A Comparative Study on Corona Virus Pandemic – What do Figures Indicate?

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.


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 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.  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).

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   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).

DATA MODELING AND METHODOLOGY
Data selection (global reported, death and recovered cases) for analyzing the impact were collected from the John Hopkins 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. 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

MODEL COMPARISON, ANALYSIS AND RESULTS
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    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.

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.

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   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.

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 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."

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.