Objective: To analyze the pilot results of both temporal and temporal-spatial models in outbreaks detection in China Infectious Diseases Automated-alert and Response System (CIDARS) to further improve the system.
Methods: The amount of signal, sensitivity, false alarm rate and time to detection regarding these two models of CIDARS, were analyzed from December 6, 2009 to December 5, 2010 in 221 pilot counties of 20 provinces.
Results: The sensitivity of these two models was equal (both 98.15%). However, when comparing to the temporal model, the temporal-spatial model had a 59.86% reduction on the signals (15 702) while the false alarm rate of the temporal-spatial model (0.73%) was lower than the temporal model (1.79%), and the time to detection of the temporal-spatial model (0 day) was also 1 day shorter than the temporal model.
Conclusion: Comparing to the temporal model, the temporal-spatial model of CIDARS seemed to be better performed on outbreak detection.
Download full-text PDF |
Source |
|---|
Publication Analysis
Top Keywords
Similar Publications
Computational exploration of injection strategies for improving medicine distribution in the liver.
Comput Biol Med
December 2024
Faculty of Chemical & Petroleum Engineering, University of Tabriz, Tabriz, Iran. Electronic address:
Background And Objectives: The liver, a vital metabolic organ, is always susceptible to various diseases that ultimately lead to fibrosis, cirrhosis, acute liver failure, chronic liver failure, and even cancer. Optimal and specific medicine delivery in various diseases, hepatectomy, shunt placement, and other surgical interventions to reduce liver damage, transplantation, optimal preservation, and revival of the donated organ all rely on a complete understanding of perfusion and mass transfer in the liver. This study aims to simulate the computational fluid dynamics of perfusion and the temporal-spatial distribution of a medicine in a healthy liver to evaluate the hemodynamic characteristics of flow and medicine transport with the purpose of more effective liver treatment.
View Article and Find Full Text PDFSet-pMAE: spatial-spEctral-temporal based parallel masked autoEncoder for EEG emotion recognition.
Cogn Neurodyn
December 2024
School of Computer Science and Engineering, Changchun University of Technology, Changchun, 130102 Jilin People's Republic of China.
The utilization of Electroencephalography (EEG) for emotion recognition has emerged as the primary tool in the field of affective computing. Traditional supervised learning methods are typically constrained by the availability of labeled data, which can result in weak generalizability of learned features. Additionally, EEG signals are highly correlated with human emotional states across temporal, spatial, and spectral dimensions.
View Article and Find Full Text PDFTemporal, spatial, and methodological considerations in evaluating the viability of measles wastewater surveillance.
Sci Total Environ
December 2024
Department of Civil & Environmental Engineering & Earth Sciences, University of Notre Dame, Notre Dame, IN 46556, United States of America. Electronic address:
Measles is a highly transmissible disease of increasing concern due to waning vaccination contributing to a significant rise in measles cases, with 283 reported cases and 16 outbreaks in the U.S. as of November 7, 2024.
View Article and Find Full Text PDFNernst-Planck-Gaussian finite element modelling of Ca electrodiffusion in amphibian striated muscle transverse tubule-sarcoplasmic reticular triadic junctional domains.
Front Physiol
December 2024
Physiological Laboratory, University of Cambridge, Cambridge, United Kingdom.
Introduction: Intracellular Ca signalling regulates membrane permeabilities, enzyme activity, and gene transcription amongst other functions. Large transmembrane Ca electrochemical gradients and low diffusibility between cell compartments potentially generate short-lived, localised, high-[Ca] microdomains. The highest concentration domains likely form between closely apposed membranes, as at amphibian skeletal muscle transverse tubule-sarcoplasmic reticular (T-SR, triad) junctions.
View Article and Find Full Text PDFMulti-dimensional hybrid bilinear CNN-LSTM models for epileptic seizure detection and prediction using EEG signals.
J Neural Eng
December 2024
School of Life Sciences, Tiangong University, NO.399, Binshuixi Road, Xiqing District, Tianjin, P.R.China., Tianjin, Tianjin, 300387, CHINA.
Objective: Automatic detection and prediction of epilepsy are crucial for improving patient care and quality of life. However, existing methods typically focus on single-dimensional information and often confuse the periodic and aperiodic components in electrophysiological signals.
Approach: We propose a novel deep learning framework that integrates temporal, spatial, and frequency information of EEG signals, in which periodic and aperiodic components are separated in the frequency domain.
Want AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!