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Saturday, May 15, 2021

An Expert Explains: The complexities of herd immunity

In several metros where serosurveys showed high immunity levels, Covid cases are still surging. What does that say about the concept of 'herd immunity'? Accounting for transmission involves several factors.

Written by Tushar Gore | New Delhi |
Updated: April 30, 2021 8:42:51 am
Coronavirus, india covid second wave, Coronavirus India, Covid-19, herd immunity, India herd immunity, herd immunity risks, Complexities of herd immunity, Covid vaccine, Express ExplainedA sample being collected for a serological survey in Delhi last year. Most seosurveys have suggested high immunity levels in the metros. (Praveen Khanna/Express Archive)

We have all heard of immunity that results in a dramatic reduction of daily cases. Despite serosurveys indicating many metros such as Delhi, Mumbai, and Pune having reached high levels of immunity, the same cities are seeing an explosive surge.

Does “herd immunity” really protect from subsequent waves? The answer is not a simple yes or no. It depends on how widespread the virus is in a community and its transmission rate.

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The number of daily cases depends on three factors: The number of infectious people in the population, the number of susceptible individuals, and the rate of transmission of the virus.

The rate of transmission is dependent on the nature of the virus and the extent of contact between individuals. The herd immunity concept is based on lowering the number of susceptible individuals. If sufficient individuals in the population are immune (either through vaccination or a prior exposure), then the number of susceptible individuals drops. For example, if the immune population is 70%, then the susceptible population is 30%.

The prediction for future cases, unfortunately, is not that simple. If the rate of transmission increases (due to change in social behaviour and increased contact) then even with a large percentage of the immune population, a significant number of daily cases can result.

The Expert

Dr Tushar Gore’s focus area is pharmaceuticals. He studied at IIT-Bombay and the University of Minnesota, and has worked at McKinsey and Novo Nordisk. He is the former MD/CEO of Resonance Laboratories, a niche pharmaceuticals manufacturer.

Not a static number

A visual representation presents the concept in a more palatable, if mostly approximate, manner. Imagine a room with susceptible and immune individuals. The virus is a ball bouncing from one person to other. If it hits a susceptible person, it sticks to the person and a new case is generated. If it hits an immune individual, it bounces on to the next individual. It can bounce only a couple of bounces after which it “dies”.

In a room with a high number of susceptible individuals, and a certain number of viruses (balls bouncing around), the balls will land on many susceptible individuals and “infect” them. In contrast, in a room largely of immune individuals and with same number of balls, many balls will land on only immune individuals and “die” after a couple of bounces. Thus the number of cases in the second room will be fewer.

Estimating exposure from deaths.

The crucial point in this example is that the number of balls (virus particles) in the two scenarios are equal. If the number of balls in the second scenario is higher, then these will find more susceptible individuals and number of cases will increase. This point is often missed in herd immunity discussions.

The “herd immunity” number is not a static number but it changes depending on the rate of transmission of the virus and the extent of virus present.

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In the metros

Serosurveys indicated that Covid had touched 56% of population in Delhi by January; 75% in some slums Mumbai in November, and about 30% in Bengaluru in November.

The population touched by Covid can also be estimated by the Infection Fatality Rate (IFR). This is the total number of deaths divided by the total people infected. In India, the estimate is 0.08%. So this number can be used to back-calculate the number of infections based on the number of deaths in the different cities.

The table alongside shows the number of people exposed to Covid in some metros until January 31 using the method above. (Based on total deaths until January 31; there is a lag between infection and death and this is ignored for present discussion.) Using population estimates for 2020, the estimate for percentage of the population with prior exposure to Covid and susceptible population is calculated.

The reasons behind the recent surge are not fully understood. The one factor that is not in doubt, however, is that interaction and contact with the population has increased since February. Such increased contact increased the virus in circulation and led to increased cases in the susceptible population. As the table shows, there is still a large enough susceptible population in the metros and can lead to many new cases – as is being seen now.

Where it’s headed

The future trajectory depends on the nature of the virus causing this recent surge – in addition to behaviour patterns. If the virus, even in the form of a new strain, is not infecting the prior infected population, then the good news is that we have dealt with this situation last year and managed to control it (even though the scientific reasons behind the control are not completely understood). The implication is that current cases are only amongst the susceptible population, and, therefore, lowering activity (to levels similar to last year) will lead to control. There is some reason to believe that this is the case, based on anecdotal evidence that this wave is disproportionately affecting the higher socio-economic segments. A new virus strain indifferent to the past exposure of an individual would have resulted in all sections getting affected equally.

In the other scenario, however, the fight is much tougher and questions loom about current vaccines as effective tools to fight this strain. Then we are back to square one.

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