scholarly journals Seeing the unseen—the iceberg phenomenon in the first months of the COVID19 pandemic

2021 ◽  
Vol 2090 (1) ◽  
pp. 012021
Author(s):  
Dragos-Victor Anghel ◽  
loan Tudor Alexandru Anghel
Keyword(s):  
Time Series ◽  
Characteristic Time ◽  
Large Population ◽  
Sir Model ◽  
Time Interval ◽  
Order Of Magnitude

Abstract We analyze the evolution of the COVID19 infections in the first months of the pandemics and show that the basic compartmental SIR model cannot explain the data, some characteristic time series being by more than an order of magnitude different from the fit function over significant parts of the documented time interval. To correct this large discrepancy, we amend the SIR model by assuming that there is a relatively large population that is infected but was not tested and confirmed. This assumption qualitatively changes the fitting possibilities of the model and, despite its simplicity, in most cases the time series can be well reproduced. The observed dynamic is only due to the transitions between two infected compartments, which are the unconfirmed infected and the confirmed infected, and the rate of closing the cases (by recovery or death) in the confirmed infected compartment. We also discuss some relevant extensions of this model, to improve the interpretation and the fitting of the data. These findings qualitatively and quantitatively evidences the “iceberg phenomenon” in epistemology.

scholarly journals Understanding the COVID19 infection curves -- finding the right numbers

2020 ◽  
Author(s):  
Dragos-Victor Anghel ◽  
Ioan Tudor Alexandru Anghel
Keyword(s):  
Time Series ◽  
Characteristic Time ◽  
Large Population ◽  
Sir Model ◽  
Time Interval ◽  
Susceptible Population ◽  
Infected People ◽  
Order Of Magnitude ◽  
The One ◽  
The Right

Abstract We analyze the evolution of the COVID19 pandemics and show that the basic compartmental SIR model cannot explain the data, some characteristic time series being by more than an order of magnitude different from the fit function over significant parts of the documented time interval. To correct this large discrepancy, we amend the SIR model by assuming that there is a relatively large population that was infected but was not tested and confirmed. This assumption qualitatively changes the fitting possibilities of the model and, despite its simplicity, in most cases, all the time series can be quite well reproduced. Nevertheless, in some cases (i.e., countries or regions) the estimated susceptible population decreases too fast. In such a case, the observed dynamic is only due to the transitions between the two infected compartments--the confirmed infected and the unconfirmed infected--and the rate of closing the cases (by recovery or death) in the confirmed infected compartment. Our analysis proves that the number of infected people is significantly larger than the one recorded and we provide a method to estimate it. We also discuss some relevant extensions of this model, to improve the interpretation and the fitting of the data.

scholarly journals Sensitivity Analysis of Sentinel-1 Backscatter to Oil Palm Plantations at Pluriannual Scale: A Case Study in Gabon, Africa

2021 ◽  
Vol 13 (11) ◽  
pp. 2075
Author(s):  
J. David Ballester-Berman ◽  
Maria Rastoll-Gimenez
Keyword(s):  
Time Series ◽  
Sensitivity Analysis ◽  
Oil Palm ◽  
Time Interval ◽  
Potential Candidate ◽  
Initial Growth ◽  
Natural Forests ◽  
Wet Season ◽  
Radar Signatures ◽  
The Times

The present paper focuses on a sensitivity analysis of Sentinel-1 backscattering signatures from oil palm canopies cultivated in Gabon, Africa. We employed one Sentinel-1 image per year during the 2015–2021 period creating two separated time series for both the wet and dry seasons. The first images were almost simultaneously acquired to the initial growth stage of oil palm plants. The VH and VV backscattering signatures were analysed in terms of their corresponding statistics for each date and compared to the ones corresponding to tropical forests. The times series for the wet season showed that, in a time interval of 2–3 years after oil palm plantation, the VV/VH ratio in oil palm parcels increases above the one for forests. Backscattering and VV/VH ratio time series for the dry season exhibit similar patterns as for the wet season but with a more stable behaviour. The separability of oil palm and forest classes was also quantitatively addressed by means of the Jeffries–Matusita distance, which seems to point to the C-band VV/VH ratio as a potential candidate for discrimination between oil palms and natural forests, although further analysis must still be carried out. In addition, issues related to the effect of the number of samples in this particular scenario were also analysed. Overall, the outcomes presented here can contribute to the understanding of the radar signatures from this scenario and to potentially improve the accuracy of mapping techniques for this type of ecosystems by using remote sensing. Nevertheless, further research is still to be done as no classification method was performed due to the lack of the required geocoded reference map. In particular, a statistical assessment of the radar signatures should be carried out to statistically characterise the observed trends.

scholarly journals WDSchain: A Toolbox for Enhancing the Security Using Blockchain Technology in Water Distribution System

Water ◽  
2021 ◽  
Vol 13 (14) ◽  
pp. 1944
Author(s):  
Haitham H. Mahmoud ◽  
Wenyan Wu ◽  
Yonghao Wang
Keyword(s):  
Time Series ◽  
Distribution Systems ◽  
Water Distribution ◽  
Time Series Data ◽  
Series Data ◽  
Process Time ◽  
Time Interval ◽  
Security Level ◽  
Data Validation ◽  
Simulated Time

This work develops a toolbox called WDSchain on MATLAB that can simulate blockchain on water distribution systems (WDS). WDSchain can import data from Excel and EPANET water modelling software. It extends the EPANET to enable simulation blockchain of the hydraulic data at any intended nodes. Using WDSchain will strengthen network automation and the security in WDS. WDSchain can process time-series data with two simulation modes: (1) static blockchain, which takes a snapshot of one-time interval data of all nodes in WDS as input and output into chained blocks at a time, and (2) dynamic blockchain, which takes all simulated time-series data of all the nodes as input and establishes chained blocks at the simulated time. Five consensus mechanisms are developed in WDSchain to provide data at different security levels using PoW, PoT, PoV, PoA, and PoAuth. Five different sizes of WDS are simulated in WDSchain for performance evaluation. The results show that a trade-off is needed between the system complexity and security level for data validation. The WDSchain provides a methodology to further explore the data validation using Blockchain to WDS. The limitations of WDSchain do not consider selection of blockchain nodes and broadcasting delay compared to commercial blockchain platforms.

ANALYSIS OF TRENDS OF TOTAL EVAPORATION FLUXES (FOR 2003-2017) ACCORDING TO THE DATA OF PRODUCT MOD16A2 FOR THE TERRITORY OF THE MARKSOVSKY DISTRICT OF THE SARATOV REGION

2021 ◽  
pp. 16-25
Author(s):  
O. S. ERMOLAEVA ◽  
◽  
A. M. ZEYLIGER
Keyword(s):  
Time Series ◽  
Irrigation System ◽  
Growing Season ◽  
Half Period ◽  
Anthropogenic Factors ◽  
Time Interval ◽  
Total Evaporation ◽  
Saratov Region ◽  
Growing Seasons ◽  
Dominant Part

This paper presents the results of calculations of areal trends of total evaporation ETa fl uxes for the growing periods of 2003-2017 in the territory of the Marksovsky district of the Saratov region. Raster layers formed for the territory with a 500 m resolution of the Eta8 (Eta 8-day averaging) for each year of the investigated time interval were obtained from tiles sets h20v03 of the product MOD16A2 for the period from May 25 to September 2 of the corresponding year. As a result, the 19830 time series of total evaporation fl uxes for the ETaw growing seasons of the 15-year study period were drawn up for the Marksovsky district. The obtained time series of geodata of the actual evapotranspiration for the growing season ETaw for each of the 15 studied years were used for the spatial analysis of ETaw trends. For the analysis, the method of nonparametric Mann-Kendal statistics was used. It revealed the presence of 2 half-periods with diametrically opposite trends in the dominant part of time series. The fi rst half-period found out negative values (downward) trends and falls on 2003-2010, the second half-period showed positive (upward) trends for 2010-2017. The presented results of the spatial distribution of both trends indicate the presence of an infl uence on ETaw both distance from the bank of R. Volga and anthropogenic factors. Hypotheses for additional analysis are proposed. For the visual deciphering of the places of abnormal values of trends velocities of the both half-periods there were used space photos of high resolution. As a result it was marked that the location of these anomalies corresponded to the location of pivot sprinklers in the territory of the Privolzhskoj irrigation system.

scholarly journals Investigation of correlation of the variations in land subsidence (detected by continuous GPS measurements) and methodological data in the surrounding areas of Lake Urmia

2012 ◽  
Vol 19 (6) ◽  
pp. 675-683 ◽  
Author(s):  
K. Moghtased-Azar ◽  
A. Mirzaei ◽  
H. R. Nankali ◽  
F. Tavakoli
Keyword(s):  
Time Series ◽  
Land Subsidence ◽  
Salt Lake ◽  
Linear Trend ◽  
Groundwater Resources ◽  
Time Interval ◽  
Gps Measurements ◽  
Lake Urmia ◽  
North West ◽  
Continuous Gps

Abstract. Lake Urmia, a salt lake in the north-west of Iran, plays a valuable role in the environment, wildlife and economy of Iran and the region, but now faces great challenges for survival. The Lake is in immediate and great danger and is rapidly going to become barren desert. As a result, the increasing demands upon groundwater resources due to expanding metropolitan and agricultural areas are a serious challenge in the surrounding regions of Lake Urmia. The continuous GPS measurements around the lake illustrate significant subsidence rate between 2005 and 2009. The objective of this study was to detect and specify the non-linear correlation of land subsidence and temperature activities in the region from 2005 to 2009. For this purpose, the cross wavelet transform (XWT) was carried out between the two types of time series, namely vertical components of GPS measurements and daily temperature time series. The significant common patterns are illustrated in the high period bands from 180–218 days band (~6–7 months) from September 2007 to February 2009. Consequently, the satellite altimetry data confirmed that the maximum rate of linear trend of water variation in the lake from 2005 to 2009, is associated with time interval from September 2007 to February 2009. This event was detected by XWT as a critical interval to be holding the strong correlation between the land subsidence phenomena and surface temperature. Eventually the analysis can be used for modeling and prediction purposes and probably stave off the damage from subsidence phenomena.

scholarly journals COMPARISON BETWEEN THE RESULTS OF LITTORAL-DRIFT COMPUTATIONS AND CUBATURE OF DEPOSITS IN A DREDGED CHANNEL

1974 ◽  
Vol 1 (14) ◽  
pp. 42
Author(s):  
V.F. Motta ◽  
J.V. Bandeira
Keyword(s):  
Time Interval ◽  
Littoral Drift ◽  
Monthly Basis ◽  
Wave Characteristics ◽  
Order Of Magnitude ◽  
Annual Volume ◽  
Offshore Oil ◽  
Remarkable Agreement

The total annual volume of littoral drift on either side of the mouth of Sergipe estuary, in the Northeast of Brazil, has been de_ termined by applying Caldwell's, Castanho's and Bijker's methods to the wave characteristics that had been recorded at a twenty-metre depth of water, over a whole year, for the design of an offshore oil terminal. The three computation methods yielded the same order of maj> nitude which was found to amount to about 80000Om^/year. The dominant drift is s outhwes tward, and its predicted amount is 660000m-*/year. It was also found that although the three methods lead to total re suits of the same order of magnitude, they do not agree as to the vari^ ation of littoral drift over the year for the s ame waves. An eight-metre deep shipping channe 1 has been dredgedaccross the bar. The channel was surveyed in December 1971, August and Decem ber 1972, and a cubature of the deposits was made after the littoraldrift computations had been carried out. As the latter had been per formed on a monthly basis, a comparison became possible between pre dieted and actual volumes of deposits for the same lengths of time. The predicted volumes for the whole year were found to be from 34 to 46% greater than the actual results. However, for the time interval August-December 1972 a remarkable agreement was found be^ tween predicted and actual results.

scholarly journals Analysis of Dynamical Behavior for Epidemic Disease COVID-19 with Application

2021 ◽  
Vol 12 (4) ◽  
pp. 568-577
Author(s):  
Dr. Maysoon M. Aziz, Et. al.
Keyword(s):  
Time Series ◽  
Linear System ◽  
Power Spectrum ◽  
Dynamical Behavior ◽  
Simulated Data ◽  
Real Data ◽  
Sir Model ◽  
Epidemic Disease ◽  
The World ◽  
Non Linear System

In this paper, we will use the differential equations of the SIR model as a non-linear system, by using the Runge-Kutta numerical method to calculate simulated values for known epidemiological diseases related to the time series including the epidemic disease COVID-19, to obtain hypothetical results and compare them with the dailyreal statisticals of the disease for counties of the world and to know the behavior of this disease through mathematical applications, in terms of stability as well as chaos in many applied methods. The simulated data was obtained by using Matlab programms, and compared between real data and simulated datd were well compatible and with a degree of closeness. we took the data for Italy as an application.  The results shows that this disease is unstable, dissipative and chaotic, and the Kcorr of it equal (0.9621), ,also the power spectrum system was used as an indicator to clarify the chaos of the disease, these proves that it is a spread,outbreaks,chaotic and epidemic disease .

scholarly journals Time series analysis of radiant heat using 75 hours VIIRS satellite day and night band nightfire data

2020 ◽  
Vol 2020 (1) ◽  
pp. 98-117
Author(s):  
Jyoti U. Devkota
Keyword(s):  
Time Series ◽  
Infrared Imaging ◽  
Time Series Data ◽  
Spline Interpolation ◽  
Radiant Heat ◽  
Series Data ◽  
Time Interval ◽  
Industrial Sites ◽  
Steel Mills ◽  
Gas Flares

Abstract The nightfires illuminated on the earth surface are caught by the satellite. These are emitted by various sources such as gas flares, biomass burning, volcanoes, and industrial sites such as steel mills. Amount of nightfires in an area is a proxy indicator of fuel consumption and CO2 emission. In this paper the behavior of radiant heat (RH) data produced by nightfire is minutely analyzed over a period of 75 hour; the geographical coordinates of energy sources generating these values are not considered. Visible Infrared Imaging Radiometer Suite Day/Night Band (VIIRS DNB) satellite earth observation nightfire data were used. These 75 hours and 28252 observations time series RH (unit W) data is from 2 September 2018 to 6 September 2018. The dynamics of change in the overall behavior these data and with respect to time and irrespective of its geographical occurrence is studied and presented here. Different statistical methodologies are also used to identify hidden groups and patterns which are not obvious by remote sensing. Underlying groups and clusters are formed using Cluster Analysis and Discriminant Analysis. The behavior of RH for three consecutive days is studied with the technique Analysis of Variance. Cubic Spline Interpolation and merging has been done to create a time series data occurring at equal minute time interval. The time series data is decomposed to study the effect of various components. The behavior of this data is also analyzed in frequency domain by study of period, amplitude and the spectrum.

scholarly journals Monitoring High Throughput Distributed System using Statistical Data Analysis

2020 ◽  
Vol 8 (6) ◽  
pp. 4590-4596
Keyword(s):  
Time Series ◽  
Statistical Analysis ◽  
Distributed System ◽  
High Throughput ◽  
Statistical Data ◽  
Time Interval ◽  
Statistical Data Analysis ◽  
Cloud Data ◽  
Data Pipeline ◽  
Cloud Data Centers

Monitoring high throughput distributed system by using a statistical analysis of the “historical time series” of an Instrumentation Data”. “The Pipeline has been made to process the information which can be otherwise called data pipeline, is a lot of information handling components associated in arrangement, where yield of one component is the contribution of the next one”. Several codes are giving different visualization for statistical analysis of data. “Network and Cloud Data Centers” generate a lot of data every second; this data can be gathered as period arrangement information. A timeseries is a grouping taken at progressive similarly dispersed focuses in time that implies at a particular time interval to a particular time, the estimations of explicit information that was taken is known as information of a time-series. “This time-series information can be gathered utilizing framework measurements like CPU, Memory, and Disk utilization”. The TICK and ELK Stack is abbreviation for a foundation of open source instruments worked “to make collection, storage, graphing, and alerting” on time arrangement data incredibly easy. As an information collector, using Telegraf, “for storing and analyzing” information and the time-series database InfluxDB and Elasticsearch. For plotting and visualizing used Grafana and Kibana. Watchman is utilized for alert refinement and once system metrics usage exceeds the specified threshold, the alert is generated and sends it to the Telegram.

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