Predicting COVID-19 transmission dynamics in the USA

In a study published in the medRxiv* server, researchers used an SEIR (susceptible, exposed, infected, recovered) type compartmental model to predict the dynamics of coronavirus disease 2019 (COVID-19) transmission in the USA.

Study: Incorporating the mutational landscape of SARS-COV-2 variants and case-dependent vaccination rates into epidemic models. Image Credit: Alexander Lukatskiy/Shutterstock

They modeled possible scenarios by varying the overall population’s adoption of non-pharmaceutical interventions (NPIs) combined with vaccine efficacy, antibody waning, and change in the predominance of the co-circulating severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants over time. They considered three key factors for this portrayal – vaccination rate, the impact of pharmaceutical interventions and NPIs, and vaccine efficacy.

The researchers calculated the transmissibility rate (β) and the vaccination rate (η) and estimated the weighted transmissibility composed of the proportion of existing strains that naturally vary over time.

Additionally, they considered time-varying vaccination rates based on the number of daily new cases. Using the past active COVID-19 case numbers for η calculations is an effective way of forecasting probable future scenarios.

Findings

The basic reproduction number (R₀) describes the transmissibility of infectious agents, and vaccinating infection-prone individuals does not reduce the R₀ value. In this study, in addition to calculating the R₀, the researchers estimated the effective reproduction number (R_e). R_e is affected by changes in immunization rate, vaccine efficacy, and NPI adoption.

In simulations, the R_e value reduced when the number of infection-prone individuals in a population decreased due to vaccination. Further, the R_e values fluctuated between 3.1953 and 0.4553 for best and worst scenarios, respectively, at 80% vaccine effectiveness, while varying η values from .0001 to .003 and the NPIs adoption rate from 0% to 80%.

Next, they simulated the scenarios of waning vaccine efficacy wherein they varied the effectiveness of the vaccine from 70% to 95%. As the vaccine efficacy increased, the number of infected individuals decreased significantly, implying that a highly effective vaccine can reduce the number of infected individuals. When the number of infected individuals decreased, more people were inclined to adopt NPIs. At NPIs adoption rate of 80%, the infections remained relatively low, regardless of vaccine effectiveness.

Conclusion and future directions

The study outlines several observations about the present COVID-19 situation in the USA using a model that dynamically tracks changes in co-circulating variants and dynamic vaccination rates.

The model captures time-varying case-dependent vaccination rates and explicitly excludes asymptomatic people from the study. Furthermore, it calculates the overall adoption of NPIs by the proportion of the population rather than suggesting the most beneficial NPIs or the efficacy of different NPI measures.

Based on the observations, the fixed transmission rate for SARS-CoV-2 transmission is obsolete, considering each variant differs in transmission potential, affecting the overall SARS-CoV-2 transmission rate over time. Consequently, tracking the changes in β values in conjunction with these changes is a useful technique to track the overall SARS-CoV-2 transmission rate at any given time.

The study findings suggest that the vaccination rate, vaccine effectiveness, and NPIs are significant factors in reducing the number of SARS-CoV-2 infected patients and controlling SARS-CoV-2 transmission. Even a slight decline in vaccination efficacy will increase the number of infected people considerably, demonstrating that vaccine efficacy is crucial for SARS-CoV-2 containment, as is the prompt delivery of the booster vaccine doses. The use of NPIs for controlling the spread of COVID-19 is crucial in a situation where viral variants cause widespread breakthrough infections, or mass vaccination campaigns fail to achieve herd immunity.

*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.