(TIỂU LUẬN) ASSESSMENT TASK 3a TEAM REPORT OVERALL TEAM CONCLUSION 17 1 the main factors that impact the number of deaths due to COVID 19

RMIT University 2020 ASSESSMENT TASK 3A TEAM REPORT RMIT University 2020 TABLE OF CONTENT I DATA COLLECTION II DESCRIPTIVE STATISTICS Check for outlier Measure of central tendency Measure of variation Box and Whisker plots Conclusion 1 1 2 III MULTIPLE REGRESSION Building Regression model (applying Backward Limitation) FINAL Regression Model of each region IV TEAM REGRESSION CONCLUSION 3 V TIME SERIES Regression output and formula of the significant trend model Recommend trend model Prediction on number of deaths on May 29, May 30, May 31 VI TIME SERIES CONCLUSION 10 10 15 16 16 VII OVERALL TEAM CONCLUSION The main factors that impact the number of deaths due to COVID-19 Covid 19 deaths prediction Two other variables Recommendation 17 17 17 18 18 VIII REFERENCES IX APPENDIX 19 22 Firstly breaking out at the end of 2019 at China, Covid 19 is now a severe pandemic that has globally effects on different fields, including work and daily life, economic, food security and medical ( The European Quality Assurance Register for Higher Education 2020; United Nations 2020; Food and Agriculture Organization of the United Nations 2020) With the vigorously spreading speed, the disease now appears at every continent (except Antarctica) and caused over 311,000 cases of fatality (Worldometer 2020) Especially at Western, the disease existed later than at Eastern, however, more complex and have not shown any sign of decreasing (French Institute of International Relations 2020) This paper will have an insight at two specificial Western regions - Europe and European Union (EU), by analyzing the number of Covid 19 deaths in each region and its relationship with related factors, examining the trend model of Covid 19 deaths in both regions as well as giving out some predictions I DATA COLLECTION This section provides data (information) that is necessary for part II and III analysis According to Romos (2018), Europe contains 54 nations and territories while the EU consists of 27 regions However, due to the lack of information in some countries, our datasets consist of 27 EU countries but only 41 European countries The datasets will contain six variables, namely total number of Covid 19 in each country period 22 January to 24 April, population, medical doctors per 10,000 people, hospital bed per 10,000 people, average temperature of the first four months and average rainfall of the first for four months For details, please see Appendix and Appendix II DESCRIPTIVE STATISTICS Mean Mode Median Range IQR Variance SD CV Europe 2837.05 14 169 25082 952 44603267.90 6678.57 235% European Union 3347.41 14 225 25082 1889 52330253.48 7233.97 216% Table Europe and European Union dataset descriptive statistics Check for outlier For Europe data set, there is no observation smaller than the lower bound (Q1-1.5 × IQR) but seven observations bigger than the upper bound (Q3+1.5 × IQR) Thus, there are seven outliers in this dataset For European Union data set, there is no observation smaller than the lower bound (Q1-1.5 × IQR) but five observations bigger than the upper bound (Q3+1.5 × IQR) Thus, there are six outliers in this dataset Measure of central tendency Since both datasets have outliers, Median, the measure of central tendency that is not affected by outliers, might be the most suitable measurement in this case Medians of Europe and EU dataset are 169 and 225, respectively For Europe, it could be implied that 50% of countries in this region have more than 169 Covid 19 deaths (period 22 January to 23 April) and 50% above 169 deaths Therefore, on average, it is 169 Covid 19 deaths per country in European dataset Similar conclusions could be made for the European Union 50% of countries in this region have more than 225 Covid 19 deaths (period 22 January to 23 April) and 50% above 225 deaths On average, it is 225 Covid 19 deaths per European Union country RMIT University 2020 Europe dataset’s Median is slightly higher than European Union dataset’s so in general, number of Covid 19 deaths in European Union countries is slightly higher than in European countries Measure of variation In this context, Interquartile Range (IQR) would be the most suitable measurement as it is not affected by outliers and could measure how much the middle 50% of observations spread out from the Median - the chosen average in this case Interquartile ranges of Europe and European Union dataset are 952 and 1889, respectively Interquartile illustrates how the middle 50% observations spread out and the smaller the IQR is, the more consistent the middle 50% observations are Thus, it could be concluded that European Union dataset is less consistent in terms of Covid 19 deaths per country In other words, the difference between the number of Covid 19 fatalities per nation in European Union is bigger than in Europe Box and Whisker plots According to the box and whisker plots graph, both datasets are right-skewed because their right part is longer than their left part This implies that for each dataset, the majority of the data are located on the high side of the graph In other words, most of the countries (in both European Union and Europe region) have a high number of Covid 19 deaths The graph also indicates that there are outliers in both datasets, meaning both datasets contain extreme values which would affect the objectiveness of some sensitive measurements, such as Mean or Range Thus, those sensitive measurements are not recommended to use in assessing the two datasets The box plot shows that Europe’s Median (quartile 2) is slightly smaller than European Union’s Median, meaning that in general, the number of Covid 19 deaths in EU countries is higher than European countries Comparing the two boxes, it could be concluded that the European Union's box is bigger than Europe’s, meaning that the number of deaths of the middle 50% European Union countries spread out more Figure Europe and EU dataset box and whisker widely from the Median than the middle 50% of Europe Since the middle 50% observations are generally considered as the most concentrated part of the dataset, it is possible that the number of Covid 19 deaths between European Union countries vary more than Europe countries’ Conclusion Having analyzed European Union and Europe datasets descriptive statistics, it could be concluded that in general, from January 22 to April 23, European Union countries have more cases of Covid 19 RMIT University 2020 deaths than Europe because Europe Median is smaller than EU Median Since both datasets contain outliers, some sensitive measurements such as Mean or Range are likely to be unreliable in and should not be used in assessing these two datasets Moreover, the IQR results of Europe and European Union revealed the variation in number of Covid 19 deaths in the two regions European Union’s IQR is smaller than Europe’s IQR so the number of Covid 19 deaths among European Union countries is considered less consistent than Europe, meaning the difference between the number of Covid 19 fatalities among European Union countries is bigger than the difference among European countries III MULTIPLE REGRESSION In this part, we will use the data of regions which are Europe and European Union (it should be noticed that all the countries within European Union are included in Europe) The data of the two regions are collected based on different variables: ● Total number of deaths due to COVID 19 ● Average temperature (in Celsius) ● Average rainfall (in mm) ● Medical doctors (per 10,000 people) ● Hospital beds (per 10,000 people) ● Population (in thousands) Among variables above, the total number of deaths due to COVID 19 is the only dependent variable Other variables including average rainfall (in mm) and average temperature (in Celsius), hospital beds (per 10,000 people), population of the country in 2018 (in thousands), and medical doctors (per 10,000 people), all are considered as independent variables Based on these independent variables, we will build multiple regression models for Europe region and Europe Union region to predict the number of death rates due to COVID 19 For each data set of each region, we will apply backward elimination to reach the final model with only the variables that are significant at 5% level of significance Building Regression model of Europe and European Union (applying Backward Limitation) * Europe Step 1: Regression output for Europe (1) It can be seen that, there are independent variables which are insignificant at 0.05 significance level, but we first eliminate the Average temperature (in Celsius) since this non significant independent variable has the highest pvalue Figure Regression Output for Europe (1) RMIT University 2020 Step 2: Regression output for Europe (2) After eliminating the Average temperature (in Celsius) out of those independent variables, we run the regression analysis again and have the summary output as below: From the regression output, there are still non-significant independent variables since their p-value is larger than 0.05 We remove the Medical doctor (per 10,000 people) since this non significant independent variable has the highest pvalue Figure Regression Output for Europe (2) Step 3: Regression output for Europe (3) After eliminating the Medical doctor (per 10,000 people) out of those independent variables, we run the regression analysis again and have the summary output as below: As can be seen in the summary output, there is still non-significant independent variable which is the Average rainfall (in mm) since its p-value is larger than 0.05 Thus, we remove it out of the independent variables Figure Regression Output for Europe (3) RMIT University 2020 Step 4: Regression output for Europe (4) (FINAL Model) After removing the non-significant independent variables including the Average rainfall (in mm), the Average temperature (in Celsius), and the Medical doctor (per 10,000 people), we reach the FINAL regression model where the two independent variables left namely the Hospital bed (per 10,000 people) and the Population (in thousands) are significant at 5% significance level (p-value < 0.05) This indicates that these two significant independent variables have an effect on the number of deaths due to COVID 19 at 5% level of significance Figure Regression Output for Europe (4) (Final Model) * European Union Step 1: Regression output for European Union (1) It can be seen that, there are independent variables which are insignificant at 0.05 significance level, but we first eliminate the Medical doctor (per 10,000 people) since this non significant independent variable has the highest p-value Figure Regression Output for European Union (1) RMIT University 2020 Step 2: Regression output for European Union (2) After eliminating the Medical doctor (per 10,000 people) out of those independent variables, we run the regression analysis again and have the summary output as below: From the regression output, there are still non-significant independent variables since their p-value is larger than 0.05 We remove the Average Temperature (in Celsius) since this non significant independent variable has the highest p-value Figure Regression Output for European Union (2) Step 3: Regression output for European Union (3) After eliminating the Average Temperature (in Celsius) out of those independent variables, we run the regression analysis again and have the summary output as below: As can be seen in the summary output, there is still non-significant independent variable which is the Average rainfall (in mm) since its pvalue is larger than 0.05 RMIT University 2020 Thus, we remove it out of the independent variables Figure Regression Output for European Union (3) Step 4: Regression output for European Union (4) (FINAL Model) After eliminating non-significant independent variables including the Average temperature (in Celsius), the Average rainfall (in mm) and the Medical doctor (per 10,000 people), we reach the FINAL regression model where the two independent variables left namely the Hospital bed (per 10,000 people) and the Population (in thousands) are significant at 5% significance level (p-value < 0.05) This indicates that these two significant independent variables have an impact on the number of deaths due to COVID 19 at 5% level of significance Figure Regression Output for European Union (4) (Final Model) FINAL Regression Model of each region * Europe’s FINAL Regression model a) Regression Output RMIT University 2020 Figure 10 Regression Output for Europe (Final Model) b) Regression Equation The number of deathsdue = 5717.342 – 102.152 ^¿ COVID ¿ × Population (in thousands) × Hospital bed (per 10,000 people) + 0.118 c) Interpret the regression coefficient of the significant independent variables: ● The coefficient of Hospital bed (per 10,000 people) ( b1 = -102.152) denotes the negative relationship between the two variables, which means for every increase in the hospital bed (per 10,000 people), the number of deaths due to COVID 19 is estimated to decrease by 102.152 deaths, given that the population holding constant ● The coefficient of Population (in thousands) ( b2 = 0.118) indicates the positive relationship between two variables, which means for every increase of 1,000 people in population (in thousands), the predicted number of deaths due to COVID 19 increases by 118 deaths, holding the hospital bed constant d) Interpret the coefficient of determination The Coefficient of Determination ( R2 = 0.320) shows that 32% of the variation in the number of deaths due to COVID 19 can be explained by the variation in the hospital bed (per 10,000 people) and the population (in thousands) Whereas, the remaining 68% of the variation in the number of deaths due to COVID 19 is affected by other factors * European Union’s FINAL Regression model a) Regression output Figure 11 Regression Output for European Union (Final Model) RMIT University 2020 This part presents the trend model analysis for the European Union and Europe by collecting the daily data for total number of deaths due to COVID-19 in each region from January 01 and April 30, 2020 (Appendix 3) Specifically, the paper provides the regression output and formula of the significant trend model in each region, gives recommendations about trend models for prediction, as well as forecasts the number of deaths due to Covid-19 in certain days in both regions Regression output and formula of the significant trend model a European Union Since the number of deaths in the European Union remain from January to February 14, our exponential trend model, the model used to indicate variable’s growth or decay, will be conducted based on data starting from February 15 when the case of death is starting to be bigger than Besides, the linear trend and quadratic trend will also be based on that day for consistency *Regression output and hypothesis test for significant trend model Linear Trend Figure 12 Regression output of the linear trend of the number of deaths in the European Union from February 15 to April 30, 2020 Hypothesis testing (at a 5% significance level) for Linear Trend in European Union Step 1: H0: β = (There is no linear trend) H1: β ≠ (There is a linear trend) Step 2: p-value (0.00) < α (0.05 )→ Reject H0 Step 3: With 95% level of confidence, we can say that there is a linear trend in the number of deaths in the European Union b0 = -694.858→ b shows that the number of deaths in European Union will be -694.858 in day (before February 15, 2020), which makes non-sense in the real life Therefore, there should be deaths on February 14, 2019 b1 = 62.211 → b1 shows that the number of deaths in European Union will increase by 62.211 deaths every day Quadratic Trend 11 RMIT University 2020 Figure 13 Regression output of the quadratic trend of the number of deaths in the European Union from February 15 to April 30, 2020 Hypothesis testing (at a 5% significance level) for Quadratic Trend in European Union Step 1: H0: β = (There is no quadratic trend) H1: β ≠ (There is a quadratic trend) Step 2: p-value (0.10) > α (0.05 )→ Do not reject H0 Step 3: With 95% level of confidence, we can say that there is no quadratic trend in the number of deaths in the European Union Exponential Trend Figure 14 Regression output of the exponential trend of the number of deaths in the European Union from February 15 to April 30, 2020 log(b0) = -1.532 → b0 = 0.029 log(b1) = 0.090 → b1 = 1.230 => (b1–1) x 100% = 23% → Number of deaths in European Union is estimated to increase by 23% every day Hypothesis testing (at a 5% significance level) for Exponential Trend in European Union Step 1: H0: β = (There is no exponential trend) H1: β ≠ (There is an exponential trend) Step 2: p-value (0.00) < α (0.05 )→ Reject H0 Step 3: With 95% level of confidence, we can say that there is an exponential trend in the number of deaths in the European Union 12 RMIT University 2020 Therefore, at 95% level of confidence, the significant trend model for this region’s data is linear and exponential trend At 95% level of confidence, there is no quadratic trend model * Formula of the significant trend model The formula of significant trend model is defined by two variables ^y = predicted number of deaths due to Covid-19 T = day EU Linear Exponential (linear format) Exponential (non-linear format) ^y = 62.211 × T - 694.858 log( ^y ) = 0.090 × T - 1.532 ^y = 1.230T × 0.029 Table 3: Formula of the significant trend model of European Union dataset b Europe Since the number of deaths in Europe remain from January to February 14, our exponential trend model, the model used to indicate variable’s growth or decay, will be conducted based on data starting from February 15 when the case of death is starting to be bigger than Besides, the linear trend and quadratic trend will also be based on that day for consistency *Hypothesis testing and regression output for significant trend model Linear trend Figure 15: Regression output of the linear trend of the number of deaths in Europe from February 15 to April 30, 2020 Hypothesis testing (at a 5% significance level) for Linear Trend in Europe Step 1: H0: β = (There is no linear trend) H1: β ≠ (There is a linear trend) Step 2: p-value (0.00) < α (0.05 )→ Reject H0 Step 3: As we reject H0, with 95% level of confidence, we can say that there is a linear trend in the number of deaths in Europe 13 RMIT University 2020 b0 = -730.229→ b0 shows that the number of deaths in Europe will be -730.229 in day (before February 15, 2020), which makes non-sense in the real life Therefore, there should be deaths on February 14, 2020 b1 = 64.342 → b1 shows that the number of deaths in Europe will increase by 64.342 deaths every day 14 RMIT University 2020 Quadratic trend Figure 16: Regression output of the quadratic trend of the number of deaths in Europe February 15 to April 30, 2020 Hypothesis testing (at a 5% significance level) for Quadratic trend in Europe Step 1: H0: β = (There is no quadratic trend) H1: β ≠ (There is a quadratic trend) Step 2: p-value (0.134) > α (0.05 )→ Do not reject H0 Step 3: As we not reject H 0, with 95% level of confidence, we can say that there is no quadratic trend in the number of deaths in Europe Exponential trend Figure 17: Regression output of the exponential trend of the number of deaths in Europe from February 15 to April 30, 2020 Hypothesis testing (at a 5% significance level) for Exponential trend in Europe Step 1: H0: β = (There is no exponential trend) H1: β ≠ (There is an exponential trend) Step 2: p-value (0.00) < α (0.05 )→ Reject H0 Step 3: As we reject H 0, with 95% level of confidence, we can say that there is an exponential trend in the number of deaths in Europe log(b0) = 0.377 → b0 = 2.38 15 RMIT University 2020 log(b1) = 0.054 → b1 = 1.13 => (b 1–1) x 100% = 13% → Number of deaths in Europe is estimated to increase by 13% every day According to the hypothesis testing above for Europe, we are 95% confident that the significant trend model for Europe is linear and exponential trend And with 95% level of confidence, there is no quadratic trend * Formula of the significant trend model The formula of significant trend model is defined by two variables: ^y = predicted number of deaths due to Covid-19 T = day Europe ^y = 64.342 × T - 730.229 Linear log( ^y )= 0.054 × Exponential (linear format) Exponential (non-linear format) T + 0.377 ^y = 1.132T × 2.382 Table 4: Formula of the significant trend model of Europe dataset Recommend trend model In order to predict precisely the number of deaths, comparing the SSE and MAD of each region is crucial According to Black (2013), this method identifies the model producing the smallest measurement errors Put differently, the lower the SSE and MAD, the closer the predicted values to the actual data Tables comparing the SSE and MAD of each region are presented following: a European Union SSE MAD Linear 59184024.557 693.8288 Exponential 109562572281.912 13479.839 Table 5: SSE and MAD calculation of European Union number of death dataset According to the table above, the SSE and MAD of linear trend model are smaller compared to exponential trend model It means that the linear trend model is recommended in order to get the most accurate predicted data for the future number of deaths in the European Union using this formula ^y = 62.211 × T - 694.858 b Europe SSE MAD Linear 60487931.442 703.543 Exponential 2151855491.893 2520.604 Table 6: SSE and MAD calculation of Europe number of death dataset From the table above, we can see that the linear trend model has the smaller SSE and MAD compared to the exponential trend This indicates this forecasting trend model not only provides the smallest measuring errors but also the closer predicted value to the actual data Therefore, linear trend model is the 16 RMIT University 2020 most appropriate trend model to predict the number of deaths in Europe This is the formula: ^y = 64.342 × T - 730.229 Prediction on number of deaths on May 29, May 30, May 31 Applying the formulas of the most suitable trend model predicted above, which is linear trend for both regions respectively, we can calculate the prediction number of deaths on specific days Specifically, May 29 is day 105, May 30 is day 106, and May 31 is day 107 The detail prediction is presented in the below table (all figures are rounded): EU Europe May 29 5837 6026 May 30 5900 6090 May 31 5962 6154 Table 7: Prediction of number of deaths in European Union and Europe on May 29, May 30 and May 31 VI TIME SERIES CONCLUSION Line chart Figure 18: Line chart of the daily total deaths over time from January 01 and April 30, 2020 in European Union and Europe The line chart above displays the daily number of deaths due to Covid 19 in the European Union and Europe from January 01 to April 30, 2020 Overall, it can be said that both regions’ number of deaths witnessed an upward trend After staying at death for 45 first days, the number of deaths in both regions have increased dramatically continuously since 15 February Even though there were decreases in some days, in general the number of deaths in both follow the upward trend throughout the time By looking at the line chart, European Union and Europe’s number of deaths are almost the same since we cannot see the clear difference between two lines This similarity can be explained because European Union is included in Europe so there might be some overlapping data However, at the end of the period, it is clear that Europe's number of deaths is slightly higher than European Union's 17 RMIT University 2020 Since the two regions line charts have similar patterns of growth, it is possible that the two regions follow the same trend model As analyzed previously, the two regions both follow a linear trend However, the SSE and MAD of both region datasets is quite high, so there will be huge difference between predicted value and actual number of deaths, which underwent lots of fluctuation as revealed in the graph Suitable trend model to predict world deaths According to Anderson (2014), in order to predict precisely the number of deaths due to Covid-19, it is advisable to find the trend model which has the highest coefficient of determination (R 2) as it assesses how well the regression equation actually describes the relationship between the days and the number of deaths In other words, the higher the coefficient of determination (R 2), the higher precision of the trend model, indicating more accuracy in the prediction of the model The coefficients of determination R of European Union and Europe are presented below: R2 (%) European Union Europe 70.5% 71.5% Table 8: Comparing Coefficient of determination ( R2 ¿ of European Union and Europe's number of deaths As is revealed from the table, the coefficient of determination of Europe is higher than that of European Union Therefore, the linear trend model of European is considered the most suitable trend model to predict the deaths resulting from Covid-19 in the world Moreover, a large sample size narrows the distribution of the test statistics, leading to the more accuracy in the test (College Board n.d) In other words, large sample size makes the outcome closer to the population value In this case, while there are only 27 countries in the EU, the number of countries in Europe is 54 (Romos 2018) Since the countries in Europe are twice more than in the EU, the trend model of Europe will provide more reliable prediction on the number of deaths, which can represent more accurately the deaths for the whole 195 countries in the world With regards to Europe, found in part V, linear trend model is the most accurate trend model to predict the number of deaths due to its smallest SSE and MAD To conclude, it consolidates the argument to use linear trend model of Europe to predict the deaths resulting from Covid-19 in the world with this formula: ^y = 64.342 × T - 730.229 VII OVERALL TEAM CONCLUSION The main factors that impact the number of deaths due to COVID-19 Having conducted multiple regression analysis in part III, it could be concluded that the average number of hospital beds and population are two significant factors that affect the number of Covid 19 deaths in Europe and EU Notably, it was pointed out that there is an inverse relationship between the number of Covid 19 deaths and the average number of hospital beds since this variable (average hospital bed) has a negative coefficient value in both datasets (-102.152 in Europe dataset and -146.198 in EU dataset) Interpreting, this reverse relationship means that the more hospital beds are provided, the less people dead because of Covid 19 As researches have shown, lack of medical equipment and facilities such as hospital beds will lead to tardy cure process and reduce the cure efficiency (Schlanger 2020; Kyodo 2020; Mangan & Schoen 2020) Thus, increasing the number of hospital beds is crucial in reducing cases of Covid 19 Contrary to hospital beds, population has a direct relationship with the number of Covid 19 deaths due to its positive cofficiency value in Europe and EU dataset (0.118 and 0.265 respectively) This direct relationship implies that the bigger the population, the more cases of Covid 19 fatality The 18 RMIT University 2020 underlying reason for this relationship possibly is because the more people, the harder social distance could be practiced (Rosenthal 2020) Covid 19 is a respiratory disease, meaning crowds and gatherings would increase the probability of spreading disease in society and the more people are infected, the more fatality cases would be Covid 19 deaths prediction In part VI, we have found out the most suitable trend model to predict world deaths due to Covid 19 which is ^y = 64.342 × T - 730.229 By applying this formula, we could predict the number of deaths due to COVID-19 in the world on June 30 2020 (day 137) When T =137, y =8084.625, meaning on June 30 2020, the number of Covid 19 deaths in the world might be around 8085 cases As this model is not one hundred percent accurate, the number that we calculated here is only approximately, not precisely Moreover, the trend model implies that the world deaths due to Covid 19 will not be reduced by the end of 2020 because on 31 December 2020 (day 321), the number of deaths would be approximate 21384.09, bigger than previous days such as June 30 (8085) This upwarding trend could be explained as ^y = 64.342 × T - 730.229 is the formula of an increasing line which goes up infinitely without any stop or decrease In other words, this trend model implies that overall the number of world deaths due to Covid 19 increases continuously However, this prediction is only correct if all factors that affect the number of Covid 19 deaths remain the same Covid 19 is happening complicatedly and nobody even expert could be certain about what would happen in the future (Kolata 2020) As analyzed previously, the number of Covid 19 deaths is affected considerably by population and number of hospital beds (per 10,000 people) Thus, if there is a significant decrease in population or increase in hospital beds, the number of Covid 19 deaths by the end of 2020 could be expected to decrease Two other variables The paper had analyzed and found out two significant variables that affect the number of Covid 19 deaths namely number of hospital beds per 10,000 people and population; however, it is possible there are other factors that influence the number of Covid 19 fatality but were not examined in this paper One of the possible variables to take further examination is the proportion of elderly in a nation According to the Centers for Disease Control and Prevention (2020), “older adults seem to be at higher risk for developing more serious complications from COVID-19 illness” In America, elderly who are 65 or older account for 80% of the nation’s Covid 19 deaths (Centers for Disease Control and Prevention 2020) Thus, it is possible that a number of old people have a direct relationship with Covid 19 deaths, meaning regions with a high ratio of old people would have a higher number of Covid 19 fatality cases Another variable could be taken into account is the number of Covid 19 testing kits According to U.S Department of Health and Human Services (2020), “shortages of testing supplies will lead to extended waits and limited hospitals’ ability to monitor the health of patients and staff” According to Le (2020), after investing more in Covid 19 testing kits, Viet Nam managed to have earlier diagnosis and more efficient medical solution for Covid 19 patients Zhai et al (2020) also states that early diagnosis would raise the treatment effect and reduce death rate Thus, it is possible that the number of testing kits has an inverse relationship with Covid 19 deaths, meaning the increase in number of testing kits might reduce the number of Covid 19 deaths Recommendation Covid 19 is the severe global epidemic that requires the whole society's effort and awareness to be resolved According to the U.S Department of Health and Human Services (2020), in order to reduce the number of Covid 19, it is crucial that every nation, region invest more in or seek out alternative solutions for medical equipment and facilities such as hospital beds or testing kits A possible way to 19 RMIT University 2020 increase bed availability is to convert nonoperational facilities in the community such as prisons and college dorms into temporary critical care units The World Health Organization (2020) also stresses the importance of ethically approved, highquality, systematic research state in stopping the disease spread In other words, it is recommended to capitalize more, both financial and human, in studying and finding out solutions to this disease besides decreasing demerit products that affect lungs and raising people's awareness about the epidemic Besides, social distance should still be practiced in order to restrict the disease source and spread because the number of deaths could only be reduced when the spreading rate is controlled 20 RMIT University 2020 VIII REFERENCES Anderson, A 2014, Business Statistics for Dummies , John Wiley & Sons, viewed 15 May 2020, ProQuest Ebook Central Black, K 2013, Applied business statistics: making better business decisions , 7th edn, John Wiley & Sons, Inc Centers for Disease Control and Prevention 2020, Older Adults, viewed 22 May 2020, College Board n.d., Power in Tests of Significance, College Board, viewed 22 May 2020, Choudhury, S 2020, 'US needs more protective equipment for health-care workers as coronavirus cases set to rise', CNBC, 23 March, viewed 12 May 2020, Food and Agriculture Organization of the United Nations 2020, Q&A: COVID-19 pandemic – impact on food and agriculture, Food and Agriculture Organization of the United Nations 2020, viewed May 2020, French Institute of International Relations 2020, Covid-19 and Europe-China Relations, viewed May 2020, Kolata, G 2020, ‘How Pandemics End’, The New York Times, May 10, viewed May 2020, Kyodo 2020, ‘Japan's local hospitals fear lack of supplies and staff may overwhelm health care systems’, The Japan Times, April 17, viewed May 2020, Le, S 2020, Containing the coronavirus (COVID-19): Lessons from Vietnam, World Bank Blog, viewed May 2020, Mangan, D & Schoen, J 2020, 'Coronavirus cases: These states face biggest potential shortfalls in hospital ICU beds', CNBC, April, viewed 12 May 2020, Our World in Data 2020, Daily and total confirmed COVID-19 deaths, World, Our World in Data, viewed May 2020, Ramos, J 2018, The Geographic Regions Of The World, Science Trends, viewed May 2020, Rosenthal, B 2020, 'Density Is New York City’s Big ‘Enemy’ in the Coronavirus Fight', The New York Times, 23 March, viewed 12 May 2020, 21 RMIT University 2020 Schlanger, Z 2020, ‘Begging for Thermometers, Body Bags, and Gowns: U.S Health Care Workers Are Dangerously Ill-Equipped to Fight COVID-19’, Time, April 20, viewed May 2020, < https://time.com/5823983/coronavirus-ppe-shortage/> The World Bank n.d., Climate data, Climate Change Knowledge Portal, viewed May 2020, The World Bank 2019, Population, The World Bank, viewed May 2020, The European Quality Assurance Register for Higher Education 2020, COVID-19 consequences, The European Quality Assurance Register for Higher Education, viewed May 2020, United Nations 2020, Everyone Included: Social Impact of COVID-19 , United Nations, viewed May 2020, U.S Department of Health and Human Services 2020, Hospital Experiences Responding to the COVID-19 Pandemic: Results of a National Pulse Survey March 23–27, 2020, viewed May 2020, World Health Organization 2020, WHO statement: Tobacco use and COVID-19, World Health Organization, viewed May 2020, World Health Organization n.d., Indicators of Hospital beds (per 10 000 population) , World Health Organization, viewed May 2020, World Health Organization n.d., Indicators of Medical doctors (per 10 000 population) , World Health Organization, viewed May 2020, Worldometer 2020, COVID-19 Coronavirus Pandemic, Worldometer, viewed May 2020, Zhai, P, Ding, Y, Wu, X, Long, J, Zhong, Y & Lie, Y 2020, The epidemiology, diagnosis and treatment of COVID-19, International Journal of Antimicrobial Agents 22 RMIT University 2020 IX APPENDIX Appendix Europe dataset ( The World Bank n.d.; The World Bank 2019; World Health Organization n.d.; World Health Organization n.d.; Worldometer 2020) 23 RMIT University 2020 Appendix EU dataset (The World Bank n.d.; The World Bank 2019; World Health Organization n.d.; World Health Organization n.d.; Worldometer 2020) 24 RMIT University 2020 Appendix Daily total death in Europe and European Union from January 2020 to April 30 2020 (Our World in Data 2020) 25 ... May 30, May 31 VI TIME SERIES CONCLUSION 10 10 15 16 16 VII OVERALL TEAM CONCLUSION The main factors that impact the number of deaths due to COVID -19 Covid 19 deaths prediction Two other variables... 0 .11 8), which shows that the population (in thousands) has a greater effect on the number of deaths due to COVID 19 of European Union This implies that the number of deaths resulting from COVID. .. life Therefore, there should be deaths on February 14 , 2 019 b1 = 62. 211 → b1 shows that the number of deaths in European Union will increase by 62. 211 deaths every day Quadratic Trend 11 RMIT