Spatio-temporal cross-correlation analysis of catfish retinal neurons

1981 ◽  
Vol 41 (3) ◽  
pp. 179-196 ◽  
Author(s):  
Robert L. Powers ◽  
David W. Arnett
Keyword(s):  
Correlation Analysis ◽  
Cross Correlation ◽  
Retinal Neurons ◽  
Cross Correlation Analysis ◽  
Spatio Temporal

scholarly journals A Syndromic Surveillance Tool to Detect Anomalous Clusters of COVID-19 Symptoms in the United States

2020 ◽  
Author(s):  
Amparo Güemes ◽  
Soumyajit Ray ◽  
Khaled Aboumerhi ◽  
Michael Richard Desjardins ◽  
Anton Kvit ◽  
...  
Keyword(s):  
Correlation Analysis ◽  
Syndromic Surveillance ◽  
Large Scale ◽  
Cross Correlation ◽  
Warning System ◽  
The United States ◽  
Johns Hopkins University ◽  
Cross Correlation Analysis ◽  
Spatio Temporal ◽  
And Control

Coronavirus SARS-COV-2 infections continue to spread across the world, yet effective large-scale disease detection and prediction remain limited. COVID Control: A Johns Hopkins University Study, is a novel syndromic surveillance approach, which collects body temperature and COVID-like illness (CLI) symptoms across the US using a smartphone app and applies spatio-temporal clustering techniques and cross-correlation analysis to create maps of abnormal symptomatology incidence that are made publicly available. The results of the cross-correlation analysis identify optimal temporal lags between symptoms and a range of COVID-19 outcomes, with new taste/smell loss showing the highest correlations. We also identified temporal clusters of change in taste/smell entries and confirmed COVID-19 incidence in Baltimore City and County. Further, we utilized an extended simulated dataset to showcase our analytics in Maryland. The resulting clusters can serve as indicators of emerging COVID-19 outbreaks, and support syndromic surveillance as an early warning system for disease prevention and control.

scholarly journals A syndromic surveillance tool to detect anomalous clusters of COVID-19 symptoms in the United States

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Amparo Güemes ◽  
Soumyajit Ray ◽  
Khaled Aboumerhi ◽  
Michael R. Desjardins ◽  
Anton Kvit ◽  
...  
Keyword(s):  
Correlation Analysis ◽  
Syndromic Surveillance ◽  
Large Scale ◽  
Cross Correlation ◽  
Warning System ◽  
The United States ◽  
Johns Hopkins University ◽  
Cross Correlation Analysis ◽  
Spatio Temporal ◽  
And Control

AbstractCoronavirus SARS-COV-2 infections continue to spread across the world, yet effective large-scale disease detection and prediction remain limited. COVID Control: A Johns Hopkins University Study, is a novel syndromic surveillance approach, which collects body temperature and COVID-like illness (CLI) symptoms across the US using a smartphone app and applies spatio-temporal clustering techniques and cross-correlation analysis to create maps of abnormal symptomatology incidence that are made publicly available. The results of the cross-correlation analysis identify optimal temporal lags between symptoms and a range of COVID-19 outcomes, with new taste/smell loss showing the highest correlations. We also identified temporal clusters of change in taste/smell entries and confirmed COVID-19 incidence in Baltimore City and County. Further, we utilized an extended simulated dataset to showcase our analytics in Maryland. The resulting clusters can serve as indicators of emerging COVID-19 outbreaks, and support syndromic surveillance as an early warning system for disease prevention and control.

Application of Cross-correlation Analysis Method for Measurement of the Fluid Flow Rate Based on X-ray Radiation

2019 ◽  
Vol 11 (1) ◽  
pp. 01025-1-01025-5 ◽  
Author(s):  
N. A. Borodulya ◽  
◽  
R. O. Rezaev ◽  
S. G. Chistyakov ◽  
E. I. Smirnova ◽  
...  
Keyword(s):  
Fluid Flow ◽  
Correlation Analysis ◽  
Flow Rate ◽  
Cross Correlation ◽  
Fluid Flow Rate ◽  
Analysis Method ◽  
X Ray ◽  
Cross Correlation Analysis ◽  
Correlation Analysis Method

scholarly journals Features of Cross-Correlation Analysis in a Data-Driven Approach for Structural Damage Assessment

Sensors ◽  
2018 ◽  
Vol 18 (5) ◽  
pp. 1571 ◽  
Author(s):  
Jhonatan Camacho Navarro ◽  
Magda Ruiz ◽  
Rodolfo Villamizar ◽  
Luis Mujica ◽  
Jabid Quiroga
Keyword(s):  
Correlation Analysis ◽  
Damage Assessment ◽  
Structural Damage ◽  
Cross Correlation ◽  
Data Driven ◽  
Cross Correlation Analysis ◽  
Data Driven Approach

POWER LAW AND STRETCHED EXPONENTIAL EFFECTS OF EXTREME EVENTS IN CHINESE STOCK MARKETS

2010 ◽  
Vol 09 (02) ◽  
pp. 203-217 ◽  
Author(s):  
XIAOJUN ZHAO ◽  
PENGJIAN SHANG ◽  
YULEI PANG
Keyword(s):  
Correlation Analysis ◽  
Power Law ◽  
Extreme Events ◽  
Stock Markets ◽  
Cross Correlation ◽  
Extreme Values ◽  
Cumulative Distribution ◽  
Stretched Exponential ◽  
Cross Correlation Analysis ◽  
Chinese Stock Markets

This paper reports the statistics of extreme values and positions of extreme events in Chinese stock markets. An extreme event is defined as the event exceeding a certain threshold of normalized logarithmic return. Extreme values follow a piecewise function or a power law distribution determined by the threshold due to a crossover. Extreme positions are studied by return intervals of extreme events, and it is found that return intervals yield a stretched exponential function. According to correlation analysis, extreme values and return intervals are weakly correlated and the correlation decreases with increasing threshold. No long-term cross-correlation exists by using the detrended cross-correlation analysis (DCCA) method. We successfully introduce a modification specific to the correlation and derive the joint cumulative distribution of extreme values and return intervals at 95% confidence level.

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