A simulation approach for COVID-19 pandemic assessment based on vaccine logistics, SARS-CoV-2 variants, and spread rate

Introduction

COVID-19 emerged in Wuhan (China) in late 2019 and had been spreading worldwide. In March 2020, the World Health Organization (WHO) declared a pandemic for the new COVID-19.

Despite advances in clinical care for the coronavirus pandemic, population-wide interventions were vital to effectively manage the pandemic due to its rapid spread and the emergence of different variants.

One of the most important interventions to control the spread of the disease is vaccination. In this study, an extended Susceptible-Infected Healed (SIR) model based on system dynamics was designed, considering the factors affecting the rate of spread of the COVID-19 pandemic.

Simulation model

System dynamics is a modeling technique applied to understand how complex and dynamic systems change over time. The strength of the methodology of system dynamics lies in the way it analyzes the impact of information feedback on decision-making in a complex system.

The designed simulation model was modeled using AnyLogic. The model was performed for three different vaccine supply scenarios and for Turkey with ~83 million population.

The model could predict how long it would take to reach 70% herd immunity based on the number of vaccines administered. The model’s equations were simple and could serve for predictions to be made by non-expert operators.

The stock flowchart of the system dynamics model, which included the strategies that could be developed and the vaccination policies that could be followed to prevent the COVID-19 epidemic, is shown below. The process consisted of several stages including vaccination distribution and administration.

Stock flow diagram of system dynamics simulation model in AnyLogic

Stock flow diagram of system dynamics simulation model (click to enlarge)

To better understand how much the dynamics and factors in the model affected herd immunity, which was the main goal of the model, a fishbone diagram of the system dynamics model of the problem is shown below.

Fishbone diagram of system dynamics simulation model

Fishbone diagram of system dynamics simulation model (click to enlarge)

Furthermore, the system dynamics model was used quantitatively to test the social immunization of the COVID-19 vaccine. First, a causal loop diagram was created, and qualitative modeling was used. Subsequently, the system dynamics model was used quantitatively.

Results

The results showed that, with a monthly supply of 15 million vaccines, social immunity would reach the target value of 70% in 161 days, compared to 117 days for 30 million vaccines and 98 days for 40 million vaccines.

Scenario 1: it would take 165 days for herd immunity to reach 70% under scenario 1 conditions.

Time graph for scenario 1 (15 million)

Time graph for scenario 1 (15 million)

Scenario 2: it would take 120 days for herd immunity to reach 70% under scenario 2 conditions.

Time graph for scenario 2 (30 million)

Time graph for scenario 2 (30 million)

Scenario 3: it would take 101 days for herd immunity to reach 70% under scenario 3 conditions.

Time graph for scenario 3 (40 million)

Time graph for scenario 3 (40 million)

Below you can see the results from three different scenarios applied to the model.

Scenario results of the simulation

Scenario results of the simulation

The simulation results demonstrated the benefits of increased vaccine supply in shortening the time required to achieve herd immunity. As the number of vaccines administered increases, there is a noticeable decrease in the number of days required to gain 70% herd immunity, emphasizing the critical role of effective vaccination logistics in pandemic response.

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