Modeling the Impact of Nationwide BCG Vaccine Recommendations on COVID-19 Transmission, Severity, and Mortality

COVID-19 was declared as pandemic on 11 th March 2020 by WHO. There are apparent dissimilarities in incidence and mortality of COVID -19 cases in different parts of world. Developing countries in Asia and Africa with fragile health system have shown lower incidence and mortality compared to developed countries with superior health system in Europe and America. Most countries in Asia and Africa have national BCG vaccination program while Europe and America do not have such program, or have ceased it. At present, there is no known therapy to treat COVID-19 disease. There is no vaccine available currently to prevent COVID- 19 disease. As mathematical modelling is ideal for predicting the rate of disease transmission as well as evaluating efficacy of possible public health prevention measures, we have created a mathematical model with seven compartments to understand nationwide BCG vaccine recommendation on COVID-19 transmission, severity and mortality. We have computed two basic reproduction number, one at vaccine free equilibrium point and other at non-vaccine free equilibrium point and carried out local stability, sensitivity and numerical analysis. Our result showed that individuals with BCG vaccinations have lower risk of getting COV ID-19 infection, shorter hospital stays, and increased rate of recovery. Furthermore, countries with long-standing universal BCG vaccination policies have reduced incidence, mortality, and severity of COVID -19. Further research will focus on exploring the immediate benefits of vaccination to healthcare workers and patients as well as benefits of BCG re-vaccination. Key words: COVID-19, BCG Vaccine, Modelling, Transmission. . CC-BY-NC-ND 4.0 International licenseIt is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted May 14, 2020. ; https://doi.org/10.1101/2020.05.10.20097121doi: medRxiv preprint NOTE: This preprint reports new research that has not been certified by peer review and should not be used to guide clinical practice. 2 1. Introduction In December of 2019, a novel strain of coronavirus was found in Wuhan, China and identified as Coronavirus Disease 2019 (COVID -19) [1]. Transmission of COVID-19, similar to numerous airborne respiratory viruses including tuberculosis and influenza, occurs through direct contact with an infected person through respiratory droplets when a person coughs or sneezes or indirect contact through interaction with contaminated surfaces with respiratory droplets from infected person [2]. As of April 18, 2020, the disease has spread to 210 countries with 2,324,731 total confirmed new cases reported and 16 0,434 deaths [3]. Since currently there is no definitive treatment or an effective vaccine for disease control, there is a crucial need to find measures that cure or reduce morbidity due to COVID-19. Figure 1: Geographical distribution of COVID -19 cases worldwide as of 18 th April 2020[4]. Adapted from European Cent re for Disease Prevention and Control. There are prominent dissimilarities in how COVID -19 is affecting different countries. Although developing countries like India, Philippines, Sri Lanka reported their initial case in January, they have yet to experience extensive community spread [5]. In contrast, developed countries like Italy and United Kingdom have experienced high mortality and widespread infection despite strong curtailing of social interactions. Stu dies show a d isproportionately smaller number of cases have been reported from disadvantaged and low-income countries [6]. It is puzzling that countries with more fragile health systems report less incidence of COVID- 19. Figure 1 shows a figure from the European Centre for Disease Prevention and Control which indicates that much of the incidence of COVID-19 is in the developing countries such as United Kingdom and United States [4]. A possible explanation for lower number of cases detected in these developing countries with extensive travel and trade links to China may stem . CC-BY-NC-ND 4.0 International licenseIt is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted May 14, 2020. ; https://doi.org/10.1101/2020.05.10.20097121doi: medRxiv preprint 3 from the induced heterologous protective activity of the Bacillus Calmette-Guerin (BCG) immunization [5]. Figure 2: Map displaying BCG vaccination policy by country. A: The country currently has universal BCG vaccination program. B: The country used to recommend BCG vaccination for everyone, but currently does not. C: The country never had universal BCG vaccination programs [7]. Adapted from BCG World Atlas. Vaccines provide protection from particular pathogen s by inducing effector mechanisms directed towards that pathogen. Certain live attenuated vaccines like the BCG vaccine increase immunity against not only a particular pathogen, but also numerous unrelated pathog ens that present with similar acute respiratory tract infections [8]. Previous studies have established that BCG vaccination confers non-specific protection via the induction of innate immune memory [9]. BCG is used widely across the world as a vaccine for Tuberculosis, with many developing nations having a universal BCG vaccination policy for new-born[10]. Studies have indicated that countries with uniform BCG vaccination policy show significantly lower COVID-19 cases and deaths per million people compared to countries where BCG vaccination policy was ceased or was never in place [5]. Figure-2 from the BCG world atlas shows that numerous developing countries currently employ a universal BCG vaccination program, while many developed countries have either ceased their BCG vaccination recommendation or never recommended it [7]. Comparison of Figures 1 and 2 also indicates that the largest number of COVID -19 cases are in countries that currently have no BCG vaccination recommendation. Furthermore, the year that universal BCG vaccination was established within a country has a significant c orrelation with the mortality rate, indicating that earlier implementation of the policy leads to protection of a larger fraction of the elderly population. For instance, although Iran currently employs a universal BCG vaccination policy, it was implemented as recently as 1984. Consequently, it reports an elevated mortality with 19.7 deaths per million inhabitants[6]. In contrast, Japan started its universal BCG policy in 1947 and has around 100 times less deaths per million people, with 0.28 deaths. Similarly, Brazil started universal vaccination in 1920 and also has an even lower mortality rate of 0.0573 deaths per million inhabitants [6]. Exploring this relationship between BCG vaccination and COVI D-19 further is crucial in establishing a potential short - or long-term prevention measure that is effective in reducing incidence and mortality of COVID -19. While previous studies have explored a possible link . CC-BY-NC-ND 4.0 International licenseIt is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted May 14, 2020. ; https://doi.org/10.1101/2020.05.10.20097121doi: medRxiv preprint 4 between the BCG vaccine and incidence of COVID-19, they have not modelled the impact of implementing a nationwide BCG immunization policy [6,7]. Mathematical modelling is ideal for predicting the rate of disease transmission as well as evaluating efficacy of possible public health prevention measures [11]. This study aims to create a model to evaluate the effect of nationwide BCG vaccine recommendations on incidence, severity, and mortality rates of COVID-19. Furthermore, our study aims to model the protentional implications of an immediate implementation of the universal BCG vaccination policy. 2. Mathematical Mode A novel corona viru s pandemic is occurring around the world, both in countries with and without the BCG vaccine . To analyse the importance of this vaccine against the fight of COVID-19, we have developed a mathematical model. This model has seven various compartments. First compartment is the class of exposed individuals to COVID-19 noted as From these individuals, some are vaccinated with BCG vaccine noted as and those who have not taken it are noted as . Now, with the certain rate these individuals transfer into Infected stage noted as and some can become critically infected noted as compartment. The individuals suffering from COVID -19 that need to be hospitalised a re noted as . The infected and hospitalised individual that recover are noted as . Table 1: Notation and its description Parameter Description Birth rate of class of exposed individuals Rate of individual who has taken BCG vaccine Rate of vaccinated individuals who get infection Rate of non-vaccinated individuals who get infection Rate at which infected individual become critically infected Rate at which infected individuals get hospitalised Recovery rate of infected individuals Rate at which critically infected individuals get hospitalised Recovery rate of hospitalised individuals Rate at which recovered individuals may get exposed Natural death rate Diseases induced death rate .E V VN I C H R B 1b 2b 3b 4b 5b 6b 7b 8b 9b µ dµ . CC-BY-NC-ND 4.0 International licenseIt is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted May 14, 2020. ; https://doi.org/10.1101/2020.05.10.20097121doi: medRxiv preprint 5 Figure 3: Transmission diagram of COVID-19 with BCG vaccine With the help of transmission shown in figure 3 and notation of parameter given in table 1, the system of non-linear ODE is derived as; (1) with Hence, the feasible region of given system is . Now, consider , , , , , then we get new system as follow E V VN I C H RB 2Vb 1EVb ( )11 VENb- 3 VNb 4 Ib 5 Ib 6 Ib 7Cb 8 Hb 9 Rb µ µ µ µ dµ dµ dµ ( )11 9 1 V dE BE V E N R Edt bb b µ=- -- + - 12 dV EV V Vdt bb µ=- - ( )131V VV V dN EN N Ndt bb µ=- - - 23 4 5 6 Vd dI VN IIIIdt bb b b b µ=+ - - - - 47 d dC ICCdt bb µ=- - 57 8 d dH ICH Hdt bb b µ=+ - - 68 9 dR IHR Rdt bb b µ=+ - - 0; , , , , , 0VEV N I C H R>³ ( ) 7,, , ,, , :VV BEV N I C H R R E V N I C H R µ + ìüL= Î + + + + + + £íý îþ 21 kbµ+= 32 kbµ+= 456 3 d kbbb µ+++ = 74 d kbµ+= 85 d kbµ+= 96 kbµ+= ( )11 9 1 V dE BE V E N R Edt bb b µ=- -- + - . CC-BY-NC-ND 4.0 International licenseIt is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted May 14, 2020. ; https://doi.org/10.1101/2020.05.10.20097121doi: medRxiv preprint 6 (2) The dynamical behaviour of system (1) is equivalent to the system (2). Hence, every solution of system (2) will remain in the region . The above system (2) has three equilibrium point by setting the equation as zero. 1. Diseases-free equilibrium point where 2. Vaccine free equilibrium point where 3. Non-vaccine free equilibrium point 11 dV EV k Vdt b=- ( )121V VV dN EN k Ndt b=- - 23 3 V dI VN k Idt bb=+ - 44 dC Ik Cdt b=- 57 5 dH IC k Hdt bb=+ - 68 6 dR IH k Rdt bb=+ - L ( ) 0 0 ,0 ,0 ,0 ,0 ,0 ,0PEE 0 BE µ= ( ) 1 11 1 1 1 1,0 , , , , ,PVEE NI C H R 2 1 11 kE b= - ( ) ( ) ( ) ( ) 1 3456 1 2 13 8 9 4 7 5 5 4 5 3 6 9 2 3 61 V kkkk B k BN kk k k k k bµ bb b b b b b b b b +-= -+ + -éùëû ( ) ( ) ( ) ( ) 3456 1 2 1 13 8 9 4 7 5 5 4 5 3 6 9 2 3 61 kkk B k BI kk k k k k bb µ bb b b b b b b b b +-= -+ + -éùëû ( ) ( ) ( ) ( ) 34 5 6 1 2 1 13 8 9 4 7 5 5 4 5 3 6 9 2 3 61 kk B k BC kk k k k k bb b µ bb b b b b b b b b +-= -+ + -éùëû ( )( ) ( ) ( ) ( ) 36 1 2 4 7 54 1 13 8 9 4 7 5 5 4 5 3 6 9 2 3 61 kB k B kH kk k k k k bb µ b b b bb b b b b b b b b +- += -+ + -éùëû ( )( ) ( ) ( ) ( ) 31 2 4 7 8 5 8 4 6 4 5 1 13 8 9 4 7 5 5 4 5 3 6 9 2 3 61 Bk B k k kR kk k k k k bb µ b b b b b b bb b b b b b b b b +- + += -+ + -éùëû ( ) 2 22 2 2 2 2,, 0 ,, , ,PEE V I C H R 1 2 1 kE b= . CC-BY-NC-ND 4.0 International licenseIt is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted May 14, 2020. ; https://doi.org/10.1101/2020.05.10.20097121doi: medRxiv preprint 7 The diseases-free equilibrium point is not valid according to current scenario and therefore we will discuss only two equilibrium points. 3. Basic Reproduction Number In this section, basic reproduction number is derived using next generation matrix method [12]. The quantity of basic reproduction number helps to understand the behaviour of the spread of COVID-19 amongs the polpulation. The basic reproduction number is denoted as . If the value of is less than 1 then the diseases is in the controlable stage. Otherwise, it has reached the epidemic stage. This is the ratio of affected infected individual by secondary infected individuals within the population. Using next generation matrix method, and are derived as and The Jacobian matrices of and are and defined as below and ( ) ( ) ( ) 3456 1 1 2 12 8 94 7 5 4 4 52 6 9 1 3 6 kkkk k BV kk k k k k µb bb b bb b b b b b -= ++ -éùëû ( ) ( ) ( ) 2456 1 1 2 12 8 94 7 5 4 4 52 6 9 1 3 6 kkk k BI kk k k k k bµ b bb b bb b b b b b -= ++ -éùëû ( ) ( ) ( ) 24 5 6 1 1 2 12 8 94 7 5 4 4 52 6 9 1 3 6 kk k BC kk k k k k bb µ b bb b bb b b b b b -= ++ -éùëû ( )( ) ( ) ( ) 26 1 1 4 7 54 2 12 8 94 7 5 4 4 52 6 9 1 3 6 kk B kH kk k k k k bµ b b b b bb b bb b b b b b -+= ++ -éùëû ( )( ) ( ) ( ) 21 1 4 7 8 5 8 4 6 4 5 2 12 8 94 7 5 4 4 52 6 9 1 3 6 kB k k kR kk k k k k bµ b b b b b b b bb b bb b b b b b -+ += ++ -éùëû 0R 0R f v ( ) 1 11 0 0 0 0 0 V EV EN f b b éù êú -êú êú êú= êú êú êú êú êúëû ( ) 1 2 23 3 44 57 5 68 6 11 9 1 V V V kV kN VN k I Ik Cv IC k H IH k R BE V E N R E bb b bb bb bb b µ éù êú êú êú -- + êú -+= êú êú -- +êú -- +êú êú-+ + - - +ëû f v F V ( ) ( ) 11 11 00 0 0 0 01 0 0 0 0 1 00 0 0 0 0 0 00 0 0 0 0 0 00 0 0 0 0 0 00 0 0 0 0 0 00 0 0 0 0 0 V EV EN F bb bb éù êú --êú êú êú= êú êú êú êú êúëû . CC-BY-NC-ND 4.0 International licenseIt is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted May 14, 2020. ; https://doi.org/10.1101/2020.05.10.20097121doi: medRxiv preprint 8 These Jacobian matrices are used to derive whose spectral radius is our desired basic reproduction number . Here, we have computed two basic reproduction number s; one at vaccine free equilibrium point and other at non-vaccine free equilibrium point. The expression of basi c reproduction number for non -vaccinated individuals i.e. at vaccine - free equilibrium point is denoted by given by Whereas, the expression of basic reproduction number for vaccinated individuals i.e. at nonvaccine-free equilibrium point is denoted by given by Moreover, the Basic reproduction for the system is . 4. Local Stability Analysis In this section, we will be discussing the local stability for vaccine and non -vaccine free equilibrium point utilizing the concept of eigenvalues. To find the eigen values, the Jacobian matrix of the system (2) is calculated as ( ) ( ) 1 2 23 3 44 57 5 68 6 11 9 11 00 0 0 0 0 00 0 0 0 0 000 0 00 0 0 0 00 0 0 00 0 0 10 0 0 1 V k k k kV k k EE V N bb b bb bb bb b bb µ éù êú êú êú-- êú -= êú êú --êú --êú êú -- + - +ëû 1FV - ( )0R (1) 0R ( ) ( )(( ( ( ) ( )) ( ( )( ) ( )) ( ) ( )( ))( ( ) ( ))) (1) 3 01 3 4 5 6 1 2 1 2 6 2 6 9 1 1 2 6 4 5 6 7 2 82 9 1 5 8 6 7 6 8 1 2 6 4 7 4 8 5 7 5 8 67 68 78 78 4 5 6 29 1 1 2 6 389 47 54 45 3 6 9 236 11 1 1 d d d Rk k k k B k B k k k k kk kk kk k k k k bb b µ b b b b b b b b b bµ b b bb bb b b b b b b b bb b b b bb bb bb µ bb b b b bb b b bbb bb bb b b =- + - + - - + + + + ++ - - + + + + + + +++ + + + - - + ++ - ( )( ( )( ( )) ( ) ( )( ) 2 11 3 4 5 6 3 8 9 4 7 5 4 45 3 6 9 236 3456 1 3 9 4 7 8 5 84 645 1 1 kkk kk k k k k k k Bk k k k k k k bb b b b b b bbb b bb µ bbb bb b -+ +- - - + + + (2) 0R ( ( )( ( ( ) ) ( ( ) ( )) ( )( ) ( ( ( ) ( ))) (2) 3 21 03 4 5 6 1 1 1 6 1 6 4 5 6 7 8 2 6 9 2 29 58 67 68 1 6 47 48 57 58 67 68 78 78 4 5 6 29 1 6 2 3 4 5 6 4 5 1 3 6 269 289 47 5 4 dd d kRk k k k B k k k k k kk kk k kk kk k k B kk k bµ µ b b b b b b b b µbµ bb bb bb bb bb bb bb bb bb bb bb µ bb b b b bb b bbb µ bbb bb b =- - + + + + + -+ +- + + + + + + + + ++ + + - + - -+ (1) ( 2) 00 0 2 RRR += . CC-BY-NC-ND 4.0 International licenseIt is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted May 14, 2020. ; https://doi.org/10.1101/2020.05.10.20097121doi: medRxiv preprint 9 Eigenvalues for above Jacobi matrix abo ut the vaccine -free equilibrium point are 0.1225, - 0.0664, -0.1375, -0.1375, -0.8463, -0.9431, 0.7019. Eigenvalues for above Jacobi matrix about the non-vaccine-free equilibrium point are 0.2298, -0.0655, -0.1595, -0.2296, -0.8464, -0.9431, -0.1021. Hence, both the equilibrium points are not locally asymptotically stable. 5. Sensitivity Analysis Here, sensitivity analysis is carried out to see which parameter is conferring a positive effect on the model. The sensitivity analysis is conducted on the basic reproduction number. It is to be done using Christoffel formula which is where is the model parameter. Table 2: Sensitivity Analysis Parameter -2.4260 -1.6980 25.09 -1.6980 -0.9799 -0.4074 1.2730 0 0.0116 0.0055 1.2450 0.5885 0.0173 0.0080 0.0124 0.0057 1.224 0.5753 1.224 0.5261 2.403 1.6980 0.0172 0.0261 From table 2, it can be observed that the growth rate is producing negative impact. Precisely, growth rate is more negative when there is no vaccinated individual. The Rate of individual vaccinated with BCG shows the positive imp act among all. Moreover, the disease induced death rate is lower for when compared to . Additionally, considering the sensitivity analysis for both the basic reproduction numbers, we conclude that BCG vaccine is beneficial in preventing COVID-19. ( ) ( ) ( ) ( ) 11 1 1 9 11 1 11 2 23 3 44 57 5 68 6 11 0 0 0 00 0 0 0 10 1 0 0 0 0 : 00 0 0 00 0 0 0 00 0 0 00 0 0 V V VN E E VE k NE k J k k k k bb µ b b b bb bb bb b bb bb é- - - - - - - ù êú -êú êú -- - êú= -êú êú -êú -êú êú -ëû 0 0 0 R R R a a a ¶G= ׶ a (1) 0R (2 ) 0R B 1b 2b 3b 4b 5b 6b 7b 8b 9b µ dµ ( )1b (1) 0R (2 ) 0R . CC-BY-NC-ND 4.0 International licenseIt is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted May 14, 2020. ; https://doi.org/10.1101/2020.05.10.20097121doi: medRxiv preprint 10 6. Numerical Simulation In this section, results of transmission of COVID-19 outbreak is simulated numerically, helping validate model results. Figure 4: Transmission of COVID-19 Figure 4 shows the change in the behaviour of individuals. This suggests that exposed individuals quickly progress into infected ones. Infected individuals increase in first 1.5 weeks and then start to decrease whereas critically infected individuals stay in lower numbers. Hospitalisation increases rapidly for at least 3 weeks and the it flattens. Recovery rate increases but at lower rate. Figure 5: Magnified plots of transmission of COVID-19 . CC-BY-NC-ND 4.0 International licenseIt is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted May 14, 2020. ; https://doi.org/10.1101/2020.05.10.20097121doi: medRxiv preprint 11 To get precise results, four clusters of figure 5 is magnified. Our data indicates that individuals should go into isolation earlier to prevent exposure to COVID-19. BCG vaccinated individual may get exposed in 0.2 week while , at the same time, non-vaccinated individual may get critically infected. Moreover, recovered individual also may become exposed and get re- infected after 2 weeks. Figure 6: Cyclic behaviour of recovered individuals Figure 6 indicates the cyclic behaviour of recovered individuals. The f irst graph shows vaccinated individuals while the other displays non-vaccinated individuals. The graphs show that the cycle of vaccinated individuals is larger than non-vaccinated individuals. Furthermore, vaccinated individuals have greater time to reinfection when compared to that of non - vaccinated individuals. . CC-BY-NC-ND 4.0 International licenseIt is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted May 14, 2020. ; https://doi.org/10.1101/2020.05.10.20097121doi: medRxiv preprint 12 Figure 7: Behaviour of exposed individuals Figure 7 compares the behaviour of exposed individuals with vaccinated individuals , non- vaccinated individuals , and infected individuals, respectively. The rate of exposure is consecutive for non-vaccinated individuals when compared to vaccinated individuals. Furthermore, the transmission from exposed to infected is quite quick i.e. almost every individual who is exposed will get infected. Figure 8: Behaviour of vaccinated and non-vaccinated individuals Figure 8 indicates that vaccinated individual s are infected gradually while non-vaccinated individuals are infection quickly. Consequently, non-vaccinated individuals are at higher risk of becoming infected with COVID-19. Figure 9: Behaviour of infected individual . CC-BY-NC-ND 4.0 International licenseIt is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted May 14, 2020. ; https://doi.org/10.1101/2020.05.10.20097121doi: medRxiv preprint 13 Figure 9 shows the behaviour of infected individuals towards critical, hospitalised and recovered individuals, respectively. If individual s are vaccinated early, the chances of becoming critically infection are low. However, this also indicates that infected individual s must receive treatment in hospitals before the infection progresses to critical stage. This is further elaborated by the last graph, which indicates that the chances of r ecovering by staying at home are negligible. Figure 10: Behaviour of hospitalised individuals Figure 10 shows the behaviour of hospitalised individuals and their recovery. This figure further emphasizes that individuals recover only after proper hospitalization is received. 7. Conclusion Our model indicates that a universal recommendation of BCG vaccination is beneficial in reducing the incidence rate of COVID-19. Moreover, the vaccination reduces the severity of the disease by reducing the hospitalization time for infected individuals. It also contributed to a higher rate of recovery for vaccinated individuals when compared to non -vaccinated individuals. The risk of reinfectio n is also reduced in vaccinated populations. Finally, the overall disease specific mortality of infected individuals is also reduced in vaccinated populations. Consequently, we validate the effectiveness of universal BCG vaccination policy in increasing herd immunity against COVID-19. Considering that the BCG vaccination is established in preventing acute respiratory tract infections even in the elderly [13], vulnerable populations may be immunized with BCG vaccines until a COIVD -19 specific vaccine is developed. This strategy would be especially beneficial in the new-borns, elderly, and frontline essential workers. This study validates the call for clinical trials of BCG vaccine against COVID -19. Given that our model shows a significant reduction in incidence of COVID-19 in countries with universal BCG vaccination, we recommend all countries to conduct more clinical trials to establish and consider policy regarding BCG vaccinations for its citizens. Our study may be subject to a few limitations. The study does not take into account confounding variables such as the potential differences in geographical and biological factors such as temperature, humidity, life expectancy, average income, social-cultural norms, ethnical genetic background, and mitigation between developing and developed countries. Due to these confounding variables, w e cannot establish causality. The epidemiological data comparing vaccinated countries and non -vaccinated countries must be monitored throughout this . CC-BY-NC-ND 4.0 International licenseIt is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted May 14, 2020. ; https://doi.org/10.1101/2020.05.10.20097121doi: medRxiv preprint 14 pandemic to establish a causal impact in reducing incidence and mortality. Overall, our study serves to establish that individuals with BCG vaccinations have lower risk of getting COVID- 19 infection, shorter hospital stays, and increased rate of recovery. Furthermore, countries with long-standing universal BCG vaccination policies have reduced incidence, mortal ity, and severity of COVID -19. Further research will focus on exploring the immediate benefits of vaccination to healthcare workers and patients as well as benefits of BCG re-vaccination. The

of this study will serve to inform and encourage further research and, ultimately, the creation of policy regarding universal BCG vaccination. Conflicts of Interest The authors declare that we have no competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Data Availability Our conclusion is based on the data derived from our above mathematical calculations which are available to readers. Ackhnowledgement Second author (AHS) is funded by a Junior Research Fellowship from the Council of Scientific & Industrial Research (file no.-09/070(0061)/2019-EMR-I) and First three authors are thankful to DST-FIST file # MSI-097 for technical support to the Department of Mathematics, Gujarat University.

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