Akademik Veri Yönetim Sistemi
40. Yöneylem Araştırması ve Endüstri Mühendisliği Kongresi, İstanbul, Türkiye, 4 - 07 Temmuz 2021, ss.98-99
In this study, the goal is to compare double and single-dose
vaccination policies. What-if analyses for various vaccine efficacies and age group prioritization strategies are conducted.
Interpretations for scenarios such as different population demographics and vaccine availability are explored using agentbased modeling and simulation. Many countries have been
conducting various vaccination studies against the Covid19
virus, which emerged in December 2019 and has affected
the whole world. Vaccines are known to be the most effective way to manage pandemics. At the end of 2020 multiple vaccines for Covid19 have been discovered. In general,
Covid19 vaccines are applied in two doses. Vaccines provide protection starting from the first dose; nonetheless it
becomes fully effective after the second dose. With a single dose application, more people can be covered however
with a double dose application higher protection is provided.
Thus, in countries with limited vaccine resources, single-dose
application strategy can be considered. Around the world,
different countries follow somewhat similar policies for vaccination rollout. Commonly, senior citizens are given priority
and receive double-dose of vaccines. On the other hand, it
is argued that younger individuals spread the disease more.
Therefore, prioritizing to vaccinate super spreaders could be
more effective to take the pandemic under control. As of
today, there are still discussions about how benefits from
vaccines can be optimized. Furthermore, it is expected that
Covid19 vaccinations will be required to be applied annually. It can be concluded that, determining the best vaccine
policy is still a recent and important problem. In real life,
comparing the results of several policies is not possible as
it has real consequences. Under these circumstances, modeling and simulation techniques are effective tools. Among
different simulation methodologies, this study considers an
agent-based modeling approach. Since; the mechanisms that
lead to the pandemic are dynamic, the individuals interact
with each other to spread the disease, and their interactions
are related to the outcome of interest. To observe the results,
the model is simulated using the NetLogo software.