Joint allocation of emergency medical resources with time-lag correlation during cross-regional epidemic outbreaks

2021 ◽  
pp. 107895
Author(s):  
Haiping Zhang ◽  
Wenhui Zhou ◽  
Yujiao Sun
Keyword(s):  
Time Lag ◽  
Medical Resources ◽  
Emergency Medical ◽  
Time Lag Correlation

Effect of spectral time-lag correlation coefficient and signal averaging on airborne CO 2 DIAL measurements

1997 ◽  
Author(s):  
Avishai Ben-David ◽  
Richard G. Vanderbeek ◽  
Steven W. Gotoff ◽  
Francis M. D'Amico
Keyword(s):  
Correlation Coefficient ◽  
Time Lag ◽  
Signal Averaging ◽  
Time Lag Correlation

scholarly journals The photon-index–time-lag correlation in black hole X-ray binaries

2017 ◽  
Vol 473 (4) ◽  
pp. 4644-4652 ◽  
Author(s):  
Pablo Reig ◽  
Nikolaos D. Kylafis ◽  
Iossif E. Papadakis ◽  
María Teresa Costado
Keyword(s):  
Black Hole ◽  
Time Lag ◽  
X Ray ◽  
Photon Index ◽  
X Ray Binaries ◽  
Time Lag Correlation

Retrospective, Analytical Study of Field First Aid following the Wenchuan Earthquake in China

2011 ◽  
Vol 26 (2) ◽  
pp. 130-134 ◽  
Author(s):  
Fan Haojun ◽  
Song Jianqi ◽  
Hou Shike
Keyword(s):  
Wenchuan Earthquake ◽  
Analytical Study ◽  
Causes Of Death ◽  
Cardiopulmonary Arrest ◽  
First Aid ◽  
Multiple Organ ◽  
Medical Resources ◽  
Emergency Rescue ◽  
The Wenchuan Earthquake ◽  
Emergency Medical

AbstractField first-aid data from the Wenchuan Earthquake in China was analyzed retrospectively in order to probe into ways to develop field first-aid operations and provide a reference for future emergency rescue. Related documents about the Wenchuan Earthquake were collected and reviewed. The state of injury and leading causes of death during the disaster were identified. The presnece of emergency medical resources on-site after the earthquake was relatively insufficient. Deaths mainly were due to cardiopulmonary arrest, severe craniocerebral injury, incurable hemorrhagic shock, and crush syndrome that caused multiple organ system dysfunction syndrome. Only by strengthening the on-site emergency medical resources, speeding-up triage, and equipping responders with professional, portable medical equipment, can field first-aid operations be delivered more efficiently.

scholarly journals Increase in public interest concerning alternative medicine during the COVID-19 pandemic in Indonesia: a Google Trends study

F1000Research ◽  
2021 ◽  
Vol 9 ◽  
pp. 1201
Author(s):  
Dewi Rokhmah ◽  
Khaidar Ali ◽  
Serius Miliyani Dwi Putri ◽  
Khoiron Khoiron
Keyword(s):  
Alternative Medicine ◽  
Public Interest ◽  
Time Lag ◽  
Google Trends ◽  
Rank Test ◽  
Search Term ◽  
Alternative Medicines ◽  
Search Volume ◽  
Relative Search Volume ◽  
Time Lag Correlation

Background: The COVID-19 pandemic has triggered individuals to increase their healthy behaviour in order to prevent transmission, including improving their immunity potentially through the use of alternative medicines. This study aimed to examine public interest on alternative medicine during the COVID-19 pandemic using Google Trends in Indonesia. Methods: Employing a quantitative study, the Spearman rank test was used to analyze the correlation between Google Relative Search Volume (RSV) of various search terms, within the categories of alternative medicine, herbal medicine and practical activity, with COVID-19 cases. In addition, time lag correlation was also investigated. Results: Public interest toward alternative medicine during COVID-19 pandemic in Indonesia is dramatically escalating. All search term categories (alternative medicine, medical herbal, and alternative medicine activities) were positively associated with COVID-19 cases (p<0.05). The terms ‘ginger’ (r=0.6376), ‘curcumin’ (r=0.6550) and ‘planting ginger’ (0.6713) had the strongest correlation. Furthermore, time lag correlation between COVID-19 and Google RSV was also positively significant (p<0.05). Conclusion: Public interest concerning alternative medicine related terms dramatically increased after the first COVID-19 confirmed case was reported in Indonesia. Time lag correlation showed good performance using weekly data. The Indonesian Government will play an important role to provide and monitor information related to alternative medicine in order for the population to receive the maximum benefit.

scholarly journals Jitter Extraction in a Noisy Signal by Fast Fourier Transform and Time Lag Correlation

2018 ◽  
Vol 884 ◽  
pp. 113-121 ◽  
Author(s):  
Gehan Anthonys ◽  
Michael J. Cree ◽  
Lee Streeter
Keyword(s):  
Fourier Transform ◽  
Fast Fourier Transform ◽  
Time Lag ◽  
Reference Signal ◽  
Clock Signal ◽  
Eye Diagram ◽  
Noisy Signals ◽  
Random Jitter ◽  
Reference Clock ◽  
Time Lag Correlation

Jitter in an electronic signal is any deviation in, or displacement of, the signal in time. This paper investigates on decomposition of two types of jitter, namely, periodic and random jitter in noisy signals. Generally, an oscilloscope generates an eye diagram by overlaying sweeps of different segments of a long data stream driven by the reference clock signal. We use the fast Fourier transform with time lag correlation of the signal since we do not have a clock reference signal and apply this technique to simulated noisy signals. We separately injected a random jitter (of known amount), periodic jitter (with known frequency and amount), and both together to various modulation frequencies of sinusoidal signals. The approach is validated by several experiments with numerous values in jitter parameters. When we separately inject random jitter (5 ps) and periodic jitter (5 ps at 4.37 MHz) to the signal, we obtained the results (4.52±0.25 ps) and (4.93±0.04 ps at 4.40±0.04 MHz), respectively.

Predicting Medical Resources Required to be Dispatched After Earthquake and Flood, Using Historical Data and Machine Learning Techniques

2018 ◽  
Vol 8 (2) ◽  
pp. 13-35
Author(s):  
Homer Papadopoulos ◽  
Antonis Korakis
Keyword(s):  
Machine Learning ◽  
Web Services ◽  
Large Scale ◽  
Historical Data ◽  
Machine Learning Techniques ◽  
Medical Resources ◽  
Simulation Tools ◽  
Medical Response ◽  
Emergency Medical ◽  
Learning Techniques

This article presents a method to predict the medical resources required to be dispatched after large-scale disasters to satisfy the demand. The historical data of past incidents (earthquakes, floods) regarding the number of victims requested emergency medical services and hospitalisation, simulation tools, web services and machine learning techniques have been combined. The authors adopted a twofold approach: a) use of web services and simulation tools to predict the potential number of victims and b) use of historical data and self-trained algorithms to “learn” from these data and provide relative predictions. Comparing actual and predicted victims needed hospitalisation showed that the proposed models can predict the medical resources required to be dispatched with acceptable errors. The results are promoting the use of electronic platforms able to coordinate an emergency medical response since these platforms can collect big heterogeneous datasets necessary to optimise the performance of the suggested algorithms.

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