Objectives: Globally, disease surveillance systems are playing a significant role in outbreak detection and response management of Infectious Diseases (IDs). However, in developing countries like Pakistan, epidemic outbreaks are difficult to detect due to scarcity of public health data and absence of automated surveillance systems. Our research is intended to formulate an integrated service-oriented visual analytics architecture for ID surveillance, identify key constituents and set up a baseline for easy reproducibility of such systems in the future.
Study Design: This research focuses on development of ID-Viewer, which is a visual analytics decision support system for ID surveillance. It is a blend of intelligent approaches to make use of real-time streaming data from Emergency Departments (EDs) for early outbreak detection, health care resource allocation and epidemic response management.
Methods: We have developed a robust service-oriented visual analytics architecture for ID surveillance, which provides automated mechanisms for ID data acquisition, outbreak detection and epidemic response management. Classification of chief-complaints is accomplished using dynamic classification module, which employs neural networks and fuzzy-logic to categorize syndromes. Standard routines by Center for Disease Control (CDC), i.e. c1-c3 (c1-mild, c2-medium and c3-ultra), and spatial scan statistics are employed for detection of temporal and spatio-temporal disease outbreaks respectively. Prediction of imminent disease threats is accomplished using support vector regression for early warnings and response planning. Geographical visual analytics displays are developed that allow interactive visualization of syndromic clusters, monitoring disease spread patterns, and identification of spatio-temporal risk zones.
Results: We analysed performance of surveillance framework using ID data for year 2011-2015. Dynamic syndromic classifier is able to classify chief-complaints to appropriate syndromes with high classification accuracy. Outbreak detection methods are able to detect the ID outbreaks in start of epidemic time zones. Prediction model is able to forecast dengue trend for 20 weeks ahead with nominal normalized root mean square error of 0.29. Interactive geo-spatiotemporal displays, i.e. heat-maps, and choropleth are shown in respective sections.
Conclusion: The proposed framework will set a standard and provide necessary details for future implementation of such a system for resource-constrained regions. It will improve early outbreak detection attributable to natural and man-made biological threats, monitor spatio-temporal epidemic trends and provide assurance that an outbreak has, or has not occurred. Advanced analytics features will be beneficial in timely organization/formulation of health management policies, disease control activities and efficient health care resource allocation.
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http://dx.doi.org/10.1016/j.puhe.2016.01.006 | DOI Listing |
Front Parasitol
March 2024
Center for Research in Infectious Diseases, College of Graduate Studies and Research, Mount Kenya University, Thika, Kenya.
Introduction: Schistosomiasis (Bilharzia), a neglected tropical disease caused by parasites, afflicts over 240 million people globally, disproportionately impacting Sub-Saharan Africa. Current diagnostic tests, despite their utility, suffer from limitations like low sensitivity. Polymerase chain reaction (PCR) and quantitative real-time PCR (qPCR) remain the most common and sensitive nucleic acid amplification tests.
View Article and Find Full Text PDFIntroduction: Visual Inspection with Acetic Acid (VIA) has been adopted for cervical cancer screening in Kenya and other Low-Middle Income Countries despite providing suboptimal results among HIV-infected women. It is mostly performed by nurses in health centers. Innovative ways of improving the performance of VIA in HIV-infected women are desired.
View Article and Find Full Text PDFSci Rep
January 2025
Department of Nephrology, Kanazawa Medical University, 1-1 Daigaku, Uchinada, 920-0293, Ishikawa, Japan.
To decrease the number of chronic kidney disease (CKD), early diagnosis of diabetic kidney disease is required. We performed invariant information clustering (IIC)-based clustering on glomerular images obtained from nephrectomized kidneys of patients with and without diabetes. We also used visualizing techniques (gradient-weighted class activation mapping (Grad-CAM) and generative adversarial networks (GAN)) to identify the novel and early pathological changes on light microscopy in diabetic nephropathy.
View Article and Find Full Text PDFAnal Chem
January 2025
State Key Laboratory of Electroanalytical Chemistry, Changchun Institute of Applied Chemistry, Chinese Academy of Sciences, 5625 Renmin Street, Changchun 130022, P. R. China.
Arginine (Arg) is involved in tissue metabolism and regulates the immune function; thereby, achieving the detection of Arg is crucial for early diagnosis and treatment of diseases. Herein, dual ratiometric fluorescence sensors were prepared with the blue emission of levorotatory/dextrorotatory carbon dots (CDs) and the red emission of porphyrin (L/D-CDs-PP) for the sensitive and portable detection of Arg. Interestingly, L-CDs-PP and D-CDs-PP displayed not only the dual emission peaks at 493 and 650 nm but also different response modes to Arg; thus, they could serve as dual ratiometric fluorescence sensors to achieve the accurate and reliable detection of Arg, with the detection limit of 23.
View Article and Find Full Text PDFOphthalmol Sci
November 2024
Notal Vision Inc., Manassas, Virginia.
Purpose: To validate the performance of the Notal OCT Analyzer (NOA) in processing self-administered OCT images from an OCT system designed for home use (home OCT [HOCT]) as part of a pivotal study aimed at achieving de novo United States Food and Drug Admininstration marketing authorization.
Design: A prospective quantitative cross-sectional artificial intelligence study.
Participants: The study enrolled adults aged ≥55 years diagnosed with neovascular age-related macular degeneration (nAMD) in ≥1 eligible eye with a best-corrected visual acuity of 20/320 or better.
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