1
|
Andre E, Isaacs C, Affolabi D, Alagna R, Brockmann D, de Jong BC, Cambau E, Churchyard G, Cohen T, Delmee M, Delvenne JC, Farhat M, Habib A, Holme P, Keshavjee S, Khan A, Lightfoot P, Moore D, Moreno Y, Mundade Y, Pai M, Patel S, Nyaruhirira AU, Rocha LEC, Takle J, Trébucq A, Creswell J, Boehme C. Connectivity of diagnostic technologies: improving surveillance and accelerating tuberculosis elimination. Int J Tuberc Lung Dis 2018; 20:999-1003. [PMID: 27393530 DOI: 10.5588/ijtld.16.0015] [Citation(s) in RCA: 25] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
In regard to tuberculosis (TB) and other major global epidemics, the use of new diagnostic tests is increasing dramatically, including in resource-limited countries. Although there has never been as much digital information generated, this data source has not been exploited to its full potential. In this opinion paper, we discuss lessons learned from the global scale-up of these laboratory devices and the pathway to tapping the potential of laboratory-generated information in the field of TB by using connectivity. Responding to the demand for connectivity, innovative third-party players have proposed solutions that have been widely adopted by field users of the Xpert(®) MTB/RIF assay. The experience associated with the utilisation of these systems, which facilitate the monitoring of wide laboratory networks, stressed the need for a more global and comprehensive approach to diagnostic connectivity. In addition to facilitating the reporting of test results, the mobility of digital information allows the sharing of information generated in programme settings. When they become easily accessible, these data can be used to improve patient care, disease surveillance and drug discovery. They should therefore be considered as a public health good. We list several examples of concrete initiatives that should allow data sources to be combined to improve the understanding of the epidemic, support the operational response and, finally, accelerate TB elimination. With the many opportunities that the pooling of data associated with the TB epidemic can provide, pooling of this information at an international level has become an absolute priority.
Collapse
Affiliation(s)
- E Andre
- Pôle de Microbiologie Médicale, Institut de Recherche Expérimentale et Clinique, Université Catholique de Louvain, Brussels, Belgium; Service de Microbiologie, Département de Biologie Clinique, Cliniques Universitaires Saint-Luc, Brussels, Belgium; European Society for Clinical Microbiology and Infectious Diseases (ESCMID) Study Group for Mycobacterial Infections (ESGMYC), ESCMID, Basel, Switzerland
| | - C Isaacs
- Foundation for Innovative New Diagnostics, Geneva, Switzerland
| | - D Affolabi
- Faculty of Health Sciences, Abomey-Calavi University, Cotonou, National Tuberculosis Programme, Cotonou, Benin
| | - R Alagna
- TB Supranational Reference Laboratory, Istituto di Ricovero e Cura a Carattere Scientifico San Raffaele Scientific Institute, Milan, Italy
| | - D Brockmann
- Institute for Theoretical Biology, Department of Biology, Humboldt University of Berlin, Berlin, Germany; Epidemiological Modelling of Infectious Diseases, Robert Koch Institute, Berlin, Germany
| | - B C de Jong
- Unit of Mycobacteriology, Department of Biomedical Sciences, Institute of Tropical Medicine, Belgium
| | - E Cambau
- European Society for Clinical Microbiology and Infectious Diseases (ESCMID) Study Group for Mycobacterial Infections (ESGMYC), ESCMID, Basel, Switzerland; Université Paris Diderot, Institut National de la Santé et de la Recherche Médicale, Unité mixte de recherche 1137, Infection, Antimicrobiens, Modélisation, Evolution, Paris, Bactériologie, Assistance Publique-Hôpitaux de Paris, Hôpital Lariboisière, Paris, France
| | | | - T Cohen
- Yale School of Public Health, Yale University, New Haven, Connecticut, USA
| | - M Delmee
- Pôle de Microbiologie Médicale, Institut de Recherche Expérimentale et Clinique, Université Catholique de Louvain, Brussels, Belgium; Service de Microbiologie, Département de Biologie Clinique, Cliniques Universitaires Saint-Luc, Brussels, Belgium
| | - J-C Delvenne
- Institute of Information and Communication Technologies, Electronics and Applied Mathematics, Centre for Operations Research and Econometrics, Université Catholique de Louvain, Belgium
| | - M Farhat
- Massachusetts General Hospital, Boston, Massachusetts, USA
| | - A Habib
- Interactive Health Solutions, Karachi, Pakistan
| | - P Holme
- Sungkyunkwan University, Seoul, South Korea
| | - S Keshavjee
- Harvard Medical School Center for Global Health Delivery, Dubai, United Arab Emirates
| | - A Khan
- Interactive Research and Development, Karachi, Pakistan
| | - P Lightfoot
- Foundation for Innovative New Diagnostics, Geneva, Switzerland
| | - D Moore
- TB Centre, London School of Hygiene & Tropical Medicine, London, UK
| | - Y Moreno
- Institute for Biocomputation and Physics of Complex Systems (BIFI), Department of Theoretical Physics, Faculty of Sciences, University of Zaragoza, Zaragoza, Spain
| | | | - M Pai
- McGill International TB Centre & McGill Global Health Programs, McGill University, Montreal, Quebec, Canada
| | - S Patel
- University Hospital Southampton NHS Foundation Trust, Southampton, UK
| | | | - L E C Rocha
- Karolinska Institutet, Stockholm, Sweden, Université de Namur, Namur, Belgium
| | - J Takle
- Global Connectivity LLC, Somerville, Massachusetts, USA
| | - A Trébucq
- International Union Against Tuberculosis and Lung Disease, France
| | - J Creswell
- Stop TB Partnership, Geneva, Switzerland
| | - C Boehme
- Foundation for Innovative New Diagnostics, Geneva, Switzerland
| |
Collapse
|
2
|
Traag VA, Aldecoa R, Delvenne JC. Detecting communities using asymptotical surprise. Phys Rev E Stat Nonlin Soft Matter Phys 2015; 92:022816. [PMID: 26382463 DOI: 10.1103/physreve.92.022816] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/02/2015] [Indexed: 06/05/2023]
Abstract
Nodes in real-world networks are repeatedly observed to form dense clusters, often referred to as communities. Methods to detect these groups of nodes usually maximize an objective function, which implicitly contains the definition of a community. We here analyze a recently proposed measure called surprise, which assesses the quality of the partition of a network into communities. In its current form, the formulation of surprise is rather difficult to analyze. We here therefore develop an accurate asymptotic approximation. This allows for the development of an efficient algorithm for optimizing surprise. Incidentally, this leads to a straightforward extension of surprise to weighted graphs. Additionally, the approximation makes it possible to analyze surprise more closely and compare it to other methods, especially modularity. We show that surprise is (nearly) unaffected by the well-known resolution limit, a particular problem for modularity. However, surprise may tend to overestimate the number of communities, whereas they may be underestimated by modularity. In short, surprise works well in the limit of many small communities, whereas modularity works better in the limit of few large communities. In this sense, surprise is more discriminative than modularity and may find communities where modularity fails to discern any structure.
Collapse
Affiliation(s)
- V A Traag
- Royal Netherlands Institute of Southeast Asian and Caribbean Studies, Leiden, The Netherlands
- e-Humanities Group, Royal Netherlands Academy of Arts and Sciences, Amsterdam, The Netherlands
| | - R Aldecoa
- Department of Physics, Northeastern University, Boston, Massachusetts 02115, USA
| | - J-C Delvenne
- ICTEAM, Université catholique de Louvain, Louvain-la-Neuve, Belgium
- CORE, Université catholique de Louvain, Louvain-la-Neuve, Belgium
| |
Collapse
|
3
|
Lambiotte R, Sinatra R, Delvenne JC, Evans TS, Barahona M, Latora V. Flow graphs: interweaving dynamics and structure. Phys Rev E Stat Nonlin Soft Matter Phys 2011; 84:017102. [PMID: 21867345 DOI: 10.1103/physreve.84.017102] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/06/2010] [Indexed: 05/31/2023]
Abstract
The behavior of complex systems is determined not only by the topological organization of their interconnections but also by the dynamical processes taking place among their constituents. A faithful modeling of the dynamics is essential because different dynamical processes may be affected very differently by network topology. A full characterization of such systems thus requires a formalization that encompasses both aspects simultaneously, rather than relying only on the topological adjacency matrix. To achieve this, we introduce the concept of flow graphs, namely weighted networks where dynamical flows are embedded into the link weights. Flow graphs provide an integrated representation of the structure and dynamics of the system, which can then be analyzed with standard tools from network theory. Conversely, a structural network feature of our choice can also be used as the basis for the construction of a flow graph that will then encompass a dynamics biased by such a feature. We illustrate the ideas by focusing on the mathematical properties of generic linear processes on complex networks that can be represented as biased random walks and their dual consensus dynamics, and show how our framework improves our understanding of these processes.
Collapse
Affiliation(s)
- R Lambiotte
- Department of Mathematics, Imperial College London, London, United Kingdom
| | | | | | | | | | | |
Collapse
|