Kulaç Oray, Toy Ayhan Özgür, Kabak Kamil Erkan
Graduate School, Yasar University, Izmir, 35100, Türkiye.
Department of Industrial Engineering, Yasar University, Izmir, 35100, Türkiye.
Comput Biol Med. 2025 Mar;186:109564. doi: 10.1016/j.compbiomed.2024.109564. Epub 2025 Jan 3.
The severity of recent Coronavirus (COVID-19) pandemics has revealed the importance of development of inoculation strategies in case of limited vaccine availability. Authorities have implemented inoculation strategies based on perceived risk factors such as age and existence of other chronic health conditions for survivability from the disease. However, various other factors can be considered for identifying the preferred inoculation strategies depending on the vaccine availability and disease spread levels. This study explores the effectiveness of inoculating different groups of population in case of various vaccine availabilities and disease spread levels by means of some performance metrics namely: Attack Rate (AR), Death Rate (DR) and Hospitalization Rate (HR).
In this study we have implemented a highly detailed Agent-Based Simulation (ABS) model that extends classical SEIR Model by including five more additional states: Asymptomatic (A), Quarantine (Q), Hospitalized (H), Dead (D) and Immune (M) which can be used as a decision support tool to prioritize the groups of the population inoculated. The approach employs the modelling of daily mobility of individuals, their interactions and transmission of virus among individuals. The population is heterogeneously clustered according to age, family size, work status, transportation and leisure preferences with 17 different groups in order to find the most appropriate one to inoculate. Three different Disease Spread Levels (DSL) (low, mid, high) are experimented with four different Vaccine Available Percentages (VAP) (25%, 50%, 75% and 85%) with a total of 84 scenarios.
As the benchmark, under the No Vaccine case Attack Rate, Hospitalization Rate, and Death Rate goes as high as 99.53%, 16.96%, and 1.38%, respectively. Corresponding highest performance metrics (rates) are 72.33%, 15.95%, and 1.35% for VAP = 25%; 50.25%, 9.55%, and 0.94% for VAP = 50%; 24.53%, 2.62%, and 0.25% for VAP = 75%; and 11.51%, 0.002%, and 0.08% for VAP = 85%. The results of our study shows that the common practice of inoculation based on the age of individual does not yield the best outcome in terms of performance metrics across all DSL and VAP values. The groups containing workers and students that represent highly interactive individuals, i.e. Group (9, 10), Group (9, 11, 10‾) and Group (9, 10, 11, 12‾) emerge as a commonly recommended choice for inoculation in the majority of cases. As expected, we observe that the higher is the VAP levels the more is the number of alternative inoculation groups.
Findings of this study present that: (i) inoculation considerably decreases the number of infected individuals, the number of deaths and the number of hospitalized individuals due to the disease, (ii) the best inoculation group/groups with respect to performance metrics varies depending on the vaccine availability percentages and disease spread levels, (iii) simultaneous implementation of both inoculation and precautions like lock-down, social distances and quarantines, yields a stronger impact on disease spread and its consequences.
近期冠状病毒（COVID-19）大流行的严重程度凸显了在疫苗供应有限的情况下制定接种策略的重要性。当局已根据年龄和其他慢性健康状况等感知风险因素实施接种策略，以提高疾病的生存几率。然而，根据疫苗供应情况和疾病传播水平，还可以考虑其他各种因素来确定首选的接种策略。本研究通过一些性能指标，即攻击率（AR）、死亡率（DR）和住院率（HR），探讨在不同疫苗供应情况和疾病传播水平下对不同人群进行接种的效果。
在本研究中，我们实施了一个高度详细的基于主体的模拟（ABS）模型，该模型在经典的SEIR模型基础上增加了五个额外状态：无症状（A）、隔离（Q）、住院（H）、死亡（D）和免疫（M），可作为一种决策支持工具，对接种人群进行优先级排序。该方法对个体的日常流动性、他们之间的互动以及病毒在个体之间的传播进行建模。根据年龄、家庭规模、工作状态、交通和休闲偏好，将人群异质性地分为17个不同组，以找到最合适的接种组。针对三种不同的疾病传播水平（DSL）（低、中、高），试验了四种不同的疫苗可用百分比（VAP）（25%、50%、75%和85%），共84种情况。
作为基准，在无疫苗情况下，攻击率、住院率和死亡率分别高达99.53%、16.96%和1.38%。对于VAP = 25%，相应的最高性能指标（率）分别为72.33%、15.95%和1.35%；对于VAP = 50%，分别为50.25%、9.55%和0.94%；对于VAP = 75%，分别为24.53%、2.62%和0.25%；对于VAP = 85%，分别为11.51%、0.002%和0.08%。我们的研究结果表明，基于个体年龄的常规接种做法在所有DSL和VAP值的性能指标方面并未产生最佳结果。包含工人和学生的组，即代表高互动性个体的第（9, 10）组、第（9, 11, 10‾）组和第（9, 10, 11, 12‾）组，在大多数情况下成为普遍推荐的接种选择。正如预期的那样，我们观察到VAP水平越高，可供选择的接种组数量就越多。
本研究结果表明：（i）接种可显著减少因该疾病感染的个体数量、死亡数量和住院个体数量；（ii）就性能指标而言，最佳接种组因疫苗可用百分比和疾病传播水平而异；（iii）同时实施接种和封锁、社交距离及隔离等预防措施，对疾病传播及其后果的影响更强。
