Our report disagrees with the earlier literature3 in both main and significant aspects. First, whilst the existing studies mainly focus on GDP as a single aggregate measure of health, we look at the global COVID-19 pandemic affecting almost all countries/ territories. Instead, as suggested by Cole and Neumayer, we directly see the effect of health on total human factor productivity during COVID-19 pandemic time. The authors also emphasized the impact of poor health, particularly developing regions, on total factor productivity to be negative, significant, and impactful across a wide variety of specifications18. The entire human factors occupied by the low-income countries include high rates of illness particularly infectious diseases, little food, malnutrition, unclean water, low level of sanitation and shelter, etc. Indeed, the burden of COVID-19 is not equally distributed globally. This substantial theoretical support for the argument that the impact of total human factors, especially from the low- and lower-middle-income countries, reduces the COVID-19 burden due to high immune responsiveness developed over the years. As a result, COVID-19 has resulted no large risk of infection comparatively in low income nations. As a supportive referral, several other reports, including Bloom et al., 1999; Webber, 2002, in the contribution of better health via improvement in total factor productivity also seem well suited for effective tackling against COVID-19 pandemic19.
We used linear-regression analysis to investigate how different GDP countries produced different COVID-19 susceptibility at different geographical locations. We found in the regression analysis, cases in each geography is significantly affected by its GDP since starting point Mar 2020 till small changes in end points. As pointed out in our earlier studies16,17 several intrinsic, extrinsic (or environmental), implicit and host factors attributing the pandemic, we again agree that GDP is not all alone contributor for disease progression. Therefore, one could not accomplish the desired COVID-19 severity measurement through regression equation, across different geographic and different human habitat conditions.
Both COVID-19 severity and prevalence are significantly less in the low GDP countries, including Dominica, St. Vincent, and the Grenadines, Vanuatu, etc. in comparison to high GDPs including the United States, United Kingdom etc. during first wave. Undoubtedly, rapid vaccination in high GDP countries improved pandemic counter-response during second wave. Still SARS-COV2 infectivity figures in high income (18.44%) and low-income countries (0.23%) emphasizing low GDP populations are very less susceptible (almost 1 out of 100) to pandemic. Thus COVID-19 pandemic is unequally distributed globally and currently favoring GDP-health negative interlinking. Notwithstanding with positive GDP influence, previous reports outline that 94% of the worldwide burden of disease was only attributed to low GDP counties. Hence, a major assessment represent differences in host immunogenetic factors currently shows that low GDP individuals and population groups predominates in high immune response to COVID-19. Paradoxically, the GDP is the most extensively established economic performance measure of any nation. However, a good review by Giannetti and coworkers emphasized its limitations for issues related to the environment damage; healthcare expenditures; natural resource consumption; cultural differences across different ethnic, gender, age, religion, etc.20.
Several studies highlight adaptive immunity against COVID-19 re-infection associated with memory B cells, several T cell subsets, along with antibodies protects the individual and population groups21,22. In continuation, a recent report by Ciocca and coworkers, outlines human immune system ability to remember previously encountered pathogens is acquired by experience and it changes throughout life23. With the advent of whole-genome sequencing analyses, we are also capable enough to shed light on the heterogeneity of immune responses, this is all due to different functional alleles in genes. Thus, prolonged infectious-disease, dietary, and environmental exposures of African populations to pathogens elevate allelic frequencies and confer protection against infectious diseases. Nonetheless, a study by Choudhury and co-workers highlights, strong immunity concurred in Africans against bacterial-; viral-; both bacterial and viral- infection offered via genes C5AR1 and MYH10; ARHGEF1, ERCC2, and TRAF2; IFNGR2 respectively24. Another study by Du and coworkers suggest a natural Killer cell Ig translate gene viz. KIR2DL5 that have been implicated in resistance to infections. Notably, KIR2DL5A varies in ethnic populations and Asian individuals carry the majority of the KIR2DL5-positive expressed variant25. Thus, profound natural killer cells in Asians including Chinese and Indians are concerned with direct invasion of pathogens at an early stage. As a result, both China and India with huge population are not severely affected by COVID-19 disease. In fact, Human leukocyte antigen (HLA) alleles implicated in host susceptibility or resistance to COVID-19 disease, for instance, individuals with the HLA-A*01:01 allele26 and HLA-B*46:01 allele27 were associated with increased COVID-19 risk. Authors suggested HLA genotypes may differentially induce the T-cell mediated heterogeneity of immune responses and could alter COVID-19 disease severity.
One of the most common Caucasian, Human leukocyte antigen (HLA)- A alpha chain isoform A*01:01 with accession NP_001229687.1 was used to predict regions of local similarity between sequences. The sequence analysis showed the optimal degree of similarity and identity with ALO23536.1, SPF82207.1 and A0A1W2PR61.1. The multiple alignments suggest the specific pairings ALO23536.1 (100% similarity and 100% identity), SPF82207.1 (100% similarity and 99.18% identity) and A0A1W2PR61.1 (86.81 % similarity and 82.69% identity). Most amino acids in the core structures of all 4 proteins were highly conserved excluding three regions Val10Leu (V10L), Tyr33Phe (Y33F) and Arg68Lys (R68K) of both proteins SPF82207.1 and A0A1W2PR61.1 differ with NP_001229687.1, and ALO23536.1. In addition, 23 point mutations Gln86Arg (Q86R), Glu87Asn (E87N), Met91Val(M91V), Ile121Arg(I121R), Arg138Gln(R138Q), Lys168Gln(K168Q), Ala173Thr(A173T), Val174Ala(V174A), Ala176Glu(A176E), Arg180Trp(R180W), Val182Ala(V182A), Asp190Glu(D190E), Gly191Trp(G190W), Pro208Ala(P208A), Pro217Ala(P217A), Ile218Val(I218V), Gly231Ser(G231S), Ala270Ser(A270T), Glu277Gln(E277Q), Glu299Trp(E299D), Leu300Arg(E300R), Thr345Ser(T345S), and Val358Met(V358M) were found in 4 HLA allele proteins. The highest 3-point mutations was seen in case of amino acid residues Glutamic acid (87, 277, 299), Alanine (173, 176, 270) and Valine (174, 182, 358).
All microorganisms, including SARS-COV-2 virus are evolving naturally over time, and hereby affecting its infectivity, disease severity, performance of preventive vaccines/ therapeutics/ diagnostic tools, or other public health measures. At present time, the WHO's expert group has recommended four SARS-COV-2 Variants of Concern (VOCs) i.e., Alpha, Beta, Gamma, Delta. Later ones are responsible for: (i) increase in transmissibility and virulence and (ii) decrease in effectiveness of available vaccines, therapeutics, diagnostics, or public health and social measures. This is a simplified depiction of survival and adaptation natural selection under illness-prone areas. Little food, malnutrition, unclean water, low level of sanitation and shelter, etc., have arisen over the years among low-income countries.
Strengths and limitations
Despite a huge variation in SARS-COV2 infectivity from economically strong (18.44%), less strong (4.44%), very less strong (2.64%) to weak (0.23%) countries, there is a good possibility of under-reporting of Covid-19 cases in low-income countries. Nevertheless, a strong margin of Covid-19 cases between, (i) economically strong and less strong countries, (ii) economically less strong and very less strong countries, (iii) economically very less strong and weak countries, still persist, suggesting limitations of this study may be less obvious. Undoubtedly, low GDP countries are very less vulnerable with robust immune responses during pandemic time and so before.
Hence, the result of evolutionary processes with similar functional alleles offering an immune response to COVID-19 seems an ideal fit to safeguard low GDP countries. On the other hand, the improved living standard along with much high health care spending in high income countries, allowed no adaptive challenges for the lives of the population and natural selection admits no historical fact of evolution. In other words, a causal relationship running from high GDP countries to offer a very low immune response in population during a pandemic, is no longer in much dispute.