Stabilizing the long-term endemic behavior of COVID-19

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In a recent study posted to the medRxiv* pre-print server, a team of researchers explored the transition of coronavirus disease 2019 (COVID-19)-induced pandemic to an endemic and the corresponding endemic incidence using two modeling approaches.

They compared gradual and rapid reopening (relaxing public health restrictions) at different vaccination levels and examined how the eventual endemic state will take shape depending on the duration of immunity, the rate of importations, the efficacy of vaccines, and the viral transmissibility.

Study: COVID-19 endgame: from pandemic to endemic? Vaccination, reopening and evolution in a well-vaccinated population. Image Credit: Jennifer M. Mason/ShutterstockStudy: COVID-19 endgame: from pandemic to endemic? Vaccination, reopening and evolution in a well-vaccinated population. Image Credit: Jennifer M. Mason/Shutterstock

Previous studies have indicated that COVID-19 will eventually become an endemic rather than getting eliminated; however, some level of restrictions will be required to prevent negative outcomes such as COVID-19-related hospitalization and deaths. Moreover, governments will have to judiciously determine the correct level and appropriate speed of reopening amid the emergence of new severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants of concern (VOCs) and factoring in SARS-CoV-2 evolution while devising their COVID-19 response plan.

The study

The researchers used a classic age and contact structured model to explore the several reopening and vaccination coverage scenarios and predict and analyze COVID-19 cases at endemicity. Next, using a Susceptible-Exposed-Infectious-Recovered (SEIRS) model, they investigated the impact of vaccination efficacy, infection importation rate, waning rate of acquired immunity, and the emergence of highly transmissible variants on the COVID-19 endemic state.

They also explored how antigenic ‘drift’ and ‘shift’ compare in these two models in terms of reduction in vaccine efficacy and the resulting impact on the number of COVID-19 cases. These models reflect the pandemic to the endemic trajectory for the five million people of British Columbia, Canada (BC).


Using the age and contact structured model, the researchers explored several scenarios of gradual vs. rapid reopening of BC for over 300 days. There were fewer cumulative cases and hospitalizations under gradual reopening than under rapid reopening at baseline vaccine efficacy. Even after complete vaccine rollout, reopening would not be feasible without seeing a rise in COVID-19 cases. These findings hold at the current case decline rate of 2% per day. Also, rapid reopening would lead to 500,000 cumulative reported cases and 19,000 hospitalizations over 900 days, and gradual reopening would lead to 450,000 cumulative cases and 17,000 hospitalizations.

The researchers also explored reopening BC at 90% vaccine coverage and 70% vaccine coverage. In some modeled simulations, slower reopening led to ~60% lower peak level of incidence and 10% fewer overall infections; however, under realistic parameters, reopening when 70% of the population is vaccinated led to a high resurgence in COVID-19 cases. The estimated maximum hospitalizations for 70% vaccination and 90% vaccination were 20 and nine per 100,000, respectively, suggesting that in both the scenarios, hospitalizations did not exceed hospital capacity and remained under 40 per 100,000. Reopening at 70% vaccine coverage led to 600,000 cumulative hospitalizations, compared to 450,000 cumulative hospitalizations when reopening at 90% vaccine coverage. Thus, it is apparent that 90% vaccination coverage led to substantially fewer cases than 70% vaccine coverage.

Similar endemic behavior was observed by the age and contact structured model and the SEIR model, as evident from the frequency and peaks of multiple waves of COVID-19 cases before it eventually becomes endemic. The authors explored endemic incidences under several immunity waning regimes to understand the impact of four endemicity-determining factors: reproduction number (RNPI), immunity duration, vaccine efficacy, and importation rate. RNPI is the estimate of reproductive number for COVID-19 in the absence of social distancing restrictions, ranging from two to four for non-VOC SARS-CoV-2 and the Delta variant having a higher transmission rate than previously predominant strains increase RNPI.

With RNPI below four, reopening will not lead to a new COVID-19 wave before becoming endemic; however, at RNPI values of five or more, cases will rebound quickly to cause another wave assuming that the relative population immunity is high due to booster doses.

The endemic state is also sensitive to the duration of acquired immunity, even under continual boosting after the immunity wanes. A gradual resurgence of cases with high endemic incidences ~40 cases per 100,000 per day was observed in a simulation of reopening when RNPI = 3.5 and immunity wanes rapidly in under 1.5 years. As the duration of immunity increased (under 2-3 years), endemic incidences decreased to around five reported cases per 100,000 each day. However, if immunity waned continuously, these projections became pessimistic before stabilizing by as late as January 2023, with the occurrence of further waves of high incidences depending on the transmissibility of future predominant variants, duration of immunity, and antigenic drift. Notably, the observed long-term endemic levels were not necessarily lower than the current pandemic levels.


The study results indicate that in a scenario where there were no public health restrictions, under current vaccine efficacy and rollout plans, COVID-19 cases will rebound and a pandemic can unfold. Even under optimistic assumptions, it is anticipated that immunity is boosted, those who have recovered are not susceptible to reinfection, and VOCs for which vaccines are less effective are not circulating. During the transition from the current pandemic state to the eventual endemic state, the speed and peak of case resurgence will depend on the rate of reopening, vaccination coverage, and vaccine efficacy, as well as the transmissibility and immune escape capacity of the dominant variant at the time of reopening.

The study highlights that SARS-CoV-2 evolution is a key threat to vaccination effectiveness and ending the pandemic. While it is not 100%, current vaccine effectiveness against SARS-CoV-2 infection is high. However, as more and more people will be vaccinated, SARS-CoV-2 selection will favor immune escape, as is apparent from the sudden emergence of the Omicron variant (B.1.1.529), which has 32 mutations responsible for its increased capability for immune evasion. The study also highlights that sudden rise and fall in vaccine efficacy before settling to endemic equilibrium will be dangerous and will result in significant setbacks in the COVID-19 response.

Throughout the pandemic, hospitalization of COVID-19 cases occurred at a relatively constant rate of around 9%, and this rate has been largely unaffected by vaccination. Overall reduced severity of 80% and reduced hospitalization rate in the endemic state would still result in under 30 hospitalizations per 100,000 per day, suggesting that this would place a burden on the health care system, particularly if enhanced by seasonal infections such as influenza. Overall, the study results suggest that the evolution of SARS-CoV-2 and the nature of waning immunity will shape the relationships between infections, reported cases, and hospitalizations. The attainment of the endemic mode without risking a resurgence of COVID-19 cases is possible if restrictions are gradually lifted, vaccine coverage is maximized, and VOCs are quickly detected. In the absence of carefully planned interventions, COVID-19 may continue to cause considerable public health disruption for several years to come.

Journal reference:
Neha Mathur

Written by

Neha Mathur

Neha is a digital marketing professional based in Gurugram, India. She has a Master’s degree from the University of Rajasthan with a specialization in Biotechnology in 2008. She has experience in pre-clinical research as part of her research project in The Department of Toxicology at the prestigious Central Drug Research Institute (CDRI), Lucknow, India. She also holds a certification in C++ programming.


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