EJBR2021v11i3art274 ISSN 2449-8955 European Journal of Biological Research Research Article European Journal of Biological Research 2021; 11(3): 274-282 DOI: http://dx.doi.org/10.5281/zenodo.4677470 Use of statistical analysis to monitor novel coronavirus-19 cases in Jammu and Kashmir, India Digvijay Pandey 1, Tajamul Islam 2*, Juniad A. Magray 2*, Aadil Gulzar 3, Shabir A. Zargar 2 1 Department of Technical Education, IET, Lucknow, India 2 Department of Botany, University of Kashmir, Srinagar-190006, J & K, India 3 Department of Environmental Science, University of Kashmir, Srinagar-190006, J & K, India * Corresponding author: E-mail: islamtajamul66@gmail.com; junaidmagray786@gmail.com Received: 12 March 2020; Revised submission: 30 March 2021; Accepted: 09 April 2021 https://jbrodka.com/index.php/ejbr Copyright: © The Author(s) 2020. Licensee Joanna Bródka, Poland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/) ABSTRACT: Coronavirus disease (COVID-19) has been increasing slowly and steadily in all the districts of Jammu and Kashmir, India. It is essential for the government and health management system to monitor the districts affected due to COVID-19. The main objective of this study is to ascertain and categorize the COVID- 19 affected districts into real clusters based on similarities within a cluster and differences among clusters in order to imply standard operating procedures (SOPs) policies, decisions, medical facilities, etc. could be improved for reducing the risk of infection and death and optimize the deployment of resources for preventing subsequent outbreaks. Keywords: Clusters; Coronavirus; SARS-CoV-2; Districts; Jammu and Kashmir; Similarities. 1. INTRODUCTION The ongoing outbreak of pandemics caused by a novel coronavirus was originated from the local seafood market, Wuhan of Hubei Province, China, in late December 2019 [1-3]. Within a short period, an infection spread all over the world [4-6]. On 30th January 2020, the WHO declared this outbreak as a Public Health Emergency of International Concern [7-8]. Genetically coronaviruses (CoVs) are a diverse group with positive ssRNA as genetic material, enveloped with a protein coat (capsid) [9]. Several organ systems are affected by the coronavirus like respiratory, enteric, hepatic, etc., with varying severity among humans and animals [10-12]. There are some variants in CoVs such as HCoV‐OC43, HCoV‐229E, HCoVNL63, and HCoV‐HKU1, which may cause mild respiratory illness [13-15]. In the past two decades, two nCoVs: SARS‐CoV and MERS‐CoV, have emerged, which causes more severe human respiratory infections [16, 17]. During the previous outbreak of epidemics caused by SARS‐CoV, there were cases of about 8000 people worldwide with nearly ca. 800 deaths, representing a mortality rate of around 10%. Whereas in MERS‐CoV, the fatality rate was ca. 35%, caused 334 deaths in 857 officially infected cases. SARS‐CoV is the seventh member of the family of CoVs that is zoonotic and infects humans. Fever, fatigue, and cough are the main symptoms of COVID‐19, similar to SARS‐CoV and MERS-CoV diseases. There is distinctness in the etiology and pathogenesis of these CoVs, which causes severe diseases in humans [18]. Pandey et al. Use of statistical analysis to monitor novel coronavirus-19 cases in India 275 European Journal of Biological Research 2021; 11(3): 274-282 On 16th March 2020, the first case of COVID-19 was reported in Jammu and Kashmir (J&K), which had a travel history from Saudi Arabia. On the day when the first patient was tested positive for the novel coronavirus, the state government declared the disease as an epidemic in the summer capital (Srinagar-SGR) of J&K and closed commercial and educational establishments. The district magistrate banned the assemblage of people and some other measures. On March 18, 2020, the government imposed section 144 and lockdown in the summer capital of Union Territory (J&K) due to an increase in the number of COVID-19 cases. Later on 22nd March 2020, the government of India (GoI) declared a 14-hour public curfew and ordered the closure of all educational institutions and commercial offices. Further, on 24th March 2020, the government announced the nationwide lockdown (phase I) for 21 days and after the completion of phase I lockdown, the government extended it up to 3rd May 2020. To stop the spread of coronavirus disease (COVID-19), many steps were taken by the state and UT’s government [19]. This study aims to classify and categorize the COVID-19 affected districts by clustering based on similarities in confirmed, active cured and death cases. This will help the government recognize the most or less affected Jammu and Kashmir districts to optimize the screening, lockdown, curfews, and other legal steps in severely affected districts or areas. Furthermore, the study will be beneficial in understanding the status of increment in COVID-19 cases across various districts and will insight the government, doctors and NGO’s to improve their policies which will be helpful to improve the various medical facilities such as ventilators, testing kits and masks that will ultimately reduce the spread of infection across the region. 2. MATERIAL AND METHODS 2.1. Study area Jammu and Kashmir (J&K) is a union territory of India that lies to the north of Himachal Pradesh and Punjab and to the west of Ladakh. It has a Mediterranean type of climate. As per Census 2011, J&K (including Ladakh) has a population of 1.25 Crores. The Jammu and Kashmir consists of two divisions, each comprises of ten districts: Srinagar (SGR), Anantnag (ANG), Bandipora (BD), Baramulla (BR), Ganderbal (GD), Budgam (BG), Kulgam (KL), Pulwama (PL), Kupwara (KP), Shopian (SP) are districts of Kashmir division. While as Jammu (JM), Ramban (RB), Reasi (RS), Udhampur (UD), Kathua (KT), Kishtwar (KW), Poonch (PN), Rajouri (RJ), Samba (SM), Doda (DD) forms the Jammu division. There are 217 Tehsils, 558 Niabats and 7055 Villages in the UT (including Ladakh as per census 2011). 2.2. Methodology The present study has been divided into three parts. Part I consists of data collection; part II consists of a statistical analysis of COVID-19 data set using cluster analysis (Brey-Curtis); and part III consists of analysis using radar charts to depict the number of confirmed, active, cured and death cases in each district. 2.2.1. Part I. Data collection and exploratory analysis The data of all cases (confirmed, active, cured and death cases) related to COVID-19 have been retrieved from March 16, 2020, to January 5, 2021, from the website of “COVID-19 Monitoring Dashboard maintained by the Ministry of Health and Family Welfare Government of India (GoI) [20]. Pandey et al. Use of statistical analysis to monitor novel coronavirus-19 cases in India 276 European Journal of Biological Research 2021; 11(3): 274-282 From the website of COVID-19 monitoring dashboard, data of all COVID-19 affected districts: ANG, BD, BR, BG, DD, GD, JM, KT, KW, KL, KP, PN, PL, RJ, RB, RS, SM, SP, SGR, UD have been collected. The data consist of four variables: the number of confirmed, active, cured/discharged and death cases. The total number of confirmed, active, cured and death cases during the above-mentioned period are 121786, 2684, 117211 and 1891, respectively as given in Table 1. Table 1. The total number of confirmed, active, cured and death cases from March 16, 2020, to January 5, 2021. District Confirmed cases Active cases Cured cases Death cases Anantnag (ANG) 4823 108 4632 83 Bandipora (BD) 4660 58 4542 60 Baramulla (BR) 8004 120 7712 172 Badgam (BG) 7644 108 7426 110 Doda (DD) 3398 45 3289 64 Ganderbal GD) 4500 81 4375 44 Jammu (JM) 24058 677 23024 357 Kathua (KT) 3214 42 3123 49 Kishtwar (KW) 2722 13 2688 21 Kulgam (KL) 2666 57 2556 53 Kupwara (KP) 5571 123 5357 91 Poonch (PN) 2462 41 2397 24 Pulwama (PL) 5600 151 5361 88 Rajouri (RJ) 3846 128 3664 54 Ramban (RB) 2113 22 2070 21 Reasi (RS) 1628 12 1601 15 Samba (SM) 2775 202 2534 39 Shopian (SP) 2524 107 2378 39 Srinagar (SGR) 25482 537 24495 450 Udhampur (UD) 4096 52 3987 57 Total 121786 2684 117211 1891 2.2.2. Part II. Cluster analysis (CS) Cluster analysis is one of the best data analyzing techniques by which the sample variables are clustered into groups based on their similarities within a group and dissimilarities among different groups [21, 22]. The Bray-Curtis method is the robust and most conventionally used method that does not require prior postulation and uses variance analysis to calculate similarities among different clusters [23, 24]. This study used the OriginPro software (version OriginPro 2019b-64 bit) to accomplish the cluster analysis. The data set has been scaled properly before executing the cluster analysis. 2.2.3. Part III. Analysis using a radar chart To show the increment of all the variables (cases), we plot the radar charts by analyzing the data set statistically using the PAST software (version 3). The radar charts are given in Figures 5-8. It is well known that these plots are easy to understand the values/increments of each variable. Pandey et al. Use of statistical analysis to monitor novel coronavirus-19 cases in India 277 European Journal of Biological Research 2021; 11(3): 274-282 3. RESULTS AND DISCUSSION Results obtained from the current study suggested four dendrograms (Figures 1-4) for each variable (confirmed, active, cured and death cases). For the visual representation, these dendrograms of cluster analysis calculated separately from all the variables of the COVID-19 data set. For confirmed cases, districts like ANG, BD and GD; BG and BR; SGR and JM; SM and KW; PL and KP; KT and DD; UD and RJ; SP and PN. For active cases, districts like SGR and JM; RS and KW; PN and KT; KL and BD; KP and BR; BG and ANG. For cured cases, districts like SGR and JM; BG and BR; SP and PN; SM and KL; PL and KP; KT and DD; UD and RJ; BG and ANG. While as for death cases, districts like SGR and JM; RB and KW; SP and SM; RJ and KL; UD and BD; PL and KP forming clusters based on their similarity in COVID-19 cases as shown in Figures 1-4. Figure 1. Dendrogram showing clustering of districts for confirmed cases of coronavirus disease (COVID-19). Figure 2. Dendrogram showing clustering of districts for active cases of coronavirus disease (COVID-19). Pandey et al. Use of statistical analysis to monitor novel coronavirus-19 cases in India 278 European Journal of Biological Research 2021; 11(3): 274-282 Figure 3. Dendrogram showing clustering of districts for cured cases of coronavirus disease (COVID-19). Figure 4. Dendrogram showing clustering of districts for death cases of coronavirus disease (COVID-19). ANG - Anantnag; SGR - Srinagar; BD - Bandipora; BR - Baramulla; GD - Ganderbal; BG - Budgam; KL - Kulgam; PL - Pulwama; KP - Kupwara; SP - Shopian; JM - Jammu, RB - Ramban; RS - Reasi, UD - Udhampur; KT - Kathua; KW - Kishtwar; PN - Poonch; RJ - Rajouri; SM - Samba; DD - Doda. All the districts of J&K have a high burden of confirmed cases. Districts like ANG, BR, BD, JM, KP, PL, RJ, SM, SGR and SP still have a good percentage of active cases. However, ANG, BP, BR, BD, JM, SGR, PL and KP have high rates of cured cases. While ANG, BP, BR, JM, SGR, PL and KP show a high rate of mortality. The trend shown in radar charts (Figures 5-8) for all the variables (confirmed, active, cured and death cases) related to COVID-19 were directly proportional; the districts with a high percentage of confirmed or active cases had a high number of cured as well as death cases. Pandey et al. Use of statistical analysis to monitor novel coronavirus-19 cases in India 279 European Journal of Biological Research 2021; 11(3): 274-282 -5000 0 5000 10000 15000 20000 25000 30000 48234660 8004 7644 3398 4500 24058 3214 2722 2666 5571 2462 5600 3846 2113 1628 2775 2524 25482 4096 DD BG BR BD ANG GD JM KT KW KL KP PN PL RJ RB RS SM SP SGR UD Figure 5. Radar chart showing the confirmed case of coronavirus disease (COVID-19). -100 0 100 200 300 400 500 600 700 800 108 58 120 108 45 81 677 4213 57 123 41 151 128 22 12 202 107 537 52 DD BG BR BD ANG GD JM KT KW KL KP PN PL RJ RB RS SM SP SGR UD Figure 6. Radar chart showing active cases of coronavirus disease (COVID-19). -5000 0 5000 10000 15000 20000 25000 30000 46324542 7712 7426 3289 4375 23024 3123 2688 2556 5357 2397 5361 3664 2070 1601 2534 2378 24495 3987 DD BG BRA BP ANG GB JM KT KW KG KP PN PL RJ RB RS SM SP SGR UD Figure 7. Radar chart showing cured cases of coronavirus disease (COVID-19). Pandey et al. Use of statistical analysis to monitor novel coronavirus-19 cases in India 280 European Journal of Biological Research 2021; 11(3): 274-282 -100 0 100 200 300 400 500 83 60 172 110 64 44 357 4921 53 91 24 88 54 21 15 39 39 450 57 DD BG BR BD ANG GD JM KT KW KL KP PN PL RJ RB RS SM SP SGR UD Figure 8. Radar chart showing death cases of coronavirus disease (COVID-19). The radar chart (Figure 5) showed that all the districts had a good percentage of confirmed COVID-19 cases, but districts like SGR, JM, BR and BG were in the severe zone. Similarly, in other radar charts (Figures 6-8), districts like SGR, JM, BR, BG, SM, PL and RJ showed a high number of active cases. Similarly, districts like SGR, JM, RJ, PL and KP showed severity in death cases. Few studies are based unequivocally on Indian COVID-19 data. Kumar [25] has used cluster analyzing to monitor the novel COVID-19 cases in Maharashtra, India. Das [26] has used the epidemiological model to estimate the basic reproduction number at national and some state levels. Ray et al. [27] used a predictive model for case counts in India. Considering the great diversity in every aspect of India and its vast population, it would be a much better idea to monitor the COVID-19 cases at each of the states individually. It would help to decide further plans and actions to contain the spread of the disease, which can be crucial for the COVID-19 affected states 4. CONCLUSIONS In this study, hierarchical (Brey-Curtis) cluster analysis was carried out to classify districts of Jammu and Kashmir based on similarity among COVID-19 cases to visually understand the impact of COVID-19. This technique grouped 20 different affected districts into four cluster and radar charts for each of the cases (variables). All the districts of J&K under clusters (Figure 1 & 5) were affected severely with COVID-19. The radar charts (Figures 5-8) showed the number of confirmed, active, cured and death cases, respectively. The trend in radar charts depicted a good percentage of cured cases in some districts: ANG, BP, BRA, BD, JM, SGR, PL and KP. It was also observed that the districts like SGR, JM, SM and PL have higher cases may need optimization of monitoring techniques which could help the government in making better policies and actions. Authors' Contributions: This work was carried out in collaboration between the authors. DP reviewed and edited the first draft of manuscript. TI conceptualized, designed and managed the analysis of the study. JAM wrote the first draft of manuscript. AG edited the final manuscript. SAZ managed the literature searches. All authors approved the final version of the manuscript. Conflict of Interest: The authors have no conflict of interest to declare. Pandey et al. Use of statistical analysis to monitor novel coronavirus-19 cases in India 281 European Journal of Biological Research 2021; 11(3): 274-282 Acknowledgment: We are thankful to all the colleagues who made valuable comments about the manuscript. REFERENCES 1. Huang C, Wang Y, Li X, Ren L, Zhao J, Hu Y, et al. 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