Health Research, Vol. 3, Issue 1, Mar  2019, Pages 8-22; DOI: 10.31058/j.hr.2019.31002 10.31058/j.hr.2019.31002

Animal Outbreak Analysis Using Statistical Process Control: A Different Perspective Approach for Descriptive Study from A Web-Based Dataset

, Vol. 3, Issue 1, Mar  2019, Pages 8-22.

DOI: 10.31058/j.hr.2019.31002

Mostafa Essam Ahmed Mostafa Eissa 1*

1 Microbiology and Immunology Department, Faculty of Pharmacy, Cairo University, Cairo, Egypt

Received: 30 October 2019; Accepted: 30 October 2019; Published: 2 December 2019

Abstract

Livestock’s health is crucial for countries’ economy as it serves as an important source of food. However, recent and old human history has witnessed many threats that impacted animal life with devastating consequences on human communities that depend on it. One of the most important causative agents for animal outbreaks is food-and-mouth disease (FMD) which is currently affecting many developing countries. Extensive records and database have been developed by many national and international organizations for this viral infection, In the present study an already established dataset will be analyzed using a unique perspective of applying statistical process control (SPC) tools that are commonly used in industry in monitoring, control and quantitative assessment of FMD outbreaks in selected countries in El Maghreb El Arabi with the aid of statistical software platforms. Two types of control charts (rare event and Laney attribute charts) were used in the outbreak analysis to show events-behavior and trend. Pareto, Pie, 3D diagrams and statistical analysis were used for data interpretation. Most of the outbreaks were started from Tunisia and spread to the upper coastal region in Algeria then propagated at a lower frequency to the south till altitude 31.87°N followed by Morocco adjacent to Casablanca. Most of cases were started, amplified and disseminated from the east of imaginary line 0.00°. Most of FMD incidences occurred in Algeria with late fewer incidents in Morocco with very limited geographical distribution. All cases of FMD were of serotype O diagnosed mostly by World Organization of Animal Health (OIE) with delay time between observation and report of events ranges from 3 to 123 days. Control charts showed intermittent significant excursions in assessed outbreak parameters, either in magnitude or time interval between events. Accordingly, incidents of outbreaks could be simply assessed quantitatively in timely manner using SPC tools.

Keywords

FMD, Livestock, SPC, Pareto, OIE, Control Charts, Pie, FMEA, CAPA, Outbreak

1. Introduction

Livestock’s health is crucial for countries’ economy as it serves as an important source of food for local consumption and export, in addition to the employment of manpower in the countries [1]. Pastoral communities based on livestock breeding are the backbone in many developing countries such as El Maghreb El Arabi countries [2]. Developing nations have received special attention from World Organization of Animal Health (OIE) due to devastating cattle outbreaks, notably those stemming from the highly contagious ailments such as Foot-and-Mouth Disease (FMD) virus which has ubiquitous nature of spreading and distribution including animate and inanimate vehicles [3]. The disease represents a constant threat to cattle' s health and even life in the countries of El Maghreb El Arabi, especially Morocco, Algeria and Tunisia.

Animal ranching constitutes an important part of labor activities in Algeria, Morocco and Tunisia with cultivable areas are approximately 414,422.9, 301,252.5 and 100,673.3 sq.km, respectively [4]. Despite that, the proportion of the agricultural land for the former country is very low (17.4%) - if compared with the latter two nations - yet it has pastoral territories are greater than both other nations (about 330,005.0 sq.km vs. 141,889.9 and 31,309.4 sq.km, respectively) since it possesses the largest surface area of homeland in Africa [4,5]. The Livestock wealth of these countries feeding on land plants have been exposed to a productivity-wasting disease i.e. FMD. A hazard that may require a different approach and analysis for risk mitigation and lesson learning to derive a suitable corrective/preventive action (CAPA).

Statistical process control (SPC) methodologies comprise several mathematical tools that were initially adopted for industrial processes for improvement of the manufacturing cycles, reducing wastes and solving problems [6,7,8,9]. However, later it was apparent that these statistical means could be used in other non-industrial processes including microbiological attributes of examined items, surgical site infection (SSI) in healthcare facilities and human epidemics trending [10,11,12].Thus, a study of animal outbreaks using SPC, particularly control charts may deliver useful information for decision making and quantitative risk analysis, in addition to an objective assessment for the degree of improvement or deterioration for the current state of the inspected properties or events.

Since studies performed on animal epidemics using SPC tools were sought to be limited compared with those in other fields, the present descriptive work aimed to provide a new perspective approach in cattle outbreak analyses using web-based data source. The study would cover initial data screening for unique statistical aspects followed by advanced techniques such as Shewhart charts and Pareto plot in addition to others that will be listed later. This investigation could lead to quantitative risk analysis score metrics based on the outcome of SPC work.

2. Materials and Methods

Food and Agriculture Organization (FAO) of the United Nations (UN) provides a comprehensive update for the dataset of global epidemic records [13]. FMD database for Tunisia, Algeria and Morocco from El Maghreb El Arabi were obtained [14]. Original inputs were originally obtained from Comma-Separated Values (CSV) file which was subjected to processing, filtering and stratification to be suitable for further processing using statistical programs.

2.1. Theory and Basic Principle of the Study

Define Livestock outbreak data should be arranged in chronological order to facilitate traceability of the disease spreading and mapping in the order of incidence. Statistical analysis of quantitative epidemic parameters e.g. susceptible population, morbidities, mortalities, exterminated units and slaughtered animals. Descriptive statistics will show data pattern and spreading (supported by normality, distribution fitting tests and histogram visually which will determine the nature of distribution), in addition to variations between values [15].

Outlier test will show aberrant cases with exceptional values to spot unusual epidemic waves [16]. On the other hand, Analysis of Variance (ANOVA) study will describe the significance of variations between parameters groups [17]. After mathematical shaping of FMD epidemic, correlation matrix [18] formation would be useful to elucidate the degree of association between event groups under investigation (e.g. sum at risk, cases observed, deaths, overall slaughtered animals, destroyed populations and delay time between observing and reporting illnesses) from outbreaks.

Pareto chart and Pie diagram were used in spotting the major contributing factors of outbreak incidents to prioritize actions for the major causes [19]. It should be noted that Pie graphs will be used for small comparisons (three country groups) but Pareto plot is useful in ordering of data based on frequency for 581 readings from columns of data from Excel sheet. Three-dimensional scatter diagram [20] was assigned specifically to show the pattern and magnitude of the outbreak reported pillars with geographical coordinates (altitude and longitude). This will add another dimension over the traditional map viz. number of the affected units.

Monitoring of outbreak incidents in chronological order could be done by two types of process-behavior charts. Firstly, Laney attribute control chart which is a modification of the classical C and U chart with correction factor for over-spreading or-under-spreading of data indicated by σZ value on chart [21]. Secondly, rare events G chart is used to observe days elapsed between events with probability of the outbreak occurrence indicated on figure [22,23]. Out-of-control (OOC) values would be indicated by numbered red dots on chart with special attention will be brought to excursions exceeding upper control limit (UCL) [24]. Time series plot [25] will be used as indication for gaps in time between dates of observation and recording of FMD outbreaks.

2.2. Data Processing and Analysis [12,26]

For convenience and fast analysis, statistical program platforms were used for interpretation and events monitoring. GraphPad Prism for Windows version 6.01 was used for column analysis, correlation matrix, outliers' identification and ANOVA tests [27] Distribution fitting study was conducted using XLSTAT version 2014.5.03 [28]. Minitab version 17.1.0 [29] was used for construction of Pareto diagram, 3-D scatter plot, histogram, time series plot, Pie diagram, G chart (rare event control chart) and attribute process-behavior chart (Laney modified trending for correction of data dispersion).

3. Results and Discussion

Grazing and breeding of livestock are linked to green zone which is associated with the highest human population density and continuous area of humid and dry sub humid from Tunisia to Algeria but separated from Morocco by natural geographical and climate barriers of arid and semiarid region [30].

3.1. Geographic Distribution Pattern of FMD Outbreaks

Chronologically, FMD outbreaks started from Tunisia and spread to the upper coastal region to Algeria then propagated at a lower frequency to the south till altitude 31.87°N followed by Morocco adjacent to Casablanca. Most of cases were started, amplified and disseminated from the east of imaginary line 0.00°. The chronological dissemination pattern is consistent with the natural geographical extension as could be found in Figure 1 [14] with high density clustering at north in the Mediterranean coastal region of both Tunisia and Algeria. World Organization of Animal Health (OIE) contributed by 99.8 % and the tiny fraction remaining by the local authorities as diagnosis source with 84 % of the affected animals were domestic cattle and sheep (Figure 1).

E: \文章\文章\ITS精排\ITS 20190912 精排文章\HR1019 20190831\Figures - 副本\1.png

Figure 1. Geographical distribution of FMD (upper) in North West countries of El Maghreb El Arabi (from [14] after modification) and Pareto chart of major impacted animal groups (lower).

Extended approach to the outbreaks mapping is 3-D scatter plot in Figure 2 which adds the intensity of the observed parameter of FMD - serotype O - epidemic as a third dimension. In general, Outbreaks in Tunisia and Algeria were showing greater magnitude and frequency that that in Morocco with the highest densities occurring in north and north east, respectively. Morocco outbreak incidents were very few, isolated and relatively with lower intensity than the other two countries. The congregations coordinate for FMD epidemic are shown in histograms of Figure 3.

E: \文章\文章\ITS精排\ITS 20190912 精排文章\HR1019 20190831\Figures - 副本\2.png

Figure 2. Three-dimensional relation diagram between locations coordinates and the outbreaks parameters.

E: \文章\文章\ITS精排\ITS 20190912 精排文章\HR1019 20190831\Figures - 副本\3.png

Figure 3. Distribution of estimated outbreak parameters between selected countries from database and the coordinates of highest rates of incidences.

Data distribution pattern could illustrate that there was more than one point for clustering of the outbreak events with one main coordinate for most of the cases. Pie chart in Figure 3 shows that the epidemic pillars are mainly demonstrated by Algeria with 100 % death cases came from FMD outbreaks in this country alone which might signal warning alarm for greater attention required for better epidemics control and improvement for animal health and disease prevention and containment.

3.2. Statistical Evaluation of FMD Epidemic

The assessed non-normally distributed outbreak parameters were close to log-normal or Weibull 3 pattern when studied using distribution fitting analysis, although they may not pass the test for some niches. Previous studies of epidemic diseases has demonstrated similar distribution pattern (Lotze et al., 2010; Abubakar et al., 2016; Ahmed Eissa, 2019a) [31,32,33]. Analysis of variance (ANOVA) showed that there is no significant difference between the number of units destroyed or slaughtered and the number of the original cases observed per an outbreak with Figure 4 demonstrating the significance of difference between the medians at P < 0.05 for the outbreak parameters. Dataset of the monitored outbreaks showed that the affected populations by epidemiological disease tend to be low in number with just few incidences of relatively spiking in the number of monitored parameters as could be seen in histogram in Figure 4 including lag time of reporting.

E: \文章\文章\ITS精排\ITS 20190912 精排文章\HR1019 20190831\Figures - 副本\4.png

Figure 4. Non-parametric rank graph of One-Way ANOVA using Kruskal-Wallis test (upper).Histogram for outbreak monitoring parameters distribution (lower).

Descriptive statistical analysis is shown in Table 1 for FMD epidemic from the recorded parameters dataset showing data distribution pattern numerically. Aberrant outbreaks were detected by Robust regression and Outlier removal (ROUT) (Q = 1.000%) and could be visualized graphically later in Shewhart charts. Significant correlation could be observed only in cases of the number of illnesses with the risk populations and both bear positive relation with the total slaughtered animals using non-parametric Spearman correlation matrix at 95 % confidence interval (CI).

Table 1. Descriptive statistics of FMD outbreak niches monitored in El Maghreb El Arabi.

Column Statistics

Sum at Risk

Sum Cases

Sum Deaths

Sum Destroyed

Sum Slaughtered

Latitude

Longitude

Lag time

Minimum

1.000

1.000

0.0

0.0

0.0

31.87

-8.520

3.000

25% Percentile

3.000

1.000

0.0

0.0

1.000

36.03

3.880

10.00

Median

10.00

3.000

0.0

0.0

4.000

36.30

5.295

13.00

75% Percentile

29.00

7.000

0.0

0.0

14.00

36.68

7.513

22.00

Maximum

2000

101.0

75.00

163.0

1005

37.27

11.10

123.0

10% Percentile

2.000

1.000

0.0

0.0

0.0

35.04

2.994

8.000

90% Percentile

97.00

15.00

0.0

1.000

36.50

36.78

10.11

25.00

Mean

43.51

7.139

0.3158

1.282

16.85

36.13

5.704

16.59

Std. Deviation

127.0

12.88

3.390

8.853

57.72

0.8296

2.983

10.76

Std. Error of Mean

5.269

0.5344

0.1497

0.3916

2.597

0.03442

0.1238

0.4464

Lower 95% CI of mean

33.17

6.090

0.02177

0.5124

11.75

36.06

5.461

15.71

Upper 95% CI of mean

53.86

8.189

0.6098

2.051

21.95

36.19

5.947

17.46

Lower 95% CI of median

9.000

2.000

0.0

0.0

4.000

36.24

4.887

12.00

Upper 95% CI of median

12.00

3.000

0.0

0.0

5.000

36.38

5.458

15.00

Coefficient of variation

291.89%

180.42%

1073.40%

690.65%

342.60%

2.30%

52.29%

64.88%

Skewness

8.771

4.415

21.05

13.83

12.44

-2.226

-0.6018

3.801

Kurtosis

109.5

23.47

462.9

229.9

192.3

5.809

3.403

26.70

3.3. Control Charts in Chronological Monitoring of FMD Outbreaks

Spotting of variable delays between observation and recording of the outbreak incidences could be observed as previously analyzed in statistical analysis. Time series plot in Figure 5 showed chronological coincidence of the two variables where significant delays might be spotted and expressed in Laney attribute chart of the lag time, notable those excursions that have exceeded UCL “marked red colored dots”. Those out-of-control points between CLs are indicative of other behavioral patterns such as early warnings or shifts of the mean value of the observed characteristic (outbreak). Interestingly, those red points in the lower side of the graph nearby LCL are good markers for exceptionally very low or no lag time between observation and reporting dates. It should be noted that most of outbreaks (about 78 %) detected between 01 July 2014 and 01 October 2014. On the other hand, rare event process-behavior charts are showing the probability of occurrence of FMD outbreaks (0.51 for observation and (0.50 for reporting) demonstrating high frequency of incidence which has become less frequent, especially after approximately outbreak number 523 chronologically.

3.4. Evaluation of Outbreak Parameters Using Process-Behavior Charts and Quantitative Risk Analysis

Dataset of the monitored FMD epidemic showed intermittent excursions in a few outbreaks due to aberrant number of impacted breeds for each assessed parameter. Red dots - above UCL - are indication of this special-cause variation. The points under focus are those assignable-cause outbreaks (exceptional number of affected population per a single event) above the center line of the mean. Epidemic parameter behavior could be seen in Figure 6.

E: \文章\文章\ITS精排\ITS 20190912 精排文章\HR1019 20190831\Figures - 副本\5.png

Figure 5. Observation reporting time relationship in outbreaks recorded in dataset.

E: \文章\文章\ITS精排\ITS 20190912 精排文章\HR1019 20190831\Figures - 副本\6.png

Figure 6. Laney-corrected attribute charts for trending of animal outbreak parameters.

Another investigated dimension of FMD epidemic was the risk hazard analysis. The study followed the same path provided in another research in an outbreak based on Failure Mode and Effects Analysis (FMEA) with modification [12,34]. Amendment made were using the lag time of each outbreak as detectability factor and the frequency was based on the ratio of the number of cases to the total population at risk. Severity term was the actual number of cases monitored in a solitary outbreak. Consequently, the product of the multiplication of the three components would yield the case risk factor (CRF) which could be summarized in Individual (I) chart of Figure 7. The risk magnitudes of the outbreaks are also shown by country as Pie diagram, administrations and regions by Pareto analysis.

E: \文章\文章\ITS精排\ITS 20190912 精排文章\HR1019 20190831\Figures - 副本\7.png

Figure 7. Risk analysis of outbreak cases shown by districts and locations using Pie and Pareto diagrams and trending chart.

E: \文章\文章\ITS精排\ITS 20190912 精排文章\HR1019 20190831\Figures - 副本\8.png

Figure 8. CRF analysis as frequency and magnitude by country using Pareto diagrams.

CRF based on average trend of the outbreak cases was estimated to be around 19.34. Accordingly, any risk value below and above this cut-off figure would be considered " Low" and " High" risks, respectively. Normally, 270 cases were found to be high risk from a total of 581 records - rate about 0.46 - versus 311 (0.54). Nevertheless, detailed screening would elucidate the major contributors for the incident. For instance, Figure 8 demonstrates the magnitude and frequency of the risk by country. Highest number of outbreaks either as number or magnitude was found in Algeria with high risk outweighs low risk cases. The designed risk score has an important role as a quantitative tool for comparison, assessment, prioritization and decision making for appropriate corrective and preventive actions (CAPA).

4. Conclusions

SPC tools provide valuable analysis for the objective study of epidemiological trends of the diseases. Despite significant delays in reporting several outbreaks, there is apparently no relation between incidents intensity and the lag times. However, the impact of these delays on dissemination and spreading of the epidemics should be investigated. A simple quantitative risk analysis could be adapted to measure an outbreak impact and magnitude. This metric can be used to measure overall current state and compare it with past, future and other outbreaks situations to measure the degree of deterioration or improvement. In the present case, FMD epidemic disease could spread swiftly from country to another especially in high population densities and green areas and limited by drought regions. Greater support and influence of national authorities are required by joint collaboration with international organizations for more effective control and containment of the epidemics.

Conflicts of Interest

The author declares that there is no conflict of interest regarding the publication of this article.

Copyright

© 2017 by the authors. Licensee International Technology and Science Press Limited. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

References

[1] World Food Summit 1996. TBD V3 CHAPTER 13 SECTION 1-5. Available online: http://www.fao.org/3/w2612e/w2612e13.htm (accessed on 8 February 2019).

[2] Jenet, A.; Buono, N.; Di Lello, S.; Gomarasca, M.;Heine, C.; Mason, S.; Nori, M.; Saavedra, R.; Van Troos, K. Pastoralism.The backbone of the worlds drylands,Vétérinaires Sans Frontières International (VSF-International), Brussels, Belgium, 2016.

[3] Home: OIE-World Organization for Animal Health. Available online: https://www.oie.int/index.php (accessed on 8 February 2019).

[4] Index Mundi 2017-Country Facts. Available online: https://www.indexmundi.com/ (accessed on10 February 2019).

[5] The Largest Countries in Africa by Land Area. Available online: https://www.worldatlas.com/articles/which-are-the-10-largest-countries-of-africa-by-size.html (accessed on 11 February 2019).

[6] Deming, W.E. On probability as a basis for action. The American Statistician, 1975, 29(4), 146-152.

[7] Deming, W.E.; Edwards, D.W. Quality, productivity, and competitive position (Vol. 183), Cambridge, MA, Massachusetts Institute of Technology, Center for advanced engineering study, 1982.

[8] Barlow, R.E.; Irony, T.Z. Foundations of statistical quality control. Lecture Notes-Monograph Series, 1992, 99-112. Available online: https://www.jstor.org/stable/4355628 (accessed on 13 February 2019).

[9] Bergman, B. Conceptualistic pragmatism: a framework for Bayesian analysis? IIE Transactions, 2008, 41(1), 86-93.

[10] Ahmed Eissa, M.E. Application of Control Charts in Monitoring of Surgical Site Infection Trending Records Using Statistical Software. Asian Journal of Applied Sciences, 2019, 12(2), 76-84.

[11] Ahmed Eissa, M.E. Determination of the Microbiological Quality of Feed City Water to Pharmaceutical Facility: Distribution Study and Statistical Analysis. Athens J Sci, 2017, 4, 143-160.

[12] Ahmed Eissa, M.E. Monitoring of Cryptosporidium spp. Outbreaks Using Statistical Process Control Tools and Quantitative Risk Analysis Based on NORS Long-term Trending. Microbiology Journal, 2019, 9(1), 1-7.

[13] FAO data - database - EMPRES-i. Available online: http://193.43.36.20/database?entryId=d6c36114-51f9-40fc-bec1-8eea8f74d2a0 (accessed on 18 February 2019).

[14] Temporo-spatial visualisation of disease outbreaks - Ausvet. Available online: https://www.ausvet.com.au/temporo-spatial-visualisation-of-disease-outbreaks (accessed on19 February 2019).

[15] Natural Resources Biometrics. Available online: https://courses.lumenlearning.com/suny-natural-resources-biometrics/chapter/chapter-1-descriptive-statistics-and-the-normal-distribution/ (accessed on 20 February 2019).

[16] GraphPad Prism 8 Statistics Guide - How to: Identify outliers. Available online: https://www.graphpad.com/guides/prism/8/statistics/stat_how_to_removing_outliers.htm (accessed on 21 February 2019).

[17] Understanding Analysis of Variance (ANOVA) and the F-test (The Minitab Blog). Available online: https://blog.minitab.com/blog/adventures-in-statistics-2/understanding-analysis-of-variance-anova-and-the-f-test (accessed on 22 February 2019).

[18] Zar, J.H. Biostatistical analysis, 5th ed.; Pearson Education Limited: Harlow, United Kingdom, 2013; ISBN 9781292024042.

[19] Pareto Charts - The Vital Few vs. The Trivial Many. Available online: https://www.dundas.com/resources/dundas-data-visualization-blog/pareto-charts-the-vital-few-vs-the-trivial-many (accessed on 23 February 2019).

[20] Scatter Plot / Scatter Chart: Definition, Examples, Excel/TI-83/TI-89/SPSS - Statistics How To. Available online: https://www.statisticshowto.datasciencecentral.com/probability-and-statistics/regression-analysis/scatter-plot-chart/ (accessed on 24 February 2019).

[21] Laney, D. Improved Control Charts for Attributes. Quality Engineering, 2002, 14(4), 531-537.

[22] G-chart (What is it? When is it used?) | Data analysis tools | Quality Advisor.

Available online: https://www.pqsystems.com/qualityadvisor/DataAnalysisTools/g_chart.php (accessed on 26 February 2019).

[23] Monitoring Rare Events with G Charts. Available online: https://blog.minitab.com/blog/michelle-paret/monitoring-rare-events-with-g-charts (accessed on 27 February 2019).

[24] Pyzdek, T. The Six Sigma Handbook, Revised and Expanded, 2nd ed.; McGraw-Hil: NY, USA, 2003; ISBN 9780071840538.

[25] Friedman, A. Statistics for library and information services. Rawman and Littlefield: Lanham, MD, 2016; ISBN 9781442249929.

[26] Ahmed Eissa, M.E. Long-Term Monitoring of Giardia as an Etiological Agent for Food-Borne Outbreaks in USA: A Brief Report. Open J Nutr Food Sci, 2019, 1(1), 1002.

[27] GraphPad Prism User Guide. Available online: http://cdn.graphpad.com/docs/prism/6/Prism-6-User-Guide.pdf (accessed on 28 February 2019).

[28] Gatignon, H. Statistical Analysis of Management Data.Springer US: Boston, MA, 2014, 77-154; ISBN 978-1-4614-8594-0.

[29] Ryan, B.; Cryer, J.; Joiner, B. Minitab handbook, 6th ed., Brooks/Cole Cengage Learning: Boston, USA, 2013; ISBN 978-1285175027.

[30] OSS. Algeria, Egypt, Jordan, Libya, Mauritania, Morocco and Tunisia ATLAS OF LAND CO ERMAPS. Available online: http://www.oss-online.org/sites/default/files/OSS-MENA-DELP-Atlas_En.pdf (accessed on 28 February 2019).

[31] Lotze, T.; Shmueli, G.; Yahav, I.; Kass-Hout, T.; Zhang, X. Simulating and evaluating biosurveillance datasets. Biosurveillance: Methods and Case Studies, 1st ed.; Chapman and Hall: Boca Raton, FL, 2352, 2010; ISBN 9780429135446.

[32] Abubakar, I.; Stagg, H.; Cohen, T.; Rodrigues, L. Oxford specialist handbook of infectious disease epidemiology, 1st ed. Oxford University Press:Hampshire, UK, 2016.

[33] Ahmed Eissa, M.E. Statistical Analysis Review and Lessons Learned from Recent Outbreak Trends of Highest Population Density States in USA: Massachusetts, New Jersey and Rhode Island. Journal Of Food Chemistry & Nanotechnology, 2019, 05(01), 8-19.

[34] Ahmed Eissa, M.E. The Use Failure Mode and Effects Analysis as Quantitative Risk Analysis Tool. Journal of Applied Sciences| ReDelve, 2019, 2019(2), 1-7.