SARS-CoV-2 infection in India bucks the trend: Trained innate immunity?

Abstract

SARS-CoV-2, the causative agent of COVID-19 pandemic caught the world unawares by its sudden onset in early 2020. Memories of the 1918 Spanish Flu were rekindled raising extreme fear for the virus, but in essence, it was the host and not the virus, which was deciding the outcome of the infection. Age, gender, and preexisting conditions played critical roles in shaping COVID-19 outcome. People of lower socioeconomic strata were disproportionately affected in industrialized countries such as the United States. India, a developing country with more than 1.3 billion population, a large proportion of it being underprivileged and with substandard public health provider infrastructure, feared for the worst outcome given the sheer size and density of its population. Six months into the pandemic, a comparison of COVID-19 morbidity and mortality data between India, the United States, and several European countries, reveal interesting trends. While most developed countries show curves expected for a fast-spreading respiratory virus, India seems to have a slower trajectory. As a consequence, India may have gained on two fronts: the spread of the infection is unusually prolonged, thus leading to a curve that is "naturally flattened"; concomitantly the mortality rate, which is a reflection of the severity of the disease has been relatively low. I hypothesize that trained innate immunity, a new concept in immunology, may be the phenomenon behind this. Biocultural, socioecological, and socioeconomic determinants seem to be influencing the outcome of COVID-19 in different regions/countries of the world.

FIGURE 1

Innate and adaptive immunity against a virus explained. A, Innate immunity comprises of mechanisms to recognize viral intermediates and respond by secreting IFNs. B, Innate immune cells like macrophages and DCs are also the link to the adaptive immunity that ultimately is responsible for clearing the virus infection. NK cells monitor for virus‐infected cells and secrete type II IFN, IFN‐γ besides actively killing the infected cells (Brandstadter & Yang, 2011)

FIGURE 2

A comparison of COVID‐19 morbidity and mortality data for India and nine other developed countries. A, Number of daily cases (blue line; 7‐day moving average) is plotted along with the daily deaths (red line; 7‐day moving average) reported due to COVID‐19. The day of the first reported case of COVID‐19 in each country was selected as the initial data point and deaths and cases until Jul 24, 2020 are shown. The number of days shown below the X‐axis indicates the total number of days past since the first recorded case. The number of days shown above the X‐axis indicates the number of days past the peak in daily deaths based on a 7‐day moving average. B, Number of daily cases (7‐day moving average) was divided by the number of daily deaths and plotted. The initial part of the curve for almost all countries except India shows a “head” and the later part a “tail”. C, The actual daily cases (dark line) show an increasing trend but when it was normalized for the number of tests done during this period (Mar 20, 2020‐Jul 18, 2020) the curve loses its slope (gray line); note: the adjusted curve may not show actual daily death numbers, but is only shown to compare the slopes of the curves. D, Time in days each country took to reach the first 10, 20, etc. deaths. The data on daily testing, daily cases, and daily deaths were obtained from ourworldindata.org

FIGURE 3

India shows a less severe COVID‐19 likely because it has more trained hosts due to endemic exposure to intracellular pathogens. A, A simplistic mortality curve model based on data for COVID‐19 considering the nature of the hosts that are succumbing to infections (Huang et al., 2020) is drawn. Based on the data from He et al. (2020), a presymptomatic transmission is assumed (a 100% transmission is an assumption in the model, even though He et al. (2020) have shown it as 44%). The resistance of the hosts increases over time in the mortality curve as shown. B, The curve of daily deaths for the United States and India. The data for each country begins from the day it had its first death and goes till the next 134 days. The 134 days was chosen since India had passed that many days from the day of its first death due to COVID‐19, till Jul 24, 2020. C, Trained immunity could also be due to exposure to endemic intracellular pathogens. The Indian hosts get a constant exposure to infections from M. tuberculosis, arthropod‐borne RNA viruses, and protozoan parasites and develops a trained immune system that protects them from a severe disease from a new but related pathogen like SARS‐CoV‐2

FIGURE 4

The COVID‐19 curve can be flattened by having more resistant hosts in the population that can halt the virus from reaching its critical mass rapidly. Populations that have more resistant hosts will prolong the community spread time and lead to a natural flattening of the curve. The gain from such naturally flattened curves is that the burden on public health care provider system will be less; however, the loss is that such populations may become endemic to such infections (Banerjee, 2020b)

FIGURE 5

India may have escaped a severe COVID‐19 disease due to trained innate “herd” immunity. A, Presumptive models to explain COVID‐19 deaths in India. Model A presumes high mortality similar to the one seen in developed countries, but inadequate testing for SARS‐CoV‐2 in India has not captured a significant part of the mortality. A country with daily deaths of >27 000 due to all causes (upper thin dashed line) and >1200 due to other infectious diseases (lower thin dashed line), will have to have a large increase in death rate above and over these figures to be detected as a genuine signal from new causes. Model B presumes that COVID‐19 was present sometime before the testing began in India and had its peak some time ago, but went undetected in the background of other deaths involving comorbidities and infections. Model C presumes that SARS‐CoV‐2 has not yet reached its critical mass in the Indian population and mortality is currently showing its peak or the peak is yet to come. (Black solid curve‐actual daily mortality due to COVID‐19; Blue curve‐daily mortality due to other infections; Red solid line‐COVID‐19 daily mortality rate confirmed by diagnostic testing). The endemicity model presumes that COVID‐19 may become a perpetual infection of a low grade similar to other viral infections like Dengue in the Indian population; there may be several smaller peaks (solid line) or a plateau (thick dashed line). B, Innate 'herd' immunity may be an important protective factor in pandemics. Trained immunity so far has only considered the BCG vaccine as a likely primer for boosting immunity against SARS‐CoV‐2. Here, I propose that endemic pathogens like other RNA viruses, M. tuberculosis (as latent infections) and others may have boosted the innate immunity of the Indian population that may be offering protection to a large majority of people from severe COVID‐19. Whether an innate 'herd' immunity factor is functioning similar to the concept of the classical herd (immunity) effect, needs further investigation

FIGURE 6

Geographical, socioecological, and socioeconomic factors may be influencing COVID‐19 outcome in different regions/countries of the world. A, Daily death curves showing different trajectories in different continents of the world. B, The daily death curves show striking differences in countries grouped according to their income status showing a negative correlation with the slope of the curves. C, The daily case curves, however, do not show a similar negative correlation with income. D, Lesser number of deaths occurs for a given number of cases in countries with lower incomes compared to the ones with higher incomes. Slopes were calculated for the daily case curves and death curves and ratios are plotted for different groups of countries (LI, low income; LMI, lower middle income; HMI, higher middle income; HI, high income). All the data shown in this figure was from Dec 31, 2019 to Jul 24, 2020. All data were obtained from ourworldindata.org