TX_1~AT/TX_2~AT International Review of Management and Marketing ISSN: 2146-4405 available at http: www.econjournals.com International Review of Management and Marketing, 2020, 10(4), 161-169. International Review of Management and Marketing | Vol 10 • Issue 4 • 2020 161 A Comparative Study on Corona Virus Pandemic – What do Figures Indicate? Annamalai Alagappan1, Sampath Kumar Venkatachary2*, Leo John Baptist3, Ravi Samikannu4, Jagdish Prasad5, Anitha Immaculate6 1Department of Software Engineering, Faculty of Computing, Botho University, Botswana, 2 Grant Thornton, Aumen Park, Fair Grounds, Gaborone, Botswana. 3Department of Network and Infrastructure Management, Faculty of Computing, Botho University, Botswana, 4Department of Electrical, Computers and Telecommunication Engineering, Botswana International University of Science and Technology, Palaype, Botswana, 5Amity School of Applied Sciences, Amity University Rajasthan, Jaipur, Rajasthan, India, 6Department of Chemistry, Holy Cross College (Autonomous), Trichy, Tamil Nadu, India. *Email: sampathkumaris123@gmail.com Received: 01 May 2020 Accepted: 20 June 2020 DOI: https://doi.org/10.32479/irmm.10320 ABSTRACT Coronavirus disease 2019 (COVID 19) is recognized as one of the most significant outbreak in recent times given the spread across the nations. It has affected over 185 countries across the globe and is still expanding significantly. This paper aims to compare the data on two counts and a detailed descriptive analysis is presented in the paper. Given the threat level and the classification of the disease as a pandemic, an attempt is made to analyse the data based on a linear regression estimation and predict its evolution. The statistical results indicate that the death and the recovery rate are influenced substantially by the facilities available in the form of hospital beds, patient-physician and nurse ratio. Keywords: Corona Virus, Statistical, Severe Acute Respiratory Syndrome, Middle East Respiratory Syndrome JEL Classifications: I0; I1; I2; C0 1. INTRODUCTION The impact of COVID-19 has been a problem for most of the nations. World Health Organisation was compelled to declare COVID-19 as a Pandemic on 11 March 2020 (WHO, 2020) due to its rapid spread across nations. The apparent impacts in Asia and Africa are probably going to be more than the rest of the world. The reasons are due to the lack of infrastructure development in critical fields like health. The vulnerability to communicable diseases is magnified manifold due to numerous factors that influence the countries like the concentration of populations, conflicts, and so on. COVID-19 pandemic likewise has uncovered the vulnerability of health infrastructures across the globe. It not only has left the most of the countries unprepared to respond to the pandemic, but also has tested some of the worlds best healthcare systems like Italy, Switzerland, the United States and so forth. As the pandemic advances, it is probably going to test many concerning nations like Romania, African nations like Nigeria, South Africa, Botswana, South American Nations like Brazil, Peru, Asian nations like Pakistan, Bangladesh, Sri Lanka, India and so on to give some examples which are viewed as high or specific hazard (Gherghel and Bulai, 2020). This hazard can be ascribed numerous variables like the postcolonialism or socialism, delay in executing changes, defilement, the proportion of patient to a specialist, government shakiness, economies, and so on (Gherghel and Bulai, 2020; Instabilitate guvernamentală cronică, 2017). Although scientists have so far identified only six coronaviruses in the coronavirus family, it is believed that only two viruses SARS and MERS have been known to transmit between human population. Apart from the above two, the newly discovered COVID-19 is also now designated as a communicable disease This Journal is licensed under a Creative Commons Attribution 4.0 International License Alagappan, et al.: A Comparative Study on Corona Virus Pandemic – What do Figures Indicate International Review of Management and Marketing | Vol 10 • Issue 4 • 2020162 and is of the highest concern among the scientific community. COVID-19 is also known for its severity in the form of causing severe pneumonia in people. Though it affects all kinds of people irrespective of race, it is known to be severe on people with the weaker immune system such as diabetic, HIV positive individuals especially among the older people (Bradburne et al., 1967; Bradburne and Somerset, 1972; Monto, 1974; Patrick et al., 2006; Lieberman et al., 2010; Nickbakhsh et al., 2016; Jiang, 2020) With WHO providing strict guidelines on various aspects of social life like prohibiting mass gatherings, social distancing as a measure of containment and affected countries strictly enforcing, it remains to be seen, if the countermeasures have been effective. The major reason for the spread has been the travellers as careers in many countries as in the case of Iran, Italy, Spain and many other countries (Pullano et al., 2020; Arab-Mazar et al., 2020; Gherghel and Bulai 2020; Biscayart et al., 2020; Rodriguez- Morales, et al., 2020). Against this backdrop, this paper aims to study and provide an insight into the various influential factors. Section 2 reviews the pandemic. Section 3 discusses data modelling, while section 4 provides the statistical analysis. Section 5 discusses the result with section 6 concluding the paper. 2. OVERVIEW OF PANDEMICS COVID-19 or Coronavirus 2019 first came to light in the city of Wuhan on 12 December as reported by the Wuhan Health Corporation, Hubei Province in the Peoples Republic of China (Biscayart et al., 2020). Though initial traces were narrowed down to the Wuhan wet market (Lu et al., 2020; Zhou et al., 2020; Biscayart et al., 2020) the scientific community is now revisiting to ascertain its primary source. What was initially a problem to the Wuhan city, had become a global problem. Thanks to the Chinese new year, during which time, most Chinese people travel to China to celebrate. With many returning or travelling to countries, after the festival, the world witnessed the emergence of the latest outbreak of zoonotic pathogen in the form of international transmission with China issuing a new confirmation on the human to human transmission. (Rodriguez-Morales, et al., 2020a; Rodriguez-Morales, et al., 2020b). By the time the Chinese government had enforced a clampdown on the towns, the disease had spread significantly to the other areas (Eder et al., 2020). Wuhan, which was the initial epicentre after the outbreak, slowly became insignificant with the USA now topping the number of infections outside of China. With the total cases across the globe now nearing 2.2 million, this pandemic is here to stay for the next couple of months if not years (Johns Hopkins School of Public Health, 2020). 2.1. Impacts of COVID-19 Tables 1-4 provides an insight into SARS, MERS and COVID-19 cases. As against the total global cases tested as reported accounted for COVID-19 stood at 2399849 as on April 9, 2020. When comparing it with the data as on July 20, 2020, the number of cases had increased to 14741412. While the recovery percentage was 25% as on April 9, 2020, the recovery percentage as on Table 1: SARS 2003 cases (WHO, 2004) SARS - 2003 Total registered cases Total cases Recovered cases Total death Total recovery percentage Total death percentage 8096 7352 744 90.81027668 9.18972332 Table 2: MERS case update as of January 2020 (WHO, 2020) MERS Total registered cases Total cases Recovered cases Total death Total recovery percentage Total death percentage 2519 1653 866 65.62127829 34.37872171 Table 3: Abstract of total COVID-19 cases as on April 19, 2020 (WHO, 2020) COVID-19 Total registered cases Total cases Recovered cases Total death Total recovery percentage Total death percentage 2399849 615674 164939 25.65469744 6.872890753 Total active cases Active cases Serious cases Percentage of serious cases 1619236 54215 3.348183958 Cases with results Total cases Recovered Death Recovery percentage Death percentage against outcome 780613 615674 164939 78.87057992 21.12942008 July 20, 2020 stood at 60% indicating that the recovery rate has doubled. Similarly, when comparing the death percentage, which stood at 7% as on April 9, 2020, it has substantially come down to 4% as on July 20, 2020. The outcome of the total cases stood COVID-19 as on July 20, 2020 Total registered cases Total cases Recovered cases Total death Total recovery percentage Total death percentage 14741412 8792382 610747 59.64409651 4.143069877 Total active cases Active cases Serious cases Percentage of serious cases 5338283 59701 1.118355846 Cases with results Total Cases Recovered Death Recovery percentage Death percentage against outcome 9403129 8792382 610747 93.50485354 6.495146456 Alagappan, et al.: A Comparative Study on Corona Virus Pandemic – What do Figures Indicate International Review of Management and Marketing | Vol 10 • Issue 4 • 2020 163 at 780613 cases as recovered with a recovery percentage of 79% as on April 9, 2020 and 93% as on July 20, 2020. The overall mortality rate thus has been lower at 6.4% (as on July 20, 2020 in the case of COVID-19 when comparing it with SARS (9%) and MERS (34%). (Bradburne et al., 1967; Bradburne and Somerset, 1972; Monto, 1974; Patrick et al., 2006; Lieberman et al., 2010; Nickbakhsh et al., 2016; Jiang, 2020). The world has encountered more than 20 scourges and pandemics from measles, Zika to Ebola, SARS, MERS and the current COVID-19. The present pandemic has caused largescale interruptions with numerous nations enforcing social standards like frequent handwashing, using masks, social distancing, school closures, lockdowns etc. As we witness the nations react and enforce aggressive policies as means to flatten the spread curve and improve the population immunity, which is a known method of controlling the pandemic spread, it is seen as a hindrance among the general public. Adding to the problem is the community’s approach towards health workers and the affected. The contemptuous behaviour of some people in the community is causing more trauma to the health workers who are overwhelmed by the crisis. This insolent behaviour is likely to mentally impact the health workers and the infected people at large. The likeliness of neurological disorders during a lockdown could be as high as 3-4% as noticed after Boston bombings (Guerriero, 2014). As governments across the globe embark on isolation to protect its people, these measures may be acceptable during the instance such as terrorist attacks, natural disasters etc. Still, they could prove otherwise in the current scenario where the mental stress is already at stake (Fagan, 2003). There is also a need for awareness among the health care workers on the patients neurological and psychological condition of the patient testing positive for COVID 19 (Jeong et al., 2016) (Torales, 2020). The other impact is the isolation and disconnection from societal care with some shocking consequences as the dying are “barriered” from the loved ones. The pandemic so far has been disproportionately affecting all segments. The epidemic also led to widespread panic across the communities, including panic buying, stocking up, and so on. Though it is too early to comment on the social impacts, it is noticeable across the communities. The impact of social media on COVID 19 pandemic has also contributed enormously negatively and impacted the public and the health workers alike. This is primarily due to the incomplete information dissemination from the government. With information flooding the social media groups in the form genuine, misleading, and fake messages, the stress levels and the anxiety levels, unjustified fear among the public, in general, is high. This flood of misleading information could lead to discrimination, stigmatisation, which in turn could lead to other problems in the form of social bullying etc. (Purgato, 2018; Mowbray, 2020). It is estimated that close to 1.5 billion children are affected by the pandemic due to the closure of schools (WHO, 2020). According to the world bank, the resultant impact of COVID-19 in the world low and middle-income countries could have far-reaching implications for millions of people who live in poverty or have only emerged from it. It is estimated that east Asian countries and Africa could be the worst impacted and may lose half of GDP with food, drug, unemployment and investment problems even before the countries face the full wrath of the disease (World Bank, 2020; Sullivan and Chalkidou, 2020). 3. DATA MODELING AND METHODOLOGY Data selection (global reported, death and recovered cases) for analyzing the impact were collected from the John Hopkins Table 4: Total reported, death and recovery for COVID-19 for the top 25 countries as on April 4, 2020 Country/region Confirmed cases 9 APR Deaths 9 APR Recovered 9 APR Beds: Patients Physicians: Patients Nurses and Midwives: Patients Death percentage Recovery percentage United States 461437 16478 25410 1338.1 1197.34 3945.29 3.57 5.51 Spain 153222 15447 52165 459.67 623.48 847.42 10.08 34.05 Italy 143626 18279 28470 488.33 587.88 842.98 12.73 19.82 France 118781 12228 23413 772.08 384.24 1150.83 10.29 19.71 Germany 118181 2607 52407 980.90 497.39 1559.60 2.21 44.34 China 82883 3339 77679 348.11 147.99 191.24 4.03 93.72 Iran, Islamic Rep. 66220 4110 32309 99.33 75.49 123.83 6.21 48.79 United Kingdom 65872 7111 359 184.44 184.82 545.93 10.80 0.54 Turkey 42282 908 2142 114.16 74.44 111.22 2.15 5.07 Belgium 24983 2523 5164 154.89 83.03 277.34 10.10 20.67 Switzerland 24051 948 10600 113.04 101.89 415.67 3.94 44.07 Netherlands 21903 2403 278 102.94 76.81 243.22 10.97 1.27 Canada 20654 503 5162 55.77 53.91 204.59 2.44 24.99 Brazil 18092 950 173 39.80 38.90 175.64 5.25 0.96 Portugal 13956 409 205 47.45 46.55 88.93 2.93 1.47 Austria 13244 295 5240 100.65 68.13 108.31 2.23 39.57 Korea, South 10423 204 6973 119.86 24.66 72.68 1.96 66.90 Russian federation 10131 76 698 83.07 40.66 87.34 0.75 6.89 Israel 9968 86 1011 30.90 32.07 51.88 0.86 10.14 Sweden 9141 793 205 23.77 49.36 105.52 8.68 2.24 India 6725 226 620 4.71 5.23 14.17 3.36 9.22 Ireland 6574 263 25 18.41 20.29 93.97 4.00 0.38 Norway 6211 108 32 24.22 28.78 112.57 1.74 0.52 Australia 6108 51 1472 23.21 21.91 77.33 0.83 24.10 Alagappan, et al.: A Comparative Study on Corona Virus Pandemic – What do Figures Indicate International Review of Management and Marketing | Vol 10 • Issue 4 • 2020164 School of public health data portal (Johns Hopkins School of Public Health, 2020). The data for the Physicians and nurses were collected from the World Bank data. The collected data was then segregated for data analysis using excel to build a time series data. Of the data for 185 countries were compared against the data of physicians and nurses/midwives and were selected for performing an analysis using COX Regression model. 4. MODEL COMPARISON, ANALYSIS AND RESULTS Figure 1 shows the increasing prevalence of difference between each previous day of the global cases reported to WHO as captured by John Hopkins school. That is the difference is computed as follows ∑ (Yc–Yp) Equation 1 The trend showed a non-constant increase in the total number of reported cases for each of the 185 countries. A brief descriptive analysis of the data was performed to analyse the pattern of newly reported or confirmed cases. Figures 1-4 shows the global confirmed cases, death rate and recovery rate as of April 9, 2020 and July 20, 2020. As seen in Figures 1-3, and Tables 4 and 5 the US has registered the highest number of cases with 461437 with a recovery per cent of 5.51% as against the death rate of 3.57% as on April 9, 2020. While comparing the same as on July 20, 2020, the recovery rate had improved significantly. It is interesting to note that death to recovery rate stood at 64% as on April 9, 2020, indicating a positive recovery rate. While Italy had the highest death rate of 12%, Spain, the Netherlands, United Kingdom, Belgium all had a near similar death rate of 10-11% indicating clearly that the overall death percentage rate is likely to be higher for most of the countries as on April 9, 2020. The same countries witnessed a significant improvement on recoveries as on July 20, 2020 and the reported cases had fallen drastically. The reason that could be attributed to initial high death rates in these countries perhaps could be attributed to the elderly population based on the regions. However, when analyzing death versus recovery percentage, the United Kingdom had the least recovery per cent of <1% as on April 9, 2020 and on par with the UK were Ireland, Brazil, Portugal, Sweden, with marginal differences. China and South Korea are the only two countries that had reported a positive recovery rate of over 60% given the trend. While performing analysis on data for April 9, 2020, for 185 countries, it can be seen that the recovery rate largely depends on various factors. It is worthwhile to note that South Korea (3%), Australia (3.46%), China (4.30%), Germany (5%), Chile (4.47%) have been effective in controlling the spread of the disease indicating that the social distancing, lockdown methods have been effective. That is technically, the countries have been effective in curtaining the movement of the people across the region. When comparing the data for July 20, 2020 it can be seen that most countries that were in the top 25 countries as on April 9, 2020 had significantly dropped in ranking, while countries like India, Brazil, Russia, South Africa which had witnessed smaller numbers reported significant raise in the number of cases and moved up the ranking order standing in the top 5 countries. This Figure 1: Top 25 countries – reported cases, recoveries, deaths as on July 20, 2020 0 5,00,000 10,00,000 15,00,000 20,00,000 25,00,000 30,00,000 35,00,000 40,00,000 45,00,000 U ni te d S ta te s B ra zi l In di a R us si an F ed er at io n S ou th A fri ca P er u M ex ic o C hi le S pa in U ni te d K in gd om Ir an , I sl am ic R ep . P ak is ta n S au di A ra bi a Ita ly Tu rk ey B an gl ad es h G er m an y C ol om bi a Fr an ce A rg en tin a C an ad a Q at ar Ir aq In do ne si a E gy pt , A ra b R ep . 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 Total Cases Total Deaths Total Recovered Alagappan, et al.: A Comparative Study on Corona Virus Pandemic – What do Figures Indicate International Review of Management and Marketing | Vol 10 • Issue 4 • 2020 165 16 47 8 15 44 7 18 27 9 12 22 8 26 07 33 39 41 10 71 11 90 8 25 23 94 8 24 03 50 3 95 0 40 9 29 5 20 4 76 86 79 3 22 6 26 3 10 8 51 39 45 .2 9 84 7. 42 84 2. 98 11 50 .8 3 15 59 .6 0 19 1. 24 12 3. 83 54 5. 93 11 1. 22 27 7. 34 41 5. 67 24 3. 22 20 4. 59 17 5. 64 88 .9 3 10 8. 31 72 .6 8 87 .3 4 51 .8 8 10 5. 52 14 .1 7 93 .9 7 11 2. 57 77 .3 3 0 2000 4000 6000 8000 10000 12000 14000 16000 18000 20000 Deaths 9 APR Beds : Pa�ents Physicians : Pa�ents Nurses & Midwives : Pa�ents Figure 3: Top 25 Countries - death, patient-bed, nurse perhaps could be attributed to number of initial cases recorded and reported and influence of various other factors like social distancing, testing ratio, lockdown effects etc. that influence the spread of the disease. Figure 2: Top 25 countries - reported Cases from January 22, 2020 to April 9, 2020 0 200000 400000 600000 800000 1000000 1200000 1400000 1600000 Australia Norway Ireland India Sweden Israel Russia Korea, South Austria Portugal Brazil Canada Netherlands Switzerland Belgium Turkey United Kingdom Iran Alagappan, et al.: A Comparative Study on Corona Virus Pandemic – What do Figures Indicate International Review of Management and Marketing | Vol 10 • Issue 4 • 2020166 The comparative results for April 9, 2020 and July 20, 2020 is tabled in Table 6. From Table 6, it can be analysed that almost all Nations have reported significant recovery percentage and substantial decrease in death percentage, indicating that the nations are ensuring sufficient effective steps and mechanisms to bring the virus under control. The rise in recovery percentage also reflects in ensuring herd immunity. 4.1. Estimating and Analysis Using Linear Regression and Bivariate Correlations Based on the observations, the data for April 9, 2020 was fed into the SPSS package for analysing the samples. The analysis provides a deep insight into the various aspects of the countries. The regression analysis indicates that there is a positive correlation when assessing the recovery and death rates reported in each of the countries. 4.2. Regression – Does Number of Beds Influence the Death Rate and Recovery Rate? To ascertain if the beds to patients ratio influenced the recovery and the death rate in the pandemic COVID-19, an linear and bivariate analysis were carried out in SPSS. The data indicated that 70% of both recovery and death rate had a significant relationship to the number of beds in the hospital for all nations. The regression equation for the recovery and death is as follows. Equations Recovery = y = 0.005x + 5.084 Death = y = 0.036x + 5.084 Figure 4: Top 25 Countries - recovered, patient-bed, nurse 25 41 0 52 16 5 28 47 0 23 41 3 52 40 7 77 67 9 32 30 9 35 9 21 42 51 64 10 60 0 27 8 5 16 2 17 3 20 5 5 24 0 69 73 69 8 10 11 20 5 62 0 25 32 1 47 2 13 38 .1 7 45 9. 67 48 8. 33 77 2. 08 98 0. 90 34 8. 11 99 .3 3 18 4. 44 11 4. 16 15 4. 89 11 3. 04 10 2. 94 55 .7 7 39 .8 0 47 .4 5 10 0. 65 11 9. 86 83 .0 7 30 .9 0 23 .7 7 4. 71 18 .4 1 24 .2 2 23 .2 1 Recovered 9 APR Beds : Patients Physicians : Patients Nurses & Midwives : Patients Table 5: Total cases, recoveries, deaths as on July 20, 2020 for top 25 countries Country Total cases Total deaths Total recovered Death % Recovery % United States 3,901,026 143,321 1,802,550 3.67 46.21 Brazil 2,100,112 79,535 1,371,229 3.79 65.29 India 1,127,281 27,628 707,523 2.45 62.76 Russian Federation 777,486 12,427 553,602 1.60 71.20 South Africa 364,328 5,033 191,059 1.38 52.44 Peru 353,590 13,187 241,955 3.73 68.43 Mexico 344,224 39,184 217,423 11.38 63.16 Chile 330,930 8,503 301,794 2.57 91.20 Spain 307,335 28,420 N/A 9.25 #VALUE! United Kingdom 294,792 45,300 N/A 15.37 #VALUE! Iran, Islamic Rep. 276,202 14,405 240,087 5.22 86.92 Pakistan 265,083 5,599 205,929 2.11 77.68 Saudi Arabia 253,349 2,523 203,259 1.00 80.23 Italy 244,434 35,045 196,949 14.34 80.57 Turkey 219,641 5,491 202,010 2.50 91.97 Bangladesh 207,453 2,668 113,556 1.29 54.74 Germany 202,901 9,163 187,800 4.52 92.56 Colombia 197,278 6,736 91,793 3.41 46.53 France 174,674 30,152 79,233 17.26 45.36 Argentina 126,755 2,260 54,105 1.78 42.68 Canada 110,338 8,852 97,051 8.02 87.96 Qatar 107,037 159 103,782 0.15 96.96 Iraq 92,530 3,781 60,528 4.09 65.41 Indonesia 88,214 4,239 46,977 4.81 53.25 Egypt, Arab Rep. 87,775 4,302 28,380 4.90 32.33 Alagappan, et al.: A Comparative Study on Corona Virus Pandemic – What do Figures Indicate International Review of Management and Marketing | Vol 10 • Issue 4 • 2020 167 Table 6: Comparison of data for July 9, 2020 and July 20, 2020 Country July 20, 2020 April 9, 2020 Raise in case% Decrease in death % Raise in recovery %Total cases July 20, 2020 Total deaths July 20, 2020 Total recovered - July 20, 2020 Total cases - April 9, 2020 Total deaths - April 9, 2020 Total recovered- 9-7-2020 United States 3,901,026 143,321 1,802,550 461437 16478 25410 88.17 3.25 45.56 Brazil 2,100,112 79,535 1,371,229 18092 950 173 99.14 3.74 65.28 India 1,127,281 27,628 707,523 6725 226 620 99.40 2.43 62.71 Russian Federation 777,486 12,427 553,602 10131 76 698 98.70 1.59 71.11 South Africa 364,328 5,033 191,059 1934 18 95 99.47 1.38 52.42 Peru 353,590 13,187 241,955 5256 138 1438 98.51 3.69 68.02 Mexico 344,224 39,184 217,423 3181 174 633 99.08 11.33 62.98 Chile 330,930 8,503 301,794 5972 57 1274 98.20 2.55 90.81 Spain 307,335 28,420 N/A 153222 15447 52165 50.14 4.22 #VALUE! United Kingdom 294,792 45,300 N/A 65872 7111 359 77.65 12.95 #VALUE! Iran, Islamic Rep. 276,202 14,405 240,087 66220 4110 32309 76.02 3.73 75.23 Pakistan 265,083 5,599 205,929 4489 65 572 98.31 2.09 77.47 Saudi Arabia 253,349 2,523 203,259 3287 44 666 98.70 0.98 79.97 Italy 244,434 35,045 196,949 143626 18279 28470 41.24 6.86 68.93 Turkey 219,641 5,491 202,010 42282 908 2142 80.75 2.09 91.00 Bangladesh 207,453 2,668 113,556 330 21 33 99.84 1.28 54.72 Germany 202,901 9,163 187,800 118181 2607 52407 41.75 3.23 66.73 Colombia 197,278 6,736 91,793 2223 69 174 98.87 3.38 46.44 France 174,674 30,152 79,233 118781 12228 23413 32.00 10.26 31.96 Argentina 126,755 2,260 54,105 1795 72 365 98.58 1.73 42.40 Canada 110,338 8,852 97,051 20654 503 5162 81.28 7.57 83.28 Qatar 107,037 159 103,782 2376 6 206 97.78 0.14 96.77 Iraq 92,530 3,781 60,528 1232 69 496 98.67 4.01 64.88 Indonesia 88,214 4,239 46,977 3293 280 252 96.27 4.49 52.97 Egypt, Arab Rep. 87,775 4,302 28,380 1699 118 348 98.06 4.77 31.94 Regression tables Model summary Model R R square Adjusted R square Std. error of the estimate 1 0.839a 0.704 0.700 81.0268 ANOVAa Model Sum of squares df Mean square F Sig. 1 Regression 2714537.625 2 1357268.812 206.733 0.000b Residual 1142368.305 174 6565.335 Total 3856905.930 176 a. Dependent variable: BedsPatients b. Predictors: (Constant), Deaths9APR, Recovered9APR Coefficientsa Model Unstandardized coefficients Standardized coefficients t Sig. B Std. error Beta 1 (Constant) 5.084 6.272 0.811 0.419 Recovered9APR 0.005 0.001 0.330 6.235 0.000 Deaths9APR 0.036 0.003 0.592 11.170 0.000 4.3. Regression – Does the Number of Physicians in the Hospital Influenced the Death Rate and Recovery Rate? The data indicated that 82% of both recovery and death rate had a significant relationship to the number of physicians attending to the COVID-19 patients in the hospital for all nations. The regression equation for the recovery and death is as follows. Equations Recovery = y = 0.002x + 1.932 Death = y = 0.040x + 1.932 Model summary Model R R square Adjusted R square Std. error of the estimate 1 0.906a 0.820 0.818 51.3195 ANOVAa Model Sum of squares df Mean square F Sig. 1 Regression 2087803.327 2 1043901.663 396.365 0.000b Residual 458262.014 174 2633.690 Total 2546065.340 176 Coefficientsa Model Unstandardized coefficients Standardized coefficients t Sig. B Std. error Beta 1 (Constant) 1.932 3.972 0.486 0.627 Recovered9APR 0.002 0.001 0.131 3.173 0.002 Deaths9APR 0.040 0.002 0.818 19.807 0.000 Alagappan, et al.: A Comparative Study on Corona Virus Pandemic – What do Figures Indicate International Review of Management and Marketing | Vol 10 • Issue 4 • 2020168 4.4. Regression – Does Number of Nurse/Midwife in the Hospital Influenced the Death Rate and Recovery Rate? The data indicated that 58% of both recovery and death rate had a significant relationship to the number of the nurses or midwife attending to the COVID-19 patients in the hospital for all nations. The regression equation for the recovery and death is as follows. Equations Recovery = y = 0.003x + 9.216 Death = y = 0.102x + 9.216 Model summary Model R R square Adjusted R square Std. error of the estimate 1 0.766a 0.586 0.581 222.7877 ANOVAa Model Sum of squares df Mean square F Sig. 1 Regression 12162267.109 2 6081133.555 122.519 0.000b Residual 8586744.272 173 49634.360 Total 20749011.382 175 Coefficientsa Model Unstandardized coefficients Standardized coefficients t Sig. B Std. Error Beta 1 (Constant) 9.216 17.296 0.533 0.595 Recovered9APR 0.003 0.002 0.066 1.053 0.294 Deaths9APR 0.102 0.009 0.722 11.509 0.000 4.5. Bivariate - correlations Correlations Deaths9APR Recovered9APR Beds patients Deaths9APR Pearson correlation 1 0.628** 0.799** Sig. (2-tailed) 0.000 0.000 n 184 184 177 Recovered9APR Pearson correlation 0.628** 1 0.701** Sig. (2-tailed) 0.000 0.000 n 184 185 177 Beds patients Pearson correlation 0.799** 0.701** 1 Sig. (2-tailed) 0.000 0.000 n 177 177 177 Physicians patients Pearson correlation 0.900** 0.644** 0.943** Sig. (2-tailed) 0.000 0.000 0.000 n 177 177 177 Nursesamp midwives patients Pearson correlation 0.764** 0.519** 0.945** Sig. (2-tailed) 0.000 0.000 0.000 n 176 176 176 Correlations Physicians patients Nursesamp midwives patients Deaths9APR Pearson correlation 0.900 0.764** Sig. (2-tailed) 0.000 0.000 n 177 176 Recovered9APR Pearson correlation 0.644** 0.519 Sig. (2-tailed) 0.000 0.000 n 177 176 Beds patients Pearson correlation 0.943** 0.945** Sig. (2-tailed) 0.000 0.000 n 177 176 Physicians patients Pearson correlation 1** 0.952** Sig. (2-tailed) 0.000 n 177 176 Nursesamp midwives patients Pearson correlation 0.952** 1** Sig. (2-tailed) 0.000 n 176 176 **Correlation is significant at the 0.01 level (2-tailed) The variables the patient to hospital beds has a positive influence of 70%. That is, the number of beds in the hospital decided the outcome of the patient’s recovery, while the converse on death is also visible. Similarly, the result of the physicians and nurse treating the COVID 19 cases had a profound influence of 81% and 58% respectively. This indicated that the countries with good infrastructure with an adequate number of physicians and nurse made a huge difference in the recovery of the patient. Though these factors play a vital role, the other influential factors namely, the intensive care provided to the patients in terms of giving proper attention, medication, ventilators, had a decisive role to play. This study is limited to the analysis of understanding the relationship of what factors influenced based either the recovery or the death. 5. DISCUSSION The mapping and analysis of the total Coronavirus cases against data indicated some exciting results. Notably, countries with high infrastructure facilities like the US, Spain, Italy all had been rated to have fared in containing the pandemic. However, the results indicated that there are other influential factors that influenced the recovery percentage. The US had a recovery rate of 5.51% and nearly on par with the death rate of 3.58%, while Spain, Switzerland and numerous other countries had a better recovery rate as indicated in Table 4. Though it is too early to provide complete information, the early detection of COVID-19 is crucial to prevent the spread. However, it should be noted that the infrastructure facilities and better healthcare professionals played a vital role in the process of recovery. As the second wave of onward transmission is active, it potentially risks the weaker health systems across the globe and as indicated the infrastructure facilities need to be attended to with high priority and the nations need to establish more temporary healthcare facilities to contain the pandemic. Many Asia and African nations are likely to be impacted more due to Alagappan, et al.: A Comparative Study on Corona Virus Pandemic – What do Figures Indicate International Review of Management and Marketing | Vol 10 • Issue 4 • 2020 169 the infrastructure facilities available in the countries. However, the response of many African countries towards the pandemic has been more positive and have managed the crises well so far. It is also essential that nations support each other in both monetarily and aid in providing assistance in the form of sending professionals under the “Doctors/Nurses without Borders.” 6. CONCLUSION Results indicate that there is a strong correlation between the selected variables when analysing the trend. The study indicated that the model is statistically significant at this point due to data on patient-bed, hospital-physicians, nurse ration influence the death and recovery process duly. It is also essential to note that these variables are influenced by other the dependent variables in the form of available ventilators, medications, safety equipment, it can be concluded that the model will result in a significant finding when the data for the reported cases, death and recovery stabilise. REFERENCES Arab-Mazar, Z., Shah, R., Rabaan, A.A., Dharma, K., Rodriguez- Morales, A.J. (2020), Mapping the incidence of COVID-19 hotspot in Iran-implications for travellers. Travel Medicine and Infectious Disease, 34, 101630. Biscayart, C., Angeleri, P., Lloveras, S., Chaves, T.D.S., Schlagenhauf, P., Rodríguez-Morales, A.J. 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