scholarly journals Epitweetr: Early warning of public health threats using Twitter data

2021 ◽  
Author(s):  
Laura Espinosa ◽  
Ariana Wijermans ◽  
Francisco Orchard ◽  
Michael Hoehle ◽  
Thomas Czernichow ◽  
...  
Keyword(s):  
Public Health ◽  
Signal Detection ◽  
Early Warning ◽  
Detection Algorithm ◽  
Detection Algorithms ◽  
Twitter Data ◽  
Epidemic Intelligence ◽  
Health Threats ◽  
Correct Location ◽  
Sensitivity Specificity

Background: ECDC performs epidemic intelligence activities to systematically collate information from a variety of sources, including Twitter, to rapidly detect public health events. The lack of a freely available, customisable and automated early warning tool using Twitter data, prompted ECDC to develop epitweetr. The specific objectives are to assess the performance of the geolocation and signal detection algorithms used by epitweetr and to assess the performance of epitweetr in comparison with the manual monitoring of Twitter for early detection of public health threats. Methods: Epitweetr collects, geolocates and aggregates tweets to generate signals and email alerts. Firstly, we evaluated manually the tweet geolocation characteristics of 1,200 tweets, and assessed its accuracy in extracting the correct location and its performance in detecting tweets with available information on the tweet geolocation. Secondly, we evaluated signals generated by epitweetr between 19 October and 30 November 2020 and we calculated the positive predictive value (PPV). Then, we evaluated the sensitivity, specificity and timeliness of epitweetr in comparison with Twitter manual monitoring. Findings: The epitweetr geolocation algorithm had an accuracy of 30.1% and 25.9% at national and subnational levels, respectively. General and specific PPV of the signal detection algorithm was 3.0% and 74.6%, respectively. Epitweetr and/or manual monitoring detected 570 signals and 454 events. Epitweetr had a sensitivity of 78.6% [75.2% - 82.0%] and PPV of 74.6% [70.5% - 78.6%]; and the manual monitoring had a sensitivity of 47.9% [43.8% - 52.0%] and PPV of 97.9% [95.8% - 99.9%]. The median validation time difference between sixteen common events detected by epitweetr and manual monitoring was -48.6 hours [(-102.8) - (-23.7) hours]. Interpretation: Epitweetr has shown to have sufficient performance as an early warning tool for public health threats using Twitter data. Having developed epitweetr as a free, open-source tool with several configurable settings and a strong automated component, it is expected to increase its usability and usefulness to public health experts.

scholarly journals Epitweetr: Early Warning of Public Health Threats Using Twitter Data

2021 ◽  
Author(s):  
Laura Espinosa ◽  
Ariana Wijermans ◽  
Francisco Orchard ◽  
Michael Höhle ◽  
Thomas Czernichow ◽  
...  
Keyword(s):  
Public Health ◽  
Early Warning ◽  
Twitter Data ◽  
Health Threats

Study of BLAST Signal Detection Algorithm in LTE System

2013 ◽  
Vol 756-759 ◽  
pp. 3183-3188
Author(s):  
Tao Lei ◽  
Deng Ping He ◽  
Fang Tang Chen
Keyword(s):  
Signal Detection ◽  
High Speed ◽  
Data Communication ◽  
Detection Algorithm ◽  
Detection Performance ◽  
Detection Algorithms ◽  
High Speed Data ◽  
Simulation Results ◽  
Better Than

BLAST can achieve high speed data communication. Its signal detection directly affects performance of BLAST receiver. This paper introduced several signal detection algorithmsZF algorithm, MMSE algorithm, ZF-SIC algorithm and MMSE-SIC algorithm. The simulation results show that the traditional ZF algorithm has the worst performance, the traditional MMSE algorithm and the ZF-SIC algorithm is similar, but with the increase of the SNR, the performance of ZF-SIC algorithm is better than MMSE algorithm. MMSE-SIC algorithm has the best detection performance in these detection algorithms.

scholarly journals An Assessment of Coherence Between Early Warning and Response Systems and Serious Cross-Border Health Threats in the European Union and Turkey

2016 ◽  
Vol 10 (6) ◽  
pp. 883-892 ◽  
Author(s):  
Perihan Elif Ekmekci
Keyword(s):  
Public Health ◽  
European Union ◽  
Early Warning ◽  
World Health ◽  
The European Union ◽  
Cross Border ◽  
Response Planning ◽  
Health Threats ◽  
Response Systems ◽  
The Eu

AbstractDisease outbreaks have attracted the attention of the public health community to early warning and response systems (EWRS) for communicable diseases and other cross-border threats to health. The European Union (EU) and the World Health Organization (WHO) have published regulations in this area. Decision 1082/2013/EU brought a new approach the management of public health threats in EU member states. Decision 1082/2013/EU brought several innovations, which included establishing a Health Security Committee; preparedness and response planning; joint procurement of medical countermeasures; ad hoc monitoring for biological, chemical, and environmental threats; EWRS; and recognition of an emergency situation and interoperability between various sectors. Turkey, as an acceding country to the EU and a member of the WHO, has been improving its national public health system to meet EU legislations and WHO standards. This article first explains EWRS as defined in Decision 1082/2013/EU and Turkey’s obligations to align its public health laws to the EU acquis. EWRS in Turkey are addressed, particularly their coherence with EU policies regarding preparedness and response, alert notification, and interoperability between health and other sectors. Finally, the challenges and limitations of the current Turkish system are discussed and further improvements are suggested. (Disaster Med Public Health Preparedness. 2016;10:883–892)

scholarly journals Evaluation of the device independent nature of a photoplethysmography-deriving smartphone app

EP Europace ◽  
2021 ◽  
Vol 23 (Supplement_3) ◽  
Author(s):  
H Gruwez ◽  
S Evens ◽  
T Proesmans ◽  
C Smeets ◽  
P Haemers ◽  
...  
Keyword(s):  
Atrial Flutter ◽  
Sensitivity And Specificity ◽  
Heart Rhythm ◽  
Smartphone Application ◽  
Detection Algorithm ◽  
Interquartile Range ◽  
Detection Algorithms ◽  
Funding Sources ◽  
Successful Measurement ◽  
Sensitivity Specificity

Abstract Funding Acknowledgements Type of funding sources: None. Background Smartphone apps using photoplethysmography (PPG) technology enable digital heart rhythm monitoring through their built-in camera, without the need for additional, specific, or costly hardware. This may positively impact the availability and scalability of remote monitoring. However, the diversity of smartphone specifications on the consumer market may raise concerns regarding the robustness of AF detection algorithms between various devices. Purpose To study the device independency of AF detection performance by a PPG-based smartphone application. Methods Patients from the cardiology department were consecutively enrolled. Patients were handed 7 iOS models and 1 Android model and were asked to consecutively perform one PPG measurement per device. A 12-lead electrocardiogram (ECG) was collected during the same consultation and interpreted by a cardiologist as reference diagnosis. To allow an objective comparison across the devices, patients who failed to perform one successful measurement on each device were excluded. Additional exclusions were atrial flutter rhythms and insufficient quality results. Sensitivity, specificity and accuracy were calculated with respect to the reference diagnosis. McNemar’s analysis was used for the head-to-head comparison of the sensitivity and specificity of the proprietary algorithm on the different smartphone devices. Results A total of 150 patients participated in the study with a median CHA2DS2-VASc score of 3 (interquartile range: 1-5). The median age of the study population was 70 (interquartile range: 56-79) years. In total, 54.7% of the population was male and the AF-prevalence was 35.3%. After the exclusion of patients with atrial flutter (n = 14) and patients who did not successfully perform a PPG measurement on each device (n = 5), diagnostic-grade results of 131 patients were used to calculate the performance of the proprietary algorithm. The sensitivity and specificity of the AF detection algorithm ranged from 90.9% (95% CI 75.7-98.1) to 100.0% (95% CI 91.0-100) and 94.5% (95% CI 86.6-98.5) to 100.0% (95% CI 94.6-100), respectively. The overall accuracy across the devices ranged from 94.4% (95% CI 88.3-97.9) to 99.0% (95% CI 94.6-100). Head-to-head comparisons of the results did not reveal significant differences in sensitivity (P = 0.125-1.000) or specificity (P = 0.375-1.000) of the proprietary AF detection algorithm among the different devices. Conclusion This study demonstrated the device-independent nature of the PPG-deriving smartphone application with respect to 12-lead ECG diagnosis.

scholarly journals What is epidemic intelligence, and how is it being improved in Europe?

2006 ◽  
Vol 11 (5) ◽  
Author(s):  
R Kaiser ◽  
D Coulombier ◽  
M Baldari ◽  
D Morgan ◽  
C Paquet
Keyword(s):  
Public Health ◽  
Early Identification ◽  
Potential Health ◽  
Health Measures ◽  
Epidemic Intelligence ◽  
Health Threats

‘Epidemic intelligence’ can be defined as all the activities related to early identification of potential health threats, their verification, assessment and investigation in order to recommend public health measures to control them.

REDUCING THE COMPUTATIONAL COMPLEXITY OF SIGNAL DETECTION IN MIMO SYSTEMS

2021 ◽  
Author(s):  
М.Г. БАКУЛИН ◽  
Т.Б.К. БЕН РАЖЕБ ◽  
В.Б. КРЕЙНДЕЛИН ◽  
А.Э. СМИРНОВ
Keyword(s):  
Computational Complexity ◽  
Signal Detection ◽  
Massive Mimo ◽  
Communication Systems ◽  
Noise Immunity ◽  
Mimo Systems ◽  
Detection Algorithm ◽  
Wireless Communication Systems ◽  
Detection Algorithms ◽  
Mimo Technology

С развитием технологии MIMO и появлением технологии massive MIMO возросла сложность обработки сигнала на приемной стороне. Применение известных алгоритмов детектирования сигнала на приемной стороне становится трудно реализуемым из-за высокой вычислительной сложности. Предлагается новая реализация известного алгоритма МСКО, которая позволяет снизить вычислительную сложность детектирования без потерь в помехоустойчивости в системах беспроводной связи, использующих технологию massive MIMO. With the development of MIMO technology and the appearance of the massive MIMO technology, the computational complexity of signal processing on the receiving side has increased. The application of known signal detection algorithms used in MIMO systems becomes difficult or even impossible to implement in massive MIMO systems because of computational complexity. We offer a new realization technique of the well-known MMSE detection algorithm with less computational complexity and without any loss in noise immunity in wireless communication systems using massive MIMO technology.

The CARE Telematics Network for the Surveillance of Influenza in Europe

1995 ◽  
Vol 34 (05) ◽  
pp. 518-522 ◽  
Author(s):  
M. Bensadon ◽  
A. Strauss ◽  
R. Snacken
Keyword(s):  
Public Health ◽  
Early Warning ◽  
Early Warning Systems ◽  
Warning Systems ◽  
Surveillance Networks ◽  
Spanish Flu ◽  
The 1950S ◽  
National Networks ◽  
First Time

Abstract:Since the 1950s, national networks for the surveillance of influenza have been progressively implemented in several countries. New epidemiological arguments have triggered changes in order to increase the sensitivity of existent early warning systems and to strengthen the communications between European networks. The WHO project CARE Telematics, which collects clinical and virological data of nine national networks and sends useful information to public health administrations, is presented. From the results of the 1993-94 season, the benefits of the system are discussed. Though other telematics networks in this field already exist, it is the first time that virological data, absolutely essential for characterizing the type of an outbreak, are timely available by other countries. This argument will be decisive in case of occurrence of a new strain of virus (shift), such as the Spanish flu in 1918. Priorities are now to include other existing European surveillance networks.

Evaluation of a New Field Epidemiology Training Program Intermediate Course to Strengthen Public Health Workforce Capacity in Tanzania

2021 ◽  
pp. 003335492097466
Author(s):  
Kate Wilson ◽  
Amir Juya ◽  
Ahmed Abade ◽  
Senga Sembuche ◽  
Devotha Leonard ◽  
...  
Keyword(s):  
Public Health ◽  
Training Program ◽  
Health Workforce ◽  
Outcome Evaluation ◽  
Exit Interviews ◽  
Field Epidemiology ◽  
Health Threats ◽  
Before And After ◽  
Workforce Capacity ◽  
Field Epidemiology Training Program

Objectives Sub-Saharan Africa faces a shortage of skilled epidemiologists to prevent, detect, and respond to health threats. Tanzania has implemented one of the first Centers for Disease Control and Prevention Field Epidemiology Training Program (FETP) Intermediate courses in Africa. This course aims to strengthen health workforce capacity in surveillance system assessment, outbreak investigation, and evaluation, prioritizing HIV control. We conducted an outcome evaluation of this new course. Methods We used a pre/post evaluation design using data from 4 cohorts of trainees who took the FETP Intermediate course from 2017 to 2020. We conducted knowledge assessments before and after each cohort and combined those results. Outcomes included knowledge and self-rated competency and trends in integrated disease surveillance and response (IDSR) data. We collected data through tests, field assignments, exit interviews, and data audits. We compared the mean change in pre-/posttest scores using linear regression and 95% CIs. We used content analysis to summarize exit interviews. Results Fifty-three FETP trainees from 10 regions enrolled in the FETP Intermediate course, and 52 (99.0%) completed the course. We found substantial increases in mean knowledge (44.0 to 68.0 points) and self-rated competency (4.14 to 4.43) scores before and after the course. Trainees evaluated 52 surveillance systems and 52 district HIV care programs, and 39 (75.0%) trainees participated in outbreak investigations. From before to after cohort 1, timeliness and completeness of IDSR reports increased from 4.2% to 52.1% and from 27.4% to 76.5%, respectively. Course strengths were quality of instruction, individualized mentoring, and practical skills gained. Challenges were mentor availability, limited time for data analysis practice, and balancing work and field assignments. Conclusions The Tanzania FETP Intermediate course substantially improved trainee knowledge and helped to improve local data quality and reporting. This course is a promising model to strengthen subnational capacity to prevent, detect, and respond to public health threats in Africa.

A dual U-Net algorithm for automating feature extraction from satellite imagery

2021 ◽  
pp. 154851292098354
Author(s):  
Samuel Humphries ◽  
Trevor Parker ◽  
Bryan Jonas ◽  
Bryan Adams ◽  
Nicholas J Clark
Keyword(s):  
Neural Networks ◽  
Object Detection ◽  
Satellite Images ◽  
Detection Algorithm ◽  
Military Operations ◽  
Detection Algorithms ◽  
Us Military ◽  
Standard Product ◽  
Different Types ◽  
Road Intersections

Quick identification of building and roads is critical for execution of tactical US military operations in an urban environment. To this end, a gridded, referenced, satellite images of an objective, often referred to as a gridded reference graphic or GRG, has become a standard product developed during intelligence preparation of the environment. At present, operational units identify key infrastructure by hand through the work of individual intelligence officers. Recent advances in Convolutional Neural Networks, however, allows for this process to be streamlined through the use of object detection algorithms. In this paper, we describe an object detection algorithm designed to quickly identify and label both buildings and road intersections present in an image. Our work leverages both the U-Net architecture as well the SpaceNet data corpus to produce an algorithm that accurately identifies a large breadth of buildings and different types of roads. In addition to predicting buildings and roads, our model numerically labels each building by means of a contour finding algorithm. Most importantly, the dual U-Net model is capable of predicting buildings and roads on a diverse set of test images and using these predictions to produce clean GRGs.

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