Over the last century, mankind has faced a few pandemics associated with the emergence of new microorganisms, the latest being the Covid-19 pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) (Andersen et al., 2020).  As of 30th April 2020, this virus was present in 212 countries and territories (Kang et al., 2020). Pandemics occur when novel microorganisms, usually viruses emerge as a result of mutations capable of coding for new potentiator biomolecules   such   as    the    spike    glycoprotein    by SARS-CoV-2 (Giovanetti et al., 2020). Consequently, the new machinery may enable the pathogen to jump between hosts, for instance from bats to pangolins and then from the intermediate host to humans (Andersen et al., 2020). Prompt data collection of the novel organisms is critical to understanding behavioral and infectious trends of these pathogens, and in disease mitigation by generating relevant antimicrobial agents (AMA) and vaccines. Efforts to generate effective AMA for novel organisms are challenged by limited understanding of the infectious agent, and hence monitoring basic trends becomes crucial. Following the spread of Covid-19, there have been mixed and unprecedented trends, and varying findings are still accumulating. Interestingly, for instance, countries that immensely practice BCG vaccination against tuberculosis (TB), caused by a bacterium (Mycobacterium tuberculosis) that has a deleterious respiratory effect (Curtis et al., 2020), have generally reported low infection and fatality rates (Miyasaka, 2020). This has led to suggestions that BCG vaccination could be slowing or helping in the protection against Covid-19 (Curtis et al., 2020). While this is likely, in part, a consequence of under-funded health systems, it is vital to investigate the observation. Previous studies have demonstrated that the BCG vaccine has non-specific beneficial effects (Curtis et al., 2020)(Uthayakumar et al., 2018), including increased protection from a variety of respiratory infectious diseases (Prentice et al., 2015). Some of these studies have revealed possible epigenetic changes that subsequently regulate cytokine production (Uthayakumar et al., 2018)(Arts et al., 2018). Coincidentally, most Covid-19 victims die due to unregulated cytokine production.

The study sought to find a correlation between BCG vaccination coverage and Covid-19 virus cases to determine if indeed that pattern arises. It has also been argued that because of variation in geographical positioning and comorbidities of the affected people (Guan et al., 2020), pandemics tend to cluster and generally impact different parts of the world to varying extents (https://coronavirus.jhu.edu/). As such, the novel Covid-19 virus is no exception with early data showing variation in distribution to different regions of the world (Ensheng et al., 2020). Herein, how the impact and the behavioral trend of the Novel Covid-19 virus might have been influenced by geographical positioning and immunization of the populace especially against TB was discussed. The current study aimed to evaluate the relationship between BCG vaccination and coronavirus isolated strains, thus the prevalence of Covid-19.

BCG vaccination vs Covid-19 infected population

To establish if there was a correlation between Covid-19 infections and the BCG vaccination, representative countries across the globe were selected and data collected from publicly available data bases. The model countries were diversely selected to represent all the continents of the world as follows; North America (Canada, USA and Mexico), South America (Peru, Brazil and Argentina), Europe (Spain, Italy, United Kingdom and Netherlands), Africa (South Africa, Egypt, Nigeria, Kenya and Congo-Brazzaville), Asia (Russia, India, China, Saudi Arabia and Iran) and Australia. While data on Covid-19 infections was obtained from The Johns Hopkins Coronavirus Resource Center (https://coronavirus.jhu.edu/) and Google News (https://news.google.com/covid19), BCG vaccination data was acquired from BCG Atlas (http://www.bcgatlas.org/). Population data for confirmation of infected ratio was obtained from https://blog.lazd.net/coronaglobe/. Correlation analysis and graphing were performed using GraphPad Prism version 8 (https://www.graphpad.com/scientific-software/prism/).

Phylogeographic analysis

A phylogeographic analysis was performed to understand the historical and contemporary evolutionary changes of SARS-CoV-2 as it spread across the world relative to the origin, Wuhan China. The genomic data used was mined from publicly available genomic databases; Global Initiative on Sharing All Influenza Data (GISAID) (https://www.gisaid.org/ ) (Elbe and Buckland-Merrett, 2017) and https://www.ncbi.nlm.nih.gov/. The accession numbers for the two databases starts with EPI_ISL_ and MT, respectively. The nucleotide sequences utilized includes the following: MT412225 (USA-WA), MT370831 (USA-NY), MT358401 (USA-LA), EPI_ISL_429806 (Canada), EPI_ISL_424345 (Mexico), EPI_ISL_430798 (Argentina), MT350282 (Brazil), MT263074 (Peru), EPI_ISL_425565 (England), EPI_ISL_426024 (UK-Scotland), MT394864 (Germany), MT396266 (Netherlands), MT359865 (Spain), MT077125 (Italy), EPI_ISL_417941 (DRC), EPI_ISL_430819 (Egypt), EPI_ISL_413550 (Nigeria), MT324062 (South-Africa), EPI_ISL_427330 (Russia), EPI_ISL_416522 (Saudi-Arabia), MT416725 (India), MT259226 (China-Wuhan), MT253696 (China-Hangzhou) and MT007544 (Australia). In some countries such as USA and United Kingdom, several genomic data were selected for different geographical locations. In the USA for instance, genome from Washington (USA-WA), New York (USA-NY) and Louisiana (USA-LA) were identified and utilized, representing West Coast, East Coast and Southern states of USA, respectively.

Phylogenetic analysis for clades of SARCoV-2 viruses from the pandemic was performed to determine the geographic spread of different clades. This was to help reveal if there was a more dominant clade, and its distribution across the globe. This was achieved using Nextstrain (https://nextstrain.org/).

BCG vaccination vs Covid-19 infected population

Data obtained from various databases were tabulated factoring in BCG vaccination coverage vs Covid-19 infected population. (Table 1) The details included population of the candidate countries, BCG vaccinations and Covid-19 infections. Further, the Covid-19 infections data was standardized to cases per million comparisons in representative countries and represented graphically (Table   1  and  Figure  2).  Interestingly,  there  were high cases of Covid-19 infections in countries that have less BCG vaccination as compared to countries that immensely use BCG vaccine.

Phylogeographic analysis

To further understand the genetic relationship and changes in the representative countries, a phylogeographic analysis was conducted.

Following alignment of the genomic data, a phylogeographic analysis was performed using the Tamura-Nei model on MEGA X (Kumar et al., 2018)as shown in Figure 4. The alignment sequences can be accessed via https://doi.org/10.6084/m9.figshare.12246302.v1. Evolutionary history was inferred by using the Maximum Likelihood method and the Tamura-Nei model. The tree with the highest log likelihood (-41348.44) is shown (Figure 4) and  was  rooted  in SARS-CoV-2 nucleotide sequence from China-Wuhan (encircled). Initial tree(s) for the heuristic search were obtained automatically by applying Neighbor-Join and BioNJ algorithms to a matrix of pairwise distances estimated using the Tamura-Nei model, and then selecting the topology with superior log likelihood value. There were a total of 29911 positions in the final dataset. Phylogenetic analysis of SARCoV-2 viruses revealed that clade A2a was the most common both in all regions.

The Covid-19 pandemic has had a global impact (Figure 1) and a high fatality rate. By the end of April 2020, there were 3,303,296 confirmed cases and 235,290 deaths globally. Further emerging in silico predictions reflect even more infections and fatalities. However, European countries and the USA have reported the highest number of cases per one million people (Ensheng et al., 2020). India and the source of the pandemic, China, had lower cases per one million people compared to the USA, Italy, Spain, Netherlands, and UK, even though they are the most populated countries in the world (Table 1). African countries under study had hardly reported high cases of Covid-19 by the date this article was written (end of April, 2020). However, there was an initial fear that due to weaker healthcare systems, Chinese expatriates (about 2 million Chinese nationals live and work in Africa), and the associated increased travel between China and Africa for education, business and leisure, the infection rates  could be higher (Kapata et al., 2020).

Coincidentally, countries with lower cases per million people had one thing in common: BCG vaccination (Figure 2). Interestingly, Iran, a country that abandoned offering a booster BCG vaccine in 1999 (Zwerling et al., 2011), registered high cases of Covid-19 (Figure 3). It is, however, hard to postulate the impact that may have had on Covid-19 incidences. The low number of cases in China could be due to the underlying effects of BCG vaccination and/or massive efforts by the government to contain the virus. Overall, countries with low TB incidences (mostly western countries) that do not administer the BCG vaccine to the general population had the highest Covid-19 cases per million people. Countries such as England stopped the BCG vaccination in 2005 (Fine, 2005) whereas Australia abandoned its program in the 1980s due to a reduction of tuberculosis incidences (Zwerling et al., 2011). India has an expansive BCG vaccination program (Zwerling et al., 2011). The country registered 27 cases per million people by the end of April 2020. Additionally, India’s reported first case was on 1/30/2020 (Table  1),  and  by  April  5,  2020,  the 1.3 billion people in India were put into lockdown. This precaution could have added  to  the  containment  of  the virus spread. An even more interesting contrast was the low number of Covid-19 cases in the United States’ southern neighbor, Mexico; while Mexico reported 152 cases per million by the end of April 2020, the USA had 3,424 cases per million by the same date. A similar trend was seen between the USA and Argentina, a South American state with high BCG vaccination coverage.

If there is a correlation between BCG vaccination in representative countries across the world and the number of reported  Covid-19  cases  was  assessed.  The  study revealed an inverse trend between countries that immensely offer BCG vaccination to the general population and reported cases of Covid-19. Whereas the study establishes a correlation, the contribution of other factors, including but not limited to test capabilities, demographics, and disease burden need to be investigated to further augment these findings. To elucidate direct antiviral effect of BCG vaccination, molecular analysis to determine the role of BCG vaccination and potential protection against coronaviruses should also be investigated. If BCG vaccine is found to have coronavirus protection benefits, the clinical need for a BCG vaccination may need to be re-evaluated in countries that do not predominantly administer it, particularly on vulnerable and easily exposed population groups such as, the elderly, healthcare workers, people with pre-existing conditions and those with other concerning comorbidities. Consequently,    evidence    of    protection  against   viral respiratory infectious agents should underscore the need for developing countries to continue the administration of BCG vaccination promptly to offer continued beneficial effects on their populations.

The authors gratefully appreciate the authors, originating and submitting laboratories of the sequences from GISAID’s EpiFlu™ and Nextstrain Databases. They also recognize Coronavirus Resource Center at Johns Hopkins University, Google’s COVID-19 news, and BCG atlas for the provision of data to the public. Lastly, they thank Philip Alabi (Brown University, Rhode Island), Rosemary Nyamboya (St George’s University Hospital, London, UK), Courtney Mariita (Troy Prep Charter Schools, New York), Michael Ochieng’ (Pontificia Universidad Comillas, Madrid, Spain) and Jessica Gilpin (Vanderbilt University, Tennessee) for their insights and feedback.

The authors have not declared any conflict of interests.