CME INDIA Presentation by Dr Keyur Acharya, Intensivist, Critical Care Unit, Royal United Hospital, Bath, Somerset, UK.
There has been a lot of talk about herd immunity in current pandemic and such approach has been contemplated by various governments at various stages. Observations so far from Brazil, Sweden and US tells us that this is not practical and sustainable. Understanding the context of herd immunity in COVID-19 helps us understand why we have needed unprecedented lockdown of population across countries.
Real Hope is Herd Immunity
Herd immunity is defined as achieving a level of immunity in population at which spread of the disease will slow down and will stop even after all preventative measures are relaxed. This understanding comes from the mathematical models of vaccination. However, in this context efficacy of vaccine is known and the assumption is that the level of vaccination will be close to 100%.
Let us try to explore this concept further using some hypothetical scenarios.
- Cost of Herd immunity:
Conventionally it is calculated as hc= 1-1/Ro where hc is herd immunity and Ro is reproduction number. Ro is average number of new infections caused by the infected individual in outbreak of a disease. Ro for measles is calculated to be 12-18 meaning thresholds for herd immunity being 92% – 95%. Now the assumption of COVID- 19 reproduction number is 2.5. University of Oxford’s COVID-19 Evidence Service Team estimates this to be 2. 63. However, estimates vary between 0.4 and 4.6.(1) So, calculating for different values we get following thresholds.
Table 1: Herd Immunity threshold and reproduction number
|Ro||Threshold for Immunity in population|
Considering this in perspective of Indian population (138,000,000 as per world population review) with calculated death rates of 1.8% (2) we get staggering numbers.
Table 2: Impact of herd immunity on population
|Ro||Threshold for immunity||How many infections needed||Estimated deaths (1.8%)|
|2||50%||690,000,000 (69 crores)||1,242,000 (1.03 crores)|
|2.5||60%||82,800,000 (83 crores)||1,245,000 (1.25 crores)|
|3||67%||92,460,000 (92 crores)||1,380,000 (1.38 crores)|
Crude estimates which is unlikely to simulate real life situations
These are crude estimates which is unlikely to simulate real life situations. But it gives us perspective in numbers how dangerous and unethical this approach (if depending on herd immunity only and allowing normal life) would be. But these calculations have some limitations.
Assuming the static model of the spread of the virus
We have assumed that Ro would remain constant and everyone is equally likely to be affected by COVID-19 that rate of transmission would remain same. All these calculations assume the static model of the spread of the virus. Limitations of these assumptions are:
- Population density is different
Herd immunity threshold will be different depending on population density. Rural areas might have more population density than urban area. Rate of infectivity, availability and accessibility of health infrastructure will be different especially in an extremely diverse country like India.
- Heterogeneity of Susceptibility
Efficacy of vaccination can be predicted with reasonable certainty as can vaccination uptake in population. This makes it easy to calculate threshold. There is also an underlying assumption that everybody is equally likely to get infected.
But in natural infection especially when it is ongoing, many other factors would influence this threshold. Social behaviour (social distancing, use of masks, handwashing etc.), biological differences, government policies and public trust in these policies and adherence.
- Estimation of R number considering heterogeneity would be different. This is difficult to achieve and several investigators have tried this. In June 2020, the journal SCIENCE (4) published a study that incorporated this concept of heterogeneity and estimated the herd immunity of COVID 19 to be at 43%. But one of the authors. Tom Britton thinks that this model has not accounted some additional sources of heterogeneity. Other group estimates that this threshold for naturally acquired immunity will be as low as 20% of the population. If that is the case, world’s hardest hit places in the world may be nearing it. However, many of these studies are not peer reviewed and are not considered reliable. It is fair to say that there is too much uncertainty around the whole concept of herd immunity in this situation.
Peak of Epidemic
At the peak of the pandemic, virus doubling time has been shown to be 3 days (3) in various countries. Exponential quantities have characteristics interval over which quantity doubles. For examples, consider an epidemic where cases double every 3rd day. In 30 days, there will be 1000-fold increase. This means that 10 doublings increase by 1000-fold in 30 days. How many doublings will it take to reach a million-fold increase? A million is thousand thousand. So, it will take 10 doubling to become thousand thousand which gives us this number 60 day.
- Projection on Health infrastructure:
Tiyagi et al (5) estimated using ARIMA model and auto ARIMA model requirements for health care facilities. They estimated ICU beds to be 10%, ventilators to be 5% and isolation beds to be 85% of active cases. Again, calculating based on our assumptions.
Table 3: Impact on health infrastructure in India
|Ro||Active infections (Crores)||ICU beds needed (Crores)||Ventilators needed (Crores)||Isolation beds needed (Crores)|
So even going by these crude estimates, mortality, demands on health structure would be huge and possibly never be met realistically. Also, we need to consider the impact of such demands on health care workers and their families. There will be significant rise in people battling long term effects like lung scarring some of which at lease will be symptomatic, possible cognitive effects (as shown by long term studies on ARDS) , pulmonary HT,Chronic thromboembolic disease and many more.
Experiences of a Brazilian city
Experiences of Brazilian city of Manaus , Swedish approach and US approach have taught us that this is not practical and sustainable. This has led to catastrophic loss of human lives without necessarily going back to normality or previous level of functioning. Lancet (6) called the approach of herd immunity as “dangerous fallacy unsupported by scientific evidence.”
Lockdown helps in flattening this curve and may take a long time in achieving this level of immunity but does help in short term by slowing down the number of new infections and death rates and allowing health systems to prepare and plan. Understandably, there is going to be economic, social cost to it.
What about Vaccine induced Herd Immunity?
Much of the early theoretical work on vaccine induced herd immunity was around the formula Vc= 1- 1/Ro where Vc is the critical minimum proportion to be vaccinated assuming the efficacy of the vaccine to be 100%. However, real life shows much more complexities like imperfect immunity, non-random vaccination, heterogenous population, freeloaders and other factors. As vaccine efficacy is never 100%, we need to factor in its efficacy and rearranged the formula.
If the vaccine efficacy if 100%, Vaccine factored formula will be same as herd immunity. If we assume E to be 0.8(80%), herd immunity needed would be
Now for our hypothetical scenarios
|Ro||Herd immunity with vaccine efficacy of 0.8|
However, limitation of these calculations is that in constantly changing dynamics of COVID -19, it will be impossible to come to any conclusions in a meaningful manner.
Natural herd immunity has huge cost in terms of mortality and health infrastructure resources.
Pragmatically, it is extremely difficult to rely on it and equally difficult to ascertain in a meaningful manner until this pandemic is over. This will also leave many people disabled and will have colossal social, financial impact. Going by experiences so far, we have not seen any evidence that it works in a predictable manner and in no way, it allows us to return to previous level of functionality.
Vaccine induced immunity is achievable
Vaccine induced immunity is achievable of if we can mobilise resources in timely and effective manner and given the scale of problem clearly it is going to be a huge challenge.
While waiting for effective vaccine we carry on maintain simple public health measures like social distancing, masks, handwashing, judicious use of drugs and health care facilities.
- COVID 10: What is the R number? BMJ 2020;369:m1891(published on 12th May 2020)
- Is India missing COVID-19 deaths? Patralekha Chatterjee Lancet Volume 396, issue 10252 , p657. Sept 05,2020
- Challenges in control of Covid-19: short doubling time and long delay to effect of interventions Pellis et al; medRxiv pre print 31st March 2020 arXiv:2004.00117 [q-bio.PE]
- A mathematical model reveals the influence of population heterogeneity on herd immunity to SARS- CoV 2 Tom Britton et al Science Vol 369, issue 6505, pp 846-849
- COVID 19: Real time forecasts of Confirmed cases, Active cases and health infrastructure requirements for India and its majority states affected states using the ARIMA model. Tyagi et al medRxiv doi: https://doi.org/10.1101/2020.05.17.20104588
- Scientific consensus on the COVID 19 pandemic: we need to act now Alwan et al Lancet volume 396, issue 10260, E71-E72, October 31 2020.
CME INDIA Tail Piece
- Manaus, the capital of Amazonas state, has recorded about 51,000 cases of COVID-19 and more than 2,500 deaths from its population of less than two million.
- Transmission of SARS-CoV-2 in Manaus, located in the Brazilian Amazon, increased quickly during March and April. It declined more slowly from May to September.
- In June, one month following the epidemic peak, 44% of the population was seropositive for SARS-CoV-2, equating to a cumulative incidence of 52%, after correcting for the false-negative rate of the antibody test.
- The seroprevalence fell in July and August due to antibody waning. After correcting for this, a final epidemic size of 66%.
- Herd immunity played a significant role in determining the size of the epidemic.
Source: medRxiv 2020.09.16.20194787; doi: https://doi.org/10.1101/2020.09.1