Patón Mauricio, Acuña Juan M, Rodríguez Jorge
Department of Chemical Engineering, College of Engineering, Khalifa University, SAN Campus PO Box 127788, Abu Dhabi, United Arab Emirates.
Department of Epidemiology and Public Health, College of Medicine. Khalifa University, PO Box 127788, Abu Dhabi, United Arab Emirates.
Infect Dis Model. 2023 Aug 23;8(4):1032-1049. doi: 10.1016/j.idm.2023.07.012. eCollection 2023 Dec.
Vaccine allocation strategies become crucial during vaccine shortages, especially in the face of potential outbreaks of new infectious diseases, as witnessed during the COVID-19 pandemic. To address this, a specialized compartmental model is created, which simulates an emerging infectious disease similar to COVID-19. This model divides the population into different age groups and is used to compare various vaccine prioritisation approaches, aiming to minimize the total number of fatalities. The model is an improvement upon previous ones as it incorporates essential behavioural factors and is adapted to account for the protective effects of vaccination against both disease infection and transmission. It takes into account human behaviors such as mask-wearing and social distancing by utilizing specific parameters related to self-protection, awareness levels, and the frequency of daily person-to-person interactions within each age group. Furthermore, a novel method for dynamic vaccine prioritisation was introduced in this study. This approach is model-independent and relies on the dynamic R number. It is the first time such a method has been developed, offering a decision-making approach that is not tied to any specific model. This innovation provides a flexible and adaptable strategy for determining vaccine priorities based on real-time data and the current state of the outbreak. Our findings reveal crucial insights into vaccine allocation strategies. When the daily rollout rates are fast (0.75% or higher) and children are eligible for vaccination, prioritising groups with high daily person-to-person interactions can lead to substantial reductions in total fatalities (up to approximately 40% lower). On the other hand, if rollout rates are slower and overall vaccination coverage is high, focusing on vaccinating elders emerges as the most effective strategy, resulting in up to approximately 10% fewer fatalities. However, the scenario changes significantly when children are not eligible for vaccination, as they constitute a highly interactive population group. In this case, the differences between priority strategies become smaller. With fast daily rollout rates, prioritisation based on interactions achieves only a 7% reduction in total fatalities, while a slower rollout with vaccination of elders first leads to an approximately 11% reduction in fatalities compared to the scenario where children are eligible for vaccination. The impact of behavioural parameters is equally critical. When the self-protection levels exercised by the population are low, it significantly affects the optimal vaccine prioritisation strategy to be followed, making it essential to consider behavioural factors in decision-making.
在疫苗短缺期间，尤其是面对新传染病可能爆发的情况时，疫苗分配策略变得至关重要，就像在新冠疫情期间所见证的那样。为了解决这个问题，创建了一个专门的分区模型，该模型模拟了一种类似于新冠病毒的新发传染病。这个模型将人群划分为不同年龄组，用于比较各种疫苗优先排序方法，旨在将总死亡人数降至最低。该模型是对先前模型的改进，因为它纳入了重要的行为因素，并适用于考虑疫苗接种对疾病感染和传播的保护作用。它通过利用与自我保护、意识水平以及每个年龄组内日常人际互动频率相关的特定参数，考虑了诸如戴口罩和保持社交距离等人类行为。此外，本研究引入了一种动态疫苗优先排序的新方法。这种方法独立于模型，依赖于动态R值。这是首次开发出这样的方法，提供了一种不依赖于任何特定模型的决策方法。这一创新为基于实时数据和疫情当前状态确定疫苗优先级提供了一种灵活且可适应的策略。我们的研究结果揭示了关于疫苗分配策略的关键见解。当日接种率较快（0.75%或更高）且儿童有资格接种疫苗时，优先考虑日常人际互动频繁的群体可大幅降低总死亡人数（最多可降低约40%）。另一方面，如果接种率较慢且总体疫苗接种覆盖率较高，将重点放在为老年人接种疫苗上则成为最有效的策略，可使死亡人数减少约10%。然而，当儿童没有资格接种疫苗时，情况会发生显著变化，因为他们是一个人际互动频繁的人群组。在这种情况下，优先策略之间的差异会变小。当日接种率较快时，基于互动的优先排序仅使总死亡人数减少7%，而与儿童有资格接种疫苗的情况相比，先为老年人接种疫苗且接种率较慢会使死亡人数减少约11%。行为参数的影响同样至关重要。当人群的自我保护水平较低时，它会显著影响应遵循的最佳疫苗优先排序策略，这使得在决策过程中考虑行为因素至关重要。
