Sampriti Roy, University of Calcutta
The concept of herd immunity states that when a large part of a population in an area has developed immunity against a certain disease, the virus or bacteria causing the disease has a significantly lesser chance of spreading from one person to another in that area. As a result, the whole community becomes protected.
So, is there a chance for the same against COVID-19? Scientists have found that there may already have been a temporary state of herd immunity in the early stages of the pandemic which was then destroyed due to change in social behaviours of people over time- something that may be a cause of the present wave that we are in.
What is the “Herd Immunity Threshold”?
For a disease to spread, a threshold proportion of a particular population must be affected by it. Now, if we see that the proportion of the population with immunity to the disease is more than the previously mentioned threshold, the spread will decline. The latter is called the Herd Immunity Threshold.
According to Nigel Goldenfeld, leader of the Biocomplexity Group at the Carl R. Woese Institute for Genomic Biology, the long term herd immunity concept doesn’t apply to COVID. “A wave of the epidemic can seem to die away due to mitigation measures when the susceptible or more social groups collectively have been infected—something we termed transient collective immunity. But once these measures are relaxed and people’s social networks are renewed, another wave can start, as we’ve seen with states and countries opening up too soon, thinking the worst was behind them,” says Goldenfeld.
Predictive modelling of COVID: Epidemiological models
According to the team at Brookhaven-UIUC (University of Illinois Urbana-Champaign) that has been carrying out various projects related to COVID-19 modelling over the past year, a good epidemiological model will not only include one timescale (called generation interval or incubation period) like that which exists for current models. The present models suggest that individual behaviours tend to remain relatively the same over a long period and the team at UIUC has made the first attempt to correct this deficiency in assumption.
Epidemiological models work by assigning each person a probability of how likely they are to infect others (social activity) and how likely they are to become infected if exposed to the same environment (biological susceptibility). Thus, to describe each group of people with different susceptibilities to disease, a multidimensional model is needed. In their work, the team at UIUC included time variations in the social activity of individuals into the existing epidemiological models. They compressed this model into only three equations and developed a single parameter called “immunity factor” to capture social and biological sources of population heterogeneity.
Reproduction number and Immunity factor
Reproduction Number: A number that indicates how transmissible an infectious disease is and specifically, how many people will be infected by one infected person. If the pool of susceptible individuals is seen to drop by 20%, so will the reproduction number.
Immunity factor: The single parameter designed to capture social and biological sources of population heterogeneity and tells us by how much the reproduction number drops in a population if susceptible individuals are removed from there.
The team looked at actual epidemic dynamics and started determining the immunity factor most consistent with data on COVID-19-related intensive care unit (ICU) admissions, hospitalizations, and daily deaths in NYC and Chicago. Eventually, they were able to extend the study to all 50 U.S. states using earlier analyses data generated by Imperial College, London.
Fairly large immunity factors developed in areas severely affected by COVID-19, where reproduction numbers were seen to fall by up to 50%. But this observation can hardly be called “good news” because according to Tkachenko (one of the authors of the study), “On a longer timescale, we estimate a much lower immunity factor of about two (~20% fall). The fact that a single wave stop doesn’t mean you’re safe. It can come back.”
How is the temporary immunity state related to people’s social behaviour?
The answer is probably known to everyone reading this. It is that people change their behaviour over time. For instance, individuals who stayed at home and followed all social distancing guidelines in day-to-day life will show an additional exposure risk once they increase any of their social activities under the impression that the pandemic is over.
Is there a way to predict future waves and their effects?
It has been found that just biological contributions to heterogeneity won’t give us universal or robust data since they only depend upon biological details. It is by incorporating social contributions that we will get more accurate data.
The scientists on the team are currently feeding statistics from “superspreader” events into the model designed by them. Upon applying it across several regions across the country (USA), overall epidemic dynamics from the end of the lockdown to early March 2021 is being attempted to be explained.
According to Tkachenko, the model could serve as a “universal patch that can be applied to conventional epidemiological models to easily account for heterogeneity.” However, it has also been stated that the prediction of future waves through the model will require additional considerations such as:
• Seasonal effects
• Geographic variabilities
• Emergence of new strains
• Vaccination levels
What must we do now?
One of the best-prescribed ways to reach herd immunity right now seems to be through vaccination, as said by Ahmed Elbanna at UIUC(Donald Biggar Willett Faculty Fellow), and we must forget about achieving herd immunity through widespread infection lest the number of people hospitalized be too high. Until most of the population is vaccinated and concrete data is available about COVID mitigation in the long run, masks and avoiding large crowds no matter what the sentiment should continue to be imposed by different governments, along with making policies that will save people’s lives as the top priority.
Also read: Gene expression System: Production of Drugs from Earthworms!!!
Source: “Time-dependent heterogeneity leads to transient suppression of the COVID-19 epidemic, not herd immunity” by Alexei V. Tkachenko, Sergei Maslov, Ahmed Elbanna, George N. Wong, Zachary J. Weiner and Nigel Goldenfeld, 8 April 2021, Proceedings of the National Academy of Sciences. DOI: 10.1073/pnas.2015972118
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