Transmission dynamics reveal the impracticality of COVID-19 herd immunity strategies

Proc Natl Acad Sci U S A. 2020 Oct 13;117(41):25897-25903. doi: 10.1073/pnas.2008087117. Epub 2020 Sep 22.


The rapid growth rate of COVID-19 continues to threaten to overwhelm healthcare systems in multiple countries. In response, severely affected countries have had to impose a range of public health strategies achieved via nonpharmaceutical interventions. Broadly, these strategies have fallen into two categories: 1) "mitigation," which aims to achieve herd immunity by allowing the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus to spread through the population while mitigating disease burden, and 2) "suppression," aiming to drastically reduce SARS-CoV-2 transmission rates and halt endogenous transmission in the target population. Using an age-structured transmission model, parameterized to simulate SARS-CoV-2 transmission in the United Kingdom, we assessed the long-term prospects of success using both of these approaches. We simulated a range of different nonpharmaceutical intervention scenarios incorporating social distancing applied to differing age groups. Our modeling confirmed that suppression of SARS-CoV-2 transmission is possible with plausible levels of social distancing over a period of months, consistent with observed trends. Notably, our modeling did not support achieving herd immunity as a practical objective, requiring an unlikely balancing of multiple poorly defined forces. Specifically, we found that 1) social distancing must initially reduce the transmission rate to within a narrow range, 2) to compensate for susceptible depletion, the extent of social distancing must be adaptive over time in a precise yet unfeasible way, and 3) social distancing must be maintained for an extended period to ensure the healthcare system is not overwhelmed.

Keywords: dynamical systems; infectious diseases; mathematical modeling.

Publication types

  • Research Support, N.I.H., Extramural

MeSH terms

  • Age Factors
  • Betacoronavirus / physiology
  • COVID-19
  • Communicable Disease Control / methods
  • Computer Simulation
  • Coronavirus Infections / epidemiology
  • Coronavirus Infections / immunology
  • Coronavirus Infections / prevention & control
  • Coronavirus Infections / transmission*
  • Disease Susceptibility / epidemiology
  • Disease Susceptibility / immunology
  • Humans
  • Immunity, Herd*
  • Models, Theoretical*
  • Pandemics / prevention & control
  • Pneumonia, Viral / epidemiology
  • Pneumonia, Viral / immunology
  • Pneumonia, Viral / prevention & control
  • Pneumonia, Viral / transmission*
  • SARS-CoV-2
  • United Kingdom / epidemiology