMID: 34492039 PMCID: PMC8448303 DOI: 10.1371/journal.pntd.0009677] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2021] [Revised: 09/17/2021] [Accepted: 07/21/2021] [Indexed: 11/18/2022] Open
Abstract
Soil-transmitted helminths (STH) are the most prevalent pathogens among the group of neglected tropical diseases (NTDs). The Kato-Katz technique is the diagnosis method recommended by the World Health Organization (WHO) although it often presents a decreased sensitivity in low transmission settings and it is labour intensive. Visual reading of Kato-Katz preparations requires the samples to be analyzed in a short period of time since its preparation. Digitizing the samples could provide a solution which allows to store the samples in a digital database and perform remote analysis. Artificial intelligence (AI) methods based on digitized samples can support diagnosis by performing an objective and automatic quantification of disease infection. In this work, we propose an end-to-end pipeline for microscopy image digitization and automatic analysis of digitized images of STH. Our solution includes (a) a digitization system based on a mobile app that digitizes microscope samples using a 3D printed microscope adapter, (b) a telemedicine platform for remote analysis and labelling, and (c) novel deep learning algorithms for automatic assessment and quantification of parasitological infections by STH. The deep learning algorithm has been trained and tested on 51 slides of stool samples containing 949 Trichuris spp. eggs from 6 different subjects. The algorithm evaluation was performed using a cross-validation strategy, obtaining a mean precision of 98.44% and a mean recall of 80.94%. The results also proved the potential of generalization capability of the method at identifying different types of helminth eggs. Additionally, the AI-assisted quantification of STH based on digitized samples has been compared to the one performed using conventional microscopy, showing a good agreement between measurements. In conclusion, this work has presented a comprehensive pipeline using smartphone-assisted microscopy. It is integrated with a telemedicine platform for automatic image analysis and quantification of STH infection using AI models.
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Affiliation(s)
| | | | - Lin Lin
- Spotlab, Madrid, Spain
- Biomedical Image Technologies, ETSI Telecomunicación, Universidad Politécnica de Madrid, Madrid, Spain
| | | | | | | | | | | | | | | | | | - Charles Mwandawiro
- Eastern and Southern Africa Center for International Parasite Control (ESACIPAC), Kenya Medical Research Institute (KEMRI), Nairobi, Kenya
| | - Paul Gichuki
- Eastern and Southern Africa Center for International Parasite Control (ESACIPAC), Kenya Medical Research Institute (KEMRI), Nairobi, Kenya
| | - Nana Aba Williams
- Barcelona Institute for Global Health (ISGlobal), Hospital Clínic-Universitat de Barcelona, Barcelona, Spain
| | - José Muñoz
- Barcelona Institute for Global Health (ISGlobal), Hospital Clínic-Universitat de Barcelona, Barcelona, Spain
| | - Stella Kepha
- Eastern and Southern Africa Center for International Parasite Control (ESACIPAC), Kenya Medical Research Institute (KEMRI), Nairobi, Kenya
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12
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Bullock J, Luccioni A, Hoffman Pham K, Sin Nga Lam C, Luengo-Oroz M. Mapping the landscape of Artificial Intelligence applications against COVID-19. J ARTIF INTELL RES 2020. [DOI: 10.1613/jair.1.12162] [Citation(s) in RCA: 181] [Impact Index Per Article: 45.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022] Open
Abstract
COVID-19, the disease caused by the SARS-CoV-2 virus, has been declared a pandemic by the World Health Organization, which has reported over 18 million confirmed cases as of August 5, 2020. In this review, we present an overview of recent studies using Machine Learning and, more broadly, Artificial Intelligence, to tackle many aspects of the COVID19 crisis. We have identified applications that address challenges posed by COVID-19 at different scales, including: molecular, by identifying new or existing drugs for treatment; clinical, by supporting diagnosis and evaluating prognosis based on medical imaging and non-invasive measures; and societal, by tracking both the epidemic and the accompanying infodemic using multiple data sources. We also review datasets, tools, and resources needed to facilitate Artificial Intelligence research, and discuss strategic considerations related to the operational implementation of multidisciplinary partnerships and open science. We highlight the need for international cooperation to maximize the potential of AI in this and future pandemics.
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13
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Hoffmann Pham KE, Luengo-Oroz M. From plague to coronavirus: vessel trajectory data from ship automatic identification systems for epidemic modeling. J Travel Med 2020; 27:5835704. [PMID: 32391908 PMCID: PMC7239158 DOI: 10.1093/jtm/taaa072] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/27/2020] [Revised: 03/04/2020] [Accepted: 05/05/2020] [Indexed: 11/18/2022]
Abstract
In addition to moving people and goods, ships can spread disease. Vessel trajectory data from ship Automatic Identification Systems (AIS) is available online and can be extracted and analyzed, as we illustrate in the case of the current coronavirus epidemic. This data should be included in epidemiological models of disease transmission to complement air traffic data and inform operational responses.
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Affiliation(s)
- Katherine E Hoffmann Pham
- Department of Technology, Operations, and Statistics, NYU Stern School of Business, 44 West 4th Street, New York, NY 10012, USA
| | - Miguel Luengo-Oroz
- United Nations Global Pulse, 370 Lexington Avenue, Suite 1707 New York, NY 10017 USA
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14
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Luengo-Oroz M, Hoffmann Pham K, Bullock J, Kirkpatrick R, Luccioni A, Rubel S, Wachholz C, Chakchouk M, Biggs P, Nguyen T, Purnat T, Mariano B. Artificial intelligence cooperation to support the global response to COVID-19. NAT MACH INTELL 2020. [DOI: 10.1038/s42256-020-0184-3] [Citation(s) in RCA: 52] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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15
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16
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García-Delgado L, Luengo-Oroz M, Cuadrado D, Postigo M. Crowdsourcing Visual Search in the real world: Applications to Collaborative Medical Image Diagnosis. J Vis 2019. [DOI: 10.1167/19.10.8c] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022] Open
Affiliation(s)
- Lara García-Delgado
- Biomedical Image Technologies, Department of Electronic Engineering at Universidad Politécnica de Madrid, and member of Spotlab, Spain
| | | | | | - María Postigo
- Universidad Politécnica de Madrid & founders of Spotlab
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17
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Linares M, Postigo M, Cuadrado D, Ortiz-Ruiz A, Gil-Casanova S, Vladimirov A, García-Villena J, Nuñez-Escobedo JM, Martínez-López J, Rubio JM, Ledesma-Carbayo MJ, Santos A, Bassat Q, Luengo-Oroz M. Collaborative intelligence and gamification for on-line malaria species differentiation. Malar J 2019; 18:21. [PMID: 30678733 PMCID: PMC6345056 DOI: 10.1186/s12936-019-2662-9] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2018] [Accepted: 01/19/2019] [Indexed: 11/28/2022] Open
Abstract
Background Current World Health Organization recommendations for the management of malaria include the need for a parasitological confirmation prior to triggering appropriate treatment. The use of rapid diagnostic tests (RDTs) for malaria has contributed to a better infection recognition and a more targeted treatment. Nevertheless, low-density infections and parasites that fail to produce HRP2 can cause false-negative RDT results. Microscopy has traditionally been the methodology most commonly used to quantify malaria and characterize the infecting species, but the wider use of this technique remains challenging, as it requires trained personnel and processing capacity. Objective In this study, the feasibility of an on-line system for remote malaria species identification and differentiation has been investigated by crowdsourcing the analysis of digitalized infected thin blood smears by non-expert observers using a mobile app. Methods An on-line videogame in which players learned how to differentiate the young trophozoite stage of the five Plasmodium species has been designed. Images were digitalized with a smartphone camera adapted to the ocular of a conventional light microscope. Images from infected red blood cells were cropped and puzzled into an on-line game. During the game, players had to decide the malaria species (Plasmodium falciparum, Plasmodium malariae, Plasmodium vivax, Plasmodium ovale, Plasmodium knowlesi) of the infected cells that were shown in the screen. After 2 months, each player’s decisions were analysed individually and collectively. Results On-line volunteers playing the game made more than 500,000 assessments for species differentiation. Statistically, when the choice of several players was combined (n > 25), they were able to significantly discriminate Plasmodium species, reaching a level of accuracy of 99% for all species combinations, except for P. knowlesi (80%). Non-expert decisions on which Plasmodium species was shown in the screen were made in less than 3 s. Conclusion These findings show that it is possible to train malaria-naïve non-experts to identify and differentiate malaria species in digitalized thin blood samples. Although the accuracy of a single player is not perfect, the combination of the responses of multiple casual gamers can achieve an accuracy that is within the range of the diagnostic accuracy made by a trained microscopist. Electronic supplementary material The online version of this article (10.1186/s12936-019-2662-9) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- María Linares
- Research Institute Hospital 12 de Octubre/CNIO, Universidad Complutense de Madrid, Ciudad Universitaria, 28040, Madrid, Spain.
| | - María Postigo
- Biomedical Image Technologies Group, DIE, ETSI Telecomunicación, Universidad Politécnica de Madrid, CEI Moncloa UPM-UCM, Madrid, Spain
| | - Daniel Cuadrado
- Biomedical Image Technologies Group, DIE, ETSI Telecomunicación, Universidad Politécnica de Madrid, CEI Moncloa UPM-UCM, Madrid, Spain
| | - Alejandra Ortiz-Ruiz
- Research Institute Hospital 12 de Octubre/CNIO, Universidad Complutense de Madrid, Ciudad Universitaria, 28040, Madrid, Spain
| | - Sara Gil-Casanova
- Biomedical Image Technologies Group, DIE, ETSI Telecomunicación, Universidad Politécnica de Madrid, CEI Moncloa UPM-UCM, Madrid, Spain
| | - Alexander Vladimirov
- Biomedical Image Technologies Group, DIE, ETSI Telecomunicación, Universidad Politécnica de Madrid, CEI Moncloa UPM-UCM, Madrid, Spain
| | - Jaime García-Villena
- Biomedical Image Technologies Group, DIE, ETSI Telecomunicación, Universidad Politécnica de Madrid, CEI Moncloa UPM-UCM, Madrid, Spain
| | - José María Nuñez-Escobedo
- Biomedical Image Technologies Group, DIE, ETSI Telecomunicación, Universidad Politécnica de Madrid, CEI Moncloa UPM-UCM, Madrid, Spain
| | - Joaquín Martínez-López
- Research Institute Hospital 12 de Octubre/CNIO, Universidad Complutense de Madrid, Ciudad Universitaria, 28040, Madrid, Spain
| | - José Miguel Rubio
- Malaria and Emerging Parasitic Diseases Laboratory, National Microbiology Centre, Instituto de Salud Carlos III, Madrid, Spain
| | - María Jesús Ledesma-Carbayo
- Biomedical Image Technologies Group, DIE, ETSI Telecomunicación, Universidad Politécnica de Madrid, CEI Moncloa UPM-UCM, Madrid, Spain.,Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Madrid, Spain
| | - Andrés Santos
- Biomedical Image Technologies Group, DIE, ETSI Telecomunicación, Universidad Politécnica de Madrid, CEI Moncloa UPM-UCM, Madrid, Spain.,Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Madrid, Spain
| | - Quique Bassat
- ISGlobal, Hospital Clínic, Universitat de Barcelona, Barcelona, Spain.,Centro de Investigação em Saúde de Manhiça (CISM), Maputo, Mozambique.,ICREA, Pg. Lluís Companys 23, 08010, Barcelona, Spain.,Pediatric Infectious Diseases Unit, Pediatrics Department, Hospital Sant Joan de Déu, University of Barcelona, Barcelona, Spain
| | - Miguel Luengo-Oroz
- Biomedical Image Technologies Group, DIE, ETSI Telecomunicación, Universidad Politécnica de Madrid, CEI Moncloa UPM-UCM, Madrid, Spain. .,Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Madrid, Spain.
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18
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Quinn JA, Nyhan MM, Navarro C, Coluccia D, Bromley L, Luengo-Oroz M. Humanitarian applications of machine learning with remote-sensing data: review and case study in refugee settlement mapping. Philos Trans A Math Phys Eng Sci 2018; 376:20170363. [PMID: 30082308 PMCID: PMC6107544 DOI: 10.1098/rsta.2017.0363] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 07/02/2018] [Indexed: 05/15/2023]
Abstract
The coordination of humanitarian relief, e.g. in a natural disaster or a conflict situation, is often complicated by a scarcity of data to inform planning. Remote sensing imagery, from satellites or drones, can give important insights into conditions on the ground, including in areas which are difficult to access. Applications include situation awareness after natural disasters, structural damage assessment in conflict, monitoring human rights violations or population estimation in settlements. We review machine learning approaches for automating these problems, and discuss their potential and limitations. We also provide a case study of experiments using deep learning methods to count the numbers of structures in multiple refugee settlements in Africa and the Middle East. We find that while high levels of accuracy are possible, there is considerable variation in the characteristics of imagery collected from different sensors and regions. In this, as in the other applications discussed in the paper, critical inferences must be made from a relatively small amount of pixel data. We, therefore, consider that using machine learning systems as an augmentation of human analysts is a reasonable strategy to transition from current fully manual operational pipelines to ones which are both more efficient and have the necessary levels of quality control.This article is part of a discussion meeting issue 'The growing ubiquity of algorithms in society: implications, impacts and innovations'.
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Affiliation(s)
| | | | - Celia Navarro
- United Nations Institute for Training and Research-Operational Satellite Applications Programme, Geneva, Switzerland
| | - Davide Coluccia
- United Nations Institute for Training and Research-Operational Satellite Applications Programme, Geneva, Switzerland
| | - Lars Bromley
- United Nations Institute for Training and Research-Operational Satellite Applications Programme, Geneva, Switzerland
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Di Clemente R, Luengo-Oroz M, Travizano M, Xu S, Vaitla B, González MC. Sequences of purchases in credit card data reveal lifestyles in urban populations. Nat Commun 2018; 9:3330. [PMID: 30127416 PMCID: PMC6102281 DOI: 10.1038/s41467-018-05690-8] [Citation(s) in RCA: 36] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2017] [Accepted: 07/06/2018] [Indexed: 11/09/2022] Open
Abstract
Zipf-like distributions characterize a wide set of phenomena in physics, biology, economics, and social sciences. In human activities, Zipf's law describes, for example, the frequency of appearance of words in a text or the purchase types in shopping patterns. In the latter, the uneven distribution of transaction types is bound with the temporal sequences of purchases of individual choices. In this work, we define a framework using a text compression technique on the sequences of credit card purchases to detect ubiquitous patterns of collective behavior. Clustering the consumers by their similarity in purchase sequences, we detect five consumer groups. Remarkably, post checking, individuals in each group are also similar in their age, total expenditure, gender, and the diversity of their social and mobility networks extracted from their mobile phone records. By properly deconstructing transaction data with Zipf-like distributions, this method uncovers sets of significant sequences that reveal insights on collective human behavior.
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Affiliation(s)
- Riccardo Di Clemente
- Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA.,The Bartlett Centre for Advanced Spatial Analysis, University College London, London, WC1E 6BT, UK
| | - Miguel Luengo-Oroz
- United Nations Global Pulse, 46th Street and 1st Avenue, New York, NY, 10017, USA
| | - Matias Travizano
- GranData, 550 15th Street Suite 36C, San Francisco, CA, 94103, USA
| | - Sharon Xu
- Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
| | - Bapu Vaitla
- Department of Environmental Health, Harvard University, 677 Huntington Avenue, Boston, MA, 02115, USA
| | - Marta C González
- Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA. .,Department of City and Regional Planning, Berkeley, CA, 94720-1820, USA. .,Lawrence Berkeley National Laboratory, 1 Cyclotron Road, Berkeley, CA, 94720-1820, USA.
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20
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Balicer RD, Luengo-Oroz M, Cohen-Stavi C, Loyola E, Mantingh F, Romanoff L, Galea G. Using big data for non-communicable disease surveillance. Lancet Diabetes Endocrinol 2018; 6:595-598. [PMID: 29146206 DOI: 10.1016/s2213-8587(17)30372-8] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/24/2017] [Revised: 10/01/2017] [Accepted: 10/01/2017] [Indexed: 10/18/2022]
Affiliation(s)
- Ran D Balicer
- Clalit Research Institute, WHO Collaborating Centre for NCD Research Prevention and Control, Chief Physician's Office, Clalit Health Services, Tel Aviv 62098, Israel; Epidemiology Department, Ben Gurion University of the Negev, Be'er Sheva, Israel.
| | | | - Chandra Cohen-Stavi
- Clalit Research Institute, WHO Collaborating Centre for NCD Research Prevention and Control, Chief Physician's Office, Clalit Health Services, Tel Aviv 62098, Israel
| | - Enrique Loyola
- WHO European Office for the Prevention and Control of Non-Communicable Diseases (NCD Office), Moscow, Russia
| | - Frederiek Mantingh
- Division of Non-Communicable Diseases and Promoting Health through the Life-Course, WHO Regional Office for Europe, Copenhagen, Denmark
| | | | - Gauden Galea
- Division of Non-Communicable Diseases and Promoting Health through the Life-Course, WHO Regional Office for Europe, Copenhagen, Denmark
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21
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Zufiria PJ, Pastor-Escuredo D, Úbeda-Medina L, Hernandez-Medina MA, Barriales-Valbuena I, Morales AJ, Jacques DC, Nkwambi W, Diop MB, Quinn J, Hidalgo-Sanchís P, Luengo-Oroz M. Identifying seasonal mobility profiles from anonymized and aggregated mobile phone data. Application in food security. PLoS One 2018; 13:e0195714. [PMID: 29698404 PMCID: PMC5919706 DOI: 10.1371/journal.pone.0195714] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2017] [Accepted: 03/28/2018] [Indexed: 11/28/2022] Open
Abstract
We propose a framework for the systematic analysis of mobile phone data to identify relevant mobility profiles in a population. The proposed framework allows finding distinct human mobility profiles based on the digital trace of mobile phone users characterized by a Matrix of Individual Trajectories (IT-Matrix). This matrix gathers a consistent and regularized description of individual trajectories that enables multi-scale representations along time and space, which can be used to extract aggregated indicators such as a dynamic multi-scale population count. Unsupervised clustering of individual trajectories generates mobility profiles (clusters of similar individual trajectories) which characterize relevant group behaviors preserving optimal aggregation levels for detailed and privacy-secured mobility characterization. The application of the proposed framework is illustrated by analyzing fully anonymized data on human mobility from mobile phones in Senegal at the arrondissement level over a calendar year. The analysis of monthly mobility patterns at the livelihood zone resolution resulted in the discovery and characterization of seasonal mobility profiles related with economic activities, agricultural calendars and rainfalls. The use of these mobility profiles could support the timely identification of mobility changes in vulnerable populations in response to external shocks (such as natural disasters, civil conflicts or sudden increases of food prices) to monitor food security.
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Affiliation(s)
| | | | | | | | | | | | | | | | | | - John Quinn
- Pulse Lab Kampala, United Nations Global Pulse, Kampala, Uganda
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22
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Ortiz-Ruiz A, Postigo M, Gil-Casanova S, Cuadrado D, Bautista JM, Rubio JM, Luengo-Oroz M, Linares M. Plasmodium species differentiation by non-expert on-line volunteers for remote malaria field diagnosis. Malar J 2018; 17:54. [PMID: 29378588 PMCID: PMC5789591 DOI: 10.1186/s12936-018-2194-8] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2017] [Accepted: 01/18/2018] [Indexed: 11/29/2022] Open
Abstract
Background Routine field diagnosis of malaria is a considerable challenge in rural and low resources endemic areas mainly due to lack of personnel, training and sample processing capacity. In addition, differential diagnosis of Plasmodium species has a high level of misdiagnosis. Real time remote microscopical diagnosis through on-line crowdsourcing platforms could be converted into an agile network to support diagnosis-based treatment and malaria control in low resources areas. This study explores whether accurate Plasmodium species identification—a critical step during the diagnosis protocol in order to choose the appropriate medication—is possible through the information provided by non-trained on-line volunteers. Methods 88 volunteers have performed a series of questionnaires over 110 images to differentiate species (Plasmodium falciparum, Plasmodium ovale, Plasmodium vivax, Plasmodium malariae, Plasmodium knowlesi) and parasite staging from thin blood smear images digitalized with a smartphone camera adapted to the ocular of a conventional light microscope. Visual cues evaluated in the surveys include texture and colour, parasite shape and red blood size. Results On-line volunteers are able to discriminate Plasmodium species (P. falciparum, P. malariae, P. vivax, P. ovale, P. knowlesi) and stages in thin-blood smears according to visual cues observed on digitalized images of parasitized red blood cells. Friendly textual descriptions of the visual cues and specialized malaria terminology is key for volunteers learning and efficiency. Conclusions On-line volunteers with short-training are able to differentiate malaria parasite species and parasite stages from digitalized thin smears based on simple visual cues (shape, size, texture and colour). While the accuracy of a single on-line expert is far from perfect, a single parasite classification obtained by combining the opinions of multiple on-line volunteers over the same smear, could improve accuracy and reliability of Plasmodium species identification in remote malaria diagnosis. Electronic supplementary material The online version of this article (10.1186/s12936-018-2194-8) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Alejandra Ortiz-Ruiz
- Research Institute Hospital 12 de Octubre, Universidad Complutense de Madrid, Ciudad Universitaria, 28040, Madrid, Spain.,SPOTLAB, S.L. C/Gran Vía 39, 2º, 28013, Madrid, Spain
| | - María Postigo
- Biomedical Image Technologies Group, DIE, ETSI Telecomunicación, Universidad Politécnica de Madrid, CEI Moncloa UPM-UCM, Madrid, Spain.,SPOTLAB, S.L. C/Gran Vía 39, 2º, 28013, Madrid, Spain
| | - Sara Gil-Casanova
- Biomedical Image Technologies Group, DIE, ETSI Telecomunicación, Universidad Politécnica de Madrid, CEI Moncloa UPM-UCM, Madrid, Spain
| | - Daniel Cuadrado
- Biomedical Image Technologies Group, DIE, ETSI Telecomunicación, Universidad Politécnica de Madrid, CEI Moncloa UPM-UCM, Madrid, Spain.,SPOTLAB, S.L. C/Gran Vía 39, 2º, 28013, Madrid, Spain
| | - José M Bautista
- Research Institute Hospital 12 de Octubre, Universidad Complutense de Madrid, Ciudad Universitaria, 28040, Madrid, Spain
| | - José Miguel Rubio
- Malaria and Emerging Parasitic Diseases Laboratory, National Microbiology Centre, Instituto de Salud Carlos III, Madrid, Spain
| | - Miguel Luengo-Oroz
- Biomedical Image Technologies Group, DIE, ETSI Telecomunicación, Universidad Politécnica de Madrid, CEI Moncloa UPM-UCM, Madrid, Spain.,Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Madrid, Spain.,SPOTLAB, S.L. C/Gran Vía 39, 2º, 28013, Madrid, Spain
| | - María Linares
- Research Institute Hospital 12 de Octubre, Universidad Complutense de Madrid, Ciudad Universitaria, 28040, Madrid, Spain. .,SPOTLAB, S.L. C/Gran Vía 39, 2º, 28013, Madrid, Spain.
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Hristova D, Rutherford A, Anson J, Luengo-Oroz M, Mascolo C. The International Postal Network and Other Global Flows as Proxies for National Wellbeing. PLoS One 2016; 11:e0155976. [PMID: 27248142 PMCID: PMC4889156 DOI: 10.1371/journal.pone.0155976] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2016] [Accepted: 05/06/2016] [Indexed: 11/18/2022] Open
Abstract
The digital exhaust left by flows of physical and digital commodities provides a rich measure of the nature, strength and significance of relationships between countries in the global network. With this work, we examine how these traces and the network structure can reveal the socioeconomic profile of different countries. We take into account multiple international networks of physical and digital flows, including the previously unexplored international postal network. By measuring the position of each country in the Trade, Postal, Migration, International Flights, IP and Digital Communications networks, we are able to build proxies for a number of crucial socioeconomic indicators such as GDP per capita and the Human Development Index ranking along with twelve other indicators used as benchmarks of national well-being by the United Nations and other international organisations. In this context, we have also proposed and evaluated a global connectivity degree measure applying multiplex theory across the six networks that accounts for the strength of relationships between countries. We conclude by showing how countries with shared community membership over multiple networks have similar socioeconomic profiles. Combining multiple flow data sources can help understand the forces which drive economic activity on a global level. Such an ability to infer proxy indicators in a context of incomplete information is extremely timely in light of recent discussions on measurement of indicators relevant to the Sustainable Development Goals.
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Affiliation(s)
| | - Alex Rutherford
- United Nations Global Pulse, New York, NY, United States of America
| | - Jose Anson
- Universal Postal Union, Bern, Switzerland
| | | | - Cecilia Mascolo
- Computer Laboratory, University of Cambridge, Cambridge, United Kingdom
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