New mathematical model identifies role of super-spreaders in SARS-CoV-2 variant emergence

Coronavirus disease 2019 (COVID-19) – caused by the highly infectious severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pathogen – has claimed over 2.7 million lives worldwide. On the 11th of March 2020, the World Health Organization (WHO) declared the rapid worldwide spread of COVID-19 a global pandemic.

Scientists around the world are fighting hard to learn more about the virus, which is essential for containing the pandemic. Scientists and public health authorities across the globe are concerned over the emergence and regional predominance of novel SARS-CoV-2 variants that exhibit higher infectivity and, in some cases, heightened virulence.

Researchers have identified several SARS-CoV-2 variants that have resulted from mutations in the virus’s genomic region that encodes the viral spike protein. The rate at which mutations are occurring has exceeded the predictions of previous phylogenetic surveys. Scientists believe that immunocompromised hosts are the reservoirs of these variants. People with weakened humoral or cell-mediated immune function have a tendency to contain high viral load for a longer period, which may be several weeks. However, it is unclear as to why some variants eventually become dominant.

The rate of infection of the new variants is extremely high and has a tremendous global impact on SARS-CoV-2 epidemiology. Researchers found that B.1.1.7 variant has a higher virulence and infection rate than the baseline variants. However, two other variants, i.e., B. and P.1, have shown potential to escape the immune responses from current vaccines and natural infection. The actual number of epidemiologically important variants is not accurate owing to the lack of sequencing facilities in many parts of the world.

Scientists believe that in the future, these variants of concern (VOC), namely, B.1.1.7, B., and P.1, may undergo further significant evolutionary changes and, thereby, new characteristics may develop.

In a new study, a team of researchers have developed a new mathematical model which helps characterize factors responsible for the emergence of new variants and their predominance. They have published their research on the medRxiv* preprint server.

Several models such as the dose-response model, transmission model, reproduction number, and the SARS-CoV-2 within-host model are associated with this study. Even though a higher rate of infection is a significant indicator of a variant’s ultimate prevalence in a population, bad luck is another significant factor that plays a vital role in variant domination in a population. The above-mentioned mathematical model helps identify this luck factor.

It was noted that stochastic burnout commonly occurred when a pathogen (e.g., virus, bacteria, fungi, etc.), with a reproductive number between 1 and 2, was introduced into a population. The probability of stochastic burnout increased in conditions where the rate of secondary infection was over-elevated, when compared to the primary infection. The model revealed that all new variants with higher infectivity emerged from immunocompromised individuals who do not transmit significantly in the population. Thereby, this model puts forward a thought-provoking hypothesis suggesting human coronaviruses (SARS, MERS, and SARS-CoV-2), which have the potential to cause a pandemic, are introduced into the human population only as a matter of chance.

The mathematical model developed in the current research revealed that variants that have established their prevalence, are due to early super spreader events. Such a phenomenon is relatively rare; however, the dominance of these variants increases during a local outbreak. These super-spreader episodes provide a great escalation in the growth rate of a potent variant, as a result of which, it rapidly establishes its predominance. During such events, these variants bypass the linear growth phase, which would eventually land them on the epidemic growth curve, which can be predicted more accurately.

The current research also highlights the importance of non-pharmaceutical interventions (NPIs) in preventing large super-spreader events. Such events can be avoided by preventing large indoor gatherings, implementation of finest quality facemasks (K95 or N95) in places where gatherings cannot be avoided, and an increase in ventilation in the indoor workplace and schools. These precautionary measures effectively limit super-spreader events and help lower the chances of episodes where a variant with high infectivity initiates a rapid epidemic. Such incidents have occurred in many regions during the pandemic, (e.g., South Korea and Boston in the U.S).

Researchers have revealed some of the limitations of the mathematical model. The qualitative predictions of the model are very robust; however, it cannot estimate the outbreak size associated with a new variant introduced in a population. This is because of unknown missing parameters. For example, the percentage of immunocompromised hosts varies greatly across the population. Further, the number of secondary infections associated with new variants may also differ because of unpredictable factors that lead to secondary infection. Another limiting factor is that the administration of NPIs varies considerably among regions and over time. As the model does not account for these factors, its predictions are phenomenological only.

*Important Notice

medRxiv publishes preliminary scientific reports that are not peer-reviewed and, therefore, should not be regarded as conclusive, guide clinical practice/health-related behavior, or treated as established information.

Journal reference:
Dr. Priyom Bose

Written by

Dr. Priyom Bose

Priyom holds a Ph.D. in Plant Biology and Biotechnology from the University of Madras, India. She is an active researcher and an experienced science writer. Priyom has also co-authored several original research articles that have been published in reputed peer-reviewed journals. She is also an avid reader and an amateur photographer.


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