Covid-19 and Herd Immunity Without Vaccination—Teaching Modern Vaccine Dogma Old Tricks?

“In theory there’s no difference between theory and practice, but in practice there is”

Attributed to (among others) modern public philosopher, and sage, Yogi Berra*

…the epidemic continues to increase so long as the density of the unaffected population is greater than the threshold density (i.e., the threshold of “herd immunity”, or “herd immunity threshold”), but when this critical point is approximately reached the epidemic begins to wane, and ultimately to die out. This point may be reached when only a small proportion of the susceptible members of the community have been affected. [7]

William Ogilvy Kermack, 1898-1970


Dr. Angus Dagleish is a Professor of Oncology at London’s St. George’s Hospital Medical School, renowned for his seminal contributions to our understanding of the virology of HIV. [1]

Commenting in the Telegraph (London), 5/26/20, about the monomaniacal focus on vaccine development as a “panacea” for SARS-Cov2, covid-19 disease, Dagleish warned,

 “We clearly cannot rely on a (covid19) vaccine as a solution. I am unable to find a model for MERS, SARS (-Cov1) or similar (coronaviruses) where a vaccine has been effective. We need a sensible plan B.” [1a]

Indeed an Infectious Diseases and Therapy review published online 4/23/20 acknowledged the ongoing failure to develop safe and efficacious human vaccines for either SARS-Cov1, or MERS, since their emergence in 2003, and 2012, respectively. [2] Moreover, preclinical animal model evaluations of four SARS-Cov1 candidate vaccine types (including two whole virus vaccines, an inactivated virus vaccine, and a recombinant DNA spike [S-] protein vaccine), despite inducing neutralizing antibody production, and conferring protection against infection with SARS-Cov1, revealed each also caused serious immunopathologic lung injury (i.e., ”hypersensitive” Th2-type, with prominent eosinophil infiltration), upon viral challenge. [3] Identical immunopathologic “hypersensitive-type” lung injury occurred when mice were administered inactivated MERS-Cov vaccine, and challenged with infectious virus, negating the ostensible benefit achieved by their development of neutralizing antibodies. [4]

These disappointing experimental observations must serve as a cautionary tale for SARS-Cov2 vaccination programs, underscoring Dagleish’s urgent admonition to pursue a “sensible plan B” to control epidemic covid-19 disease.

Absent tenable vaccination programs, what alternative strategy might be deployed to blunt the advance of covid-19? Ironically, it is the same sine qua non goal of a successful vaccine—achieving “herd immunity.”

Although the term “herd immunity” was first coined in 1923 by Topley and Wilson [5], it only became “widely used,” as a 2011 review noted, “in recent decades,” “stimulated by the increasing use of vaccines,” “and discussions of disease eradication.” [6] Contra the vaccination paradigm of disease “eradication,” herd immunity, including its mathematical underpinnings (i.e., the 1927 Kermack-McKendrick “theorem [7],” still applied by vaccine modelers), was originally conceptualized to address this question:

“Assuming a given total quantity of resistance against a specific bacterial parasite to be available among a considerable population, in what way should that resistance be distributed among the individuals at risk, so as best to ensure against the epidemic spread of the disease, of which the parasite is the causal agent?” [5]

Topley further acknowledged, in 1926, the influence of heterogeneity in disease susceptibility upon herd immunity:

“The resistance of the herd at any given moment will be determined by the frequency distribution within it of individuals of varying orders of susceptibility.” [8]

Even the foundational modelers Kermack and McKendrick, whose seminal 1927 paper was, “limited to the case in which all members of a community are initially equally susceptible to the disease”—an assumption still used by vaccine-driven disease eradication (or near eradication) modeling [9]—emphasized:

…the epidemic continues to increase so long as the density of the unaffected population is greater than the threshold density (i.e., the threshold of “herd immunity”, or “herd immunity threshold”), but when this critical point is approximately reached the epidemic begins to wane, and ultimately to die out. This point may be reached when only a small proportion of the susceptible members of the community have been affected. [7]

S.F. Dudley, added an important observation extending the concept of herd immunity beyond prevention of symptomatic infection, to include protection against non-fatal, and certainly less morbid illness, in 1929:

“This immunity may be acquired latently, without illness, and, even if not always enough to prevent symptomatic infection, may be such that severity and fatality are decreased” [10]

Classic Kermack and McKendrick theory based on the assumption of homogeneity (in particular, equal susceptibility to disease) [7], is still invoked when estimating the herd immunity threshold (HIT) required for disease eradication, or near eradication by mass vaccination. [9] The HIT, the proportion of immunity (p) within a given population beyond which the effective reproduction number (R0) of an infection is unity (i.e., when each infected person transmits the infection, on average, to just one other person), is given by the equation: p=HIT= (R0-1/ R0). [11] This is a simplified, naïve model of a homogeneous population in which an infected individual is equally likely to infect R0 other individuals, all of whom are susceptible hosts at the outset, while it is further assumed that the entire population has the same R0 value. [11] Such (over-) simplified HIT calculation methodology, and the goal of disease-eradication/near-eradication, now frames the discussion of herd immunity vis-à-vis covid-19. [12] Calculations based upon these assumptions predict the HIT “must exceed 0.67” before the incidence of SARS-CoV2 infection will start to decline. [12]

Several investigators (11,13,14), however, have challenged the validity of reflexively applying this vaccine-based paradigm—specifically, the assumptions of homogeneity, but also, at least indirectly, the corollary goal of covid-19 eradication/near eradication. Acknowledging the self-evident variability of both susceptibility and R0 within populations, they have re-calculated HITs for covid-19 considerably below the allegedly “axiomatic” cutpoint “> 0.67.” [12] Their sound modeling methods capture real world, commonsensical host-disease interaction heterogeneity. [11,13,14] They argue, plausibly, R0 must vary, since some people are more likely than others to transmit infection due to occupation, environment, lifestyle and other factors. For instance, an infected, married healthcare worker with a family (and perhaps extended family) has a much greater potential to infect others compared to a single person working alone from home. [11] In practice, both R0 and host susceptibility are variable, and a graphical plot illustrating how this variation can profoundly lower HITs is provided below (in Figure 1.). [13]

Figure 1. Herd immunity threshold with variation in susceptibility and exposure to infection.

Reproduced from Gomes et al. [13] Vertical lines indicate coefficients of individual variation for several infectious diseases according to literature: [green] susceptibility or exposure to malaria (Amazon 1.8, Africa 2.4 ); [blue] susceptibility or exposure to tuberculosis (Portugal 2.4, Brazil 3.3); [orange) infectiousness for SARS-CoV-1 (Singapore 2.62, Beijing 2.64); [dotted black] infectiousness for SARS-CoV-2 (3.2).

Gomes et al concluded: “naturally acquired immunity to SARS-CoV-2 may place populations over the herd immunity threshold once as few as 10-20% of its individuals are immune.” [13] Separate calculations of HITs ranging from ~18% to 43%—each substantially below the dogmatically asserted value of ~70% [12]—have recently been reported. [11,14]

Independent immunological evidence is amassing [15,16,17,18] which bolsters such mathematical arguments. These data extend the discussion, appropriately, beyond the confines of generating detectable (B-lymphocyte-produced) immunglobulin “antibodies,” especially “neutralizing antibodies,” the “humoral immunity” assessed in SARS-Cov2 seroprevalence studies (for example, [19]). Additional, recently described immune responses capable of lowering the HIT by preventing SARS-Cov2 infection, and/or reducing covid-19 disease severity, include:

–The presence of cross-reactive human coronavirus antibodies [15] (i.e., induced by coronaviruses responsible for 15-30% of seasonal common colds [20]), which might lessen covid-19 disease severity

–The presence of pre-existing cell-mediated immunity (apart from “humoral/antibody”-mediated immunity), namely, SARS-Cov2 cross-reactive CD4+ T-cells, in 34% of SARS-Cov2 antibody seronegative healthy Berlin, Germany blood donors [16]

–The presence of SARS-CoV-2−reactive CD4+ T cells detected in ~40-60% of unexposed individuals (healthy U.S. blood donors whose specimens were obtained between 2015 and 2018, prior to covid-19’s emergence), suggesting cross-reactive T-cell recognition between circulating “common cold” coronaviruses and SARS-Cov2 [17]

–Evidence of cross-reactive memory T-cell-immunity, SARS-CoV2 NP (nucleocapsid)-specific, and non-structural (NSP) cross-reactive T-cells, specifically, conferred by prior infection with not only SARS-Cov1, but also common cold-causing human coronaviruses, and other “unknown coronaviruses, possibly of animal origin” in persons (healthy Singapore blood donors) unexposed to either SARS-Cov1 or SARS-Cov2. The investigators maintained: It was remarkable to find that NSP7/13-specific T cells were detected in 9 out of 18 (50%) SARS-CoV-1/2 unexposed donors”; and “Remarkably, we (also) detected NP-specific T cells in some of our SARS-CoV-1/2 unexposed individuals.” [18]

Oxford University Professor of Theoretical Epidemiology, Dr. Sunetra Gupta, during a 5/21/20 interview, provided an excellent lay summary of the limitations of antibody seroprevalence testing for ascertaining covid-19 population immunity, which alluded in general terms to the evidence adduced above:

Dr. Gupta: “Some people may not actually produce these (SARS-Cov2) antibodies. We certainly know of cases where people have tested positive (on viral antigen tests) who don’t evince an antibody response, or one that’s detectable. And there are various reasons for this. One is that if they’re lucky enough they may be able to deal with the virus at a more basic fundamental level with our (their) innate immune responses. We may also be able to fend off the virus with pre-existing responses against other coronaviruses, which I think is very likely to play a role in protection, particularly against severity of disease and death. So while the antibody tests give us an indication of how many people have been exposed, they may not tell the whole story.”

Interviewer: “So the number of cases (of infection with Covid-19) that comes out of an antibody test, in your view, is the kind of ‘low bound’ of the percentage who are likely to have been exposed. The truth lies somewhere upwards of that.”

Dr. Gupta: “That’s a very good way of putting it.” [21]

Dr. Gupta’s Oxford University colleagues at the Center For Evidence-Based Medicine, Professor Carl Heneghan and Dr. Jason Oke, predicted, 3/17/20 [22],  that accounting for “historical experience, trends in the data, increased number of infections in the population at large, and potential impact of misclassification of deaths,” the infection fatality ratio (IFR=actual covid-19 deaths/total covid-19 infections, including asymptomatic infections) for covid-19, would range between 0.1%-0.41%, very consistent with the  pandemic influenza outbreak of 1957-58 [23, 23a, 23b], and hardly “apocalyptic.” [24]

Heneghan and Oke’s predictions [22] have been validated by U.S. Centers for Disease Control and Prevention (CDC) data published 5/20/20 [25], and Stanford Professor of Epidemiology Dr. John Ioannidis’ independent pooling of  the findings from twelve studies, reported 5/19/21.[26].

The CDC data revealed an IFR for covid-19 in the U.S. ranging from 0.20%-0.27% (dependent upon asymptomatic infections comprising an estimated 50% to 35% of total infections). [25]  Ioannidis’ analysis of covid-19 IFRs from 12 seroprevalence studies, each with ≥ 500 sampled, found: 7/12 with a corrected IFR range of 0.06%-0.16%, like seasonal flu; 3/12 modestly higher, 0.25%-0.40%;  and 2/12 modestly lower, 0.02%-0.03%. [26]

Covid-19 summary IFRs in these much less than “apocalyptic” ranges [25, 26] should raise additional questions, beyond unproven safety and efficacy [2,3,4], about the urgency of vaccine development, and certainly mass deployment. The relatively benign nature of covid-19 in children, compared to seasonal influenza, should further give us pause. For example, as depicted in Table 1., below (using mortality data available through May 8, 2020), U.S. children 0-14 years old were ~7-times more likely to die from influenza, this year, despite vaccination programs, than covid-19.[27]

Table 1. Age-specific data for the U.S. showing population, estimated deaths from all and specific causes over three months, compared with COVID-19 cases and deaths from the beginning of the COVID-19 pandemic to May 8 2020 (extracted from ref.[27])

Finally, consider the laments of Oxford University vaccine researchers Professors Adrian Hill (the covid-19 vaccine project leader) [28], and Sir John Bell [29], as covid-19 cases rapidly dwindle in the U.K. A Telegraph (London) interview of Professor Hill, published 5/23/20, reported:

“Professor Hill said that of 10,000 people recruited to test the vaccine in the coming weeks – half of whom will be given a placebo – he expected fewer than 50 people to catch the virus. If fewer than 20 test positive, then the results may be useless, he warned. ‘It is a race, yes. But it’s not a race against the other guys. It’s a race against the virus disappearing, and against time,’ Professor Hill, 61, told the Telegraph from his university laboratory, long emptied by the lockdown. ‘We said earlier in the year that there was an 80 per cent chance of developing an effective vaccine by September. But at the moment, there’s a 50 per cent chance that we get no result at all. We’re in the bizarre position of wanting Covid to stay, at least for a little while. But cases are declining.’” [28]

Hill’s colleague, Oxford University Regius Professor of Medicine Sir John Bell, made the same rueful observations during an interview with The Times of London (published 5/24/20). “You wouldn’t start (trials) in London now for sure.” He added that scientists might have to “chase” the virus around the nation for the vaccine trials to be successful.[29]

Naturally-acquired herd immunity to covid-19 [11, 13,14], combined with earnest (and determined) protection of the vulnerable elderly (especially nursing home, and assisted living facility residents) [30], is an eminently reasonable and practical alternative to the dubious “panacea” [1,2,3,4,28,29] of mass vaccination against the virus.


* “In Theory There Is No Difference Between Theory and Practice, While In Practice There Is

[1] Angus Dagleish

[1a] Dagleish A. “Antibody testing will expose the folly of lockdown—The breakthrough may yet helpfully show that Ferguson and his ill-informed colleagues got it spectacularly wrong” The Telegraph (London), May 26, 2020

[2] Padron-Regelado E. “Vaccines for SARS-CoV-2: Lessons from Other Coronavirus Strains” Infect Dis Ther 2020; 9:255–274

[3] Tseng CT, Sbrana E, Iwata-Yoshikawa N, Newman PC, Garron T, et al. “Immunization with SARS Coronavirus Vaccines Leads to Pulmonary Immunopathology on Challenge with the SARS Virus.” PLOS 2012;

[4] Agrawal AS, Tao X, Algaissi A, Garron T, Narayanan K, et al. “Immunization with inactivated Middle East Respiratory Syndrome coronavirus vaccine leads to lung immunopathology on challenge with live virus” Human Vaccines & Immunotherapeutics, 2016;12:9, 2351-2356.

[5] Topley WW, Wilson GS. The Spread of Bacterial Infection. The Problem of Herd-Immunity. J Hyg (Lond). 1923;21(3):243‐249.

[6] Fine P, Eames K, Heymann DL, “Herd Immunity”: A Rough Guide, Clinical Infectious Diseases, 2011; 52: 911–916,

[7] Kermack WO, McKendrick “A contribution to the mathematical theory of epidemics” Proc R Soc London 1927; 115: 700-7217

[8] Topley WW. “THE SECOND (1926) Milroy Lecture ON EXPERIMENTAL EPIDEMIOLOGY” The Lancet 1926; 207: 531-537.

[9] Anderson R., May R. “Vaccination and herd immunity to infectious diseases. Nature 1985; 318: 323–329

[10] Dudley SF. “Human Adaptation to the Parasitic Environment” Proc R Soc Med. 1929;22:569‐592.

[11] Brennan PV, Brennan LP. “Susceptibility-adjusted herd immunity threshold model and potential R0 distribution fitting the observed Covid-19 data in Stockholm” medRxiv 2020.05.19.20104596; May 22, 2020.

[12] Randolph HE, Barreiro LB. “Herd Immunity: Understanding COVID-19” Immunity. 2020;52:737‐741.

[13] Gomes MGM,  Corder RM,  King JG,  Langwig KE, et al. “Individual variation in susceptibility or exposure to SARS-CoV-2 lowers the herd immunity threshold” medRxiv 2020.04.27.20081893; May 21, 2020

[14] Britton T, Ball F, Trapman P. “The disease-induced herd immunity level for Covid-19 is substantially lower than the classical herd immunity level” arXiv:2005.03085v1 May 6, 2020

[15] Ng K, Faulkner N, Cornish G,  Rosa A, et al. “Pre-existing and de novo humoral immunity to SARS-CoV-2 in humans” bioRxiv 2020.05.14.095414; May 15, 2020

[16] Braun J, Loyal L, Marco Frentsch M, Wendisch D, et al. “Presence of SARS-CoV-2 reactive T cells in COVID-19 patients and healthy donors” medRxiv 2020.04.17.20061440; April 22, 2020

[17] Grifoni A, Weiskopf D, Ramirez SI, Mateus J. “Targets of T Cell Responses to SARS-CoV-2 Coronavirus in Humans with COVID-19 Disease and Unexposed Individuals” Cell (in press) 2020;

[18] Le Bert N, Tan AT,  Kunasegaran K, Tham CYL, et al. “Different pattern of pre-existing SARS-COV-2 specific T cell immunity in SARS-recovered and uninfected individuals” bioRxiv 2020.05.26.115832; May 27, 2020

[19] Streeck H, Schulte B, Kuemmerer B, Richter E, et al. “Infection fatality rate of SARS-CoV-2 infection in a German community with a super-spreading event” medRxiv 2020.05.04.20090076; June 2, 2020

[20] Mesel-Lemoine M, Millet J, Vidalain PO, et al. “A human coronavirus responsible for the common cold massively kills dendritic cells but not monocytes” J Virol. 2012; 86:7577‐7587.

[21] “Oxford Epidemiologist Dr Sunetra Gupta on Covid-19 Immunity Due to Prior Coronavirus Infection, Etc.” Video clip: Extracted from this full Unherd Interview:

[22] Oke J, Heneghan C. “Global Covid-19 Case Fatality Rates” March 17, 2020

[23] The 1957-58 influenza A H2N2 virus pandemic, and its overall U.S. mortality, and CFR, provide useful benchmarks for comparison. This pandemic also originated, like SARS-Cov2, in China. Dunn FL. “PANDEMIC INFLUENZA IN 1957. REVIEW OF INTERNATIONAL SPREAD OF NEW ASIAN STRAIN” JAMA. 1958;166:1140-1148. 10.1001/jama.1958.02990100028006

[23a] The CDC estimated that just over 25% of the U.S. population then (census of ~ 170 million), some 45 million persons, were rapidly infected with the new virus between October, and November, 1957. Trotter Jr. Y, Dunn FL, Drachman RH, Henderson DA, et al.  “ASIAN INFLUENZA IN THE UNITED STATES, 1957–1958” Am J of Epidem, 1959; 70, 34-50.

[23b] Despite a vaccination program, there were ultimately 116,000 deaths, a resulting pandemic H2N2 influenza A CFR of 0.26% (116,000 deaths/45,000,000 infections). CDC: “1957-1958 Pandemic (H2N2 virus)”

[24] Ferguson N, Laydon D, Gilani GN, et al. Report 9: Impact of non-pharmaceutical interventions (NPIs) to reduce COVID19 mortality and healthcare demand. Imperial College London, 16 March 2020.

[25] “COVID-19 Pandemic Planning Scenarios” CDC, May 20, 2020

[26] Ioannidis J. “The infection fatality rate of COVID-19 inferred from seroprevalence data” medRxiv 2020.05.13.20101253; May 19, 2020

[27] Bhopal S, Bagarai J, Bhopal R. “Children’s mortality from COVID-19 compared with all-deaths and other relevant causes of death: epidemiological information for decision-making by parents, teachers, clinicians and policymakers” Public Health (in press). Available online May 30, 2020

[28] Gardner B. “Oxford University Covid-19 vaccine trial has 50 per cent chance of ‘no result’—Project leader Prof Adrian Hill warns against ‘over-promising’, as vaccine success is far from guaranteed” Telegraph (London), May 22, 2020

[29] Meddings S. “Coronavirus researchers may have to ‘chase’ infections to do vaccine tests—Universities and drug companies warn that the falling number of infections in the UK is impeding efforts to develop a cure for Covid-19” The Sunday Times (London), May 24, 2020

[30] Girvan G, Roy A. “Nursing Homes & Assisted Living Facilities Account for 42% of COVID-19 Deaths—A startling statistic has profound implications for the way we’ve managed the coronavirus pandemic” May 7, 2020; updated May 22, and June 2, 2020.


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