Architecture of a Learning Surveillance System for Malaria Elimination in India

Sreeganga, S. D.; Mitra, Susanna Ghosh; Ramaprasad, Arkalgud

doi:10.5220/0008944103770382

Cited by 1 publication

(3 citation statements)

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“…The ontology was developed in two phases from that of an earlier work on cyberenvironment for malaria surveillance proposed by two authors-one from computer science working on malaria surveillance, and the other from management information systems and strategic management [12] incorporating relevant elements that would capture the complex and dynamic landscape of surveillance presented in a conference. That ontology was first refined and extended for a project on developing a learning surveillance system for malaria elimination in India in collaboration with community medicine, public health, and healthcare systems experts [6]. The stakeholder dimension was added to the ontology in this phase.…”

Section: Ontologymentioning

confidence: 99%

“…Two tests of convergence to a consensus on the elements of a dimension were whether they were reasonably (a) mutually exclusive, and (b) exhaustive for TB surveillance, based on the research available and practice knowledge of the collaborators. The initial and subsequent ontologies have been validated by peer review and presentation at academic conferences [6,7].…”

Section: Ontologymentioning

confidence: 99%

“…Its attributes are in line with the Learning Health Systems described as 'cyber-social ecosystem', to solve complex interdisciplinary problems of timely evidence, and to support best care practices [5]. As an emergent area of research, the concept has been applied to malaria elimination [6] and vector-borne diseases [7].…”

Section: Introductionmentioning

confidence: 99%

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Envisioning a learning surveillance system for tuberculosis

Gadicherla

Krishnappa

Madhuri³

et al. 2020

PLoS ONE

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Surveillance is critical for interrupting transmission of global epidemics. Research has highlighted gaps in the surveillance for tuberculosis that range from failure to collect real-time data to lack of standardization of data for informed decision-making at different levels of the health system. Our research aims to advance conceptual and methodological foundations for the development of a learning surveillance system for Tuberculosis, that involves systematic collection, analysis, interpretation, and feedback of outcome-specific data. It would concurrently involve the health care delivery system, public health laboratory, and epidemiologists. For our study, we systemically framed the cyber environment of TB surveillance as an ontology of the learning surveillance system. We validated the ontology by binary coding of dimensions and elements of the ontology with the metadata from an existing surveillance platform—GPMS TB Transportal. Results show GPMS TB Transportal collects a critical range of data for active case investigation and presumptive case screening for identifying and detecting confirmed TB cases. It is therefore targeted at assisting the Active Case Finding program. Building on the results, we demonstrate enhanced surveillance strategies for GPMS that are enumerated as pathways in the ontology. Our analysis reveals the scope for embedding learning surveillance pathways for digital applications in Direct Benefit Transfer, and Drug Resistance Treatment in National TB Elimination Programme in India. We discuss the possibilities of developing the transportal into a multi-level computer-aided decision support system for TB, using the innumerable pathways encapsulated in the ontology.

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Section: Ontologymentioning

confidence: 99%

Section: Ontologymentioning

confidence: 99%