The 10th and largest Ebola virus disease epidemic in the Democratic Republic of the Congo (DRC) was declared in North Kivu Province in August 2018 and ended in June 2020. We describe and evaluate an Early Warning, Alert and Response System (EWARS) implemented in the Beni health zone of DRC during August 5, 2018-June 30, 2020. During this period, 194,768 alerts were received, of which 30,728 (15.8%) were validated as suspected cases. From these, 801 confirmed and 3 probable cases were detected. EWARS showed an overall good performance: sensitivity and specificity >80%, nearly all (97%) of alerts investigated within 2 hours of notification, and good demographic representativeness. The average cost of the system was US $438/case detected and US $1.8/alert received. The system was stable, despite occasional disruptions caused by political insecurity. Our results demonstrate that EWARS was a cost-effective component of the Ebola surveillance strategy in this setting.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8632192 | PMC |
| http://dx.doi.org/10.3201/eid2712.210290 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
The analysis of urban collaborative governance in public health emergencies with fuzzy theory based on BP algorithm.
Sci Rep
December 2024
School of Management Science and Engineering, Shandong Jianzhu University, Jinan, 250101, China.
This study seeks to improve urban supply chain management and collaborative governance in the context of public health emergencies (PHEs) by integrating fuzzy theory with the Back Propagation Neural Network (BPNN) algorithm. By combining these two approaches, an early warning mechanism for supply chain risks during PHEs is developed. The study employs Matlab software to simulate supply chain risks, incorporating fuzzy inference techniques with the adaptive data modeling capabilities of neural networks for both training and testing.
View Article and Find Full Text PDFPerformance of A-DROP, NEWS2, and REMS in predicting in-hospital mortality and mechanical ventilation in pneumonia patients in the emergency department: a retrospective cohort study.
Int J Emerg Med
December 2024
Department of Emergency Medicine, Faculty of Medicine, Siriraj Hospital, Mahidol University, 2 Wanglang Road, Bangkoknoi, Bangkok, 10700, Thailand.
Background: Pneumonia is a potentially life-threatening respiratory tract infection. Many Early Warning Scores (EWS) were developed to detect patients with high risk for adverse clinical outcomes, but few have explored the utility of these EWS for pneumonia patients in the Emergency Department (ED) setting. We aimed to compare the prognostic utility of A-DROP, NEWS2, and REMS in predicting in-hospital mortality and the requirement for mechanical ventilation among ED patients with pneumonia.
View Article and Find Full Text PDFMultidimensional evaluation of seismic emergency capabilities in Chinese cities: the case of Changchun.
Sci Rep
December 2024
College of Jilin Emergency Management, Changchun Institute of Technology, Changchun, 130012, China.
In the context of rapid urbanization, the proliferation of high-density residential zones and intricate infrastructure networks markedly amplifies a city's susceptibility to natural calamities, notably seismic events. Thus, a precise evaluation of a city's emergency capability for seismic events is imperative. This research proposes a novel and all-encompassing evaluation framework for indicators, grounded in crisis management theory, covering the entire spectrum of disaster mitigation, preparedness, response, and recovery.
View Article and Find Full Text PDFDeep learning-based study on assessment and enhancement strategy for geological disaster emergency evacuation capacity in Changbai Mountain North Scenic Area.
Sci Rep
December 2024
College of Jilin Emergency Management, Changchun Institute of Technology, Changchun, 130021, China.
This study focuses on the northern scenic area of Changbai Mountain, aiming to evaluate the emergency evacuation capacity of the region in the context of geological disasters and to formulate corresponding improvement strategies. Due to the relatively small area of this region, difficulties in data acquisition, and insufficient precision, traditional models for evaluating emergency evacuation capacity are typically applied to urban built environments, with relatively few studies addressing scenic areas. To tackle these issues, this research employs the Real-Enhanced Super-Resolution Generative Adversarial Network (Real-ESRGAN), which successfully resolves the problem of blurriness in remote sensing images and significantly enhances image clarity.
View Article and Find Full Text PDFIntelligent identification and warning method of disc cutter abnormal wear in TBM construction based on extreme learning machine.
Sci Rep
December 2024
Xinjiang Irtysh River Investment and Development (Group) Co., Ltd., Wulumuqi, 830000, China.
Abnormal cutter wear has a serious impact on TBM construction. If not found in time, it may lead to the cutterhead overall failure. Aiming at this problem, a general model and method to identify and warn the abnormal cutter wear using Extreme Learning Machine (ELM) is proposed.
View Article and Find Full Text PDFWant 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!