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使用基于主体的建模方法测试不同的新冠疫苗接种策略。

Testing Different COVID-19 Vaccination Strategies Using an Agent-Based Modeling Approach.

作者信息

Trad Fouad, El Falou Salah

机构信息

Faculty of Engineering, Lebanese University, Ras Maska, Al Koura, Lebanon.

Faculty of Science, Lebanese University, Ras Maska, Al Koura, Lebanon.

出版信息

SN Comput Sci. 2022;3(4):307. doi: 10.1007/s42979-022-01199-6. Epub 2022 May 25.

DOI:10.1007/s42979-022-01199-6
PMID:35637643
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9131986/
Abstract

Vaccination has been the long-awaited solution ever since the COVID-19 pandemic started. But the problem is that vaccine shots cannot be delivered at the same time to all populations, because of their limited quantity from one side, and their high demand from the other side. Therefore, countries need a way to test the effect of different distribution strategies before applying them. But how can they do this? To assist countries with this task, we built an agent-based model that runs on top of the Monte Carlo algorithm. This model simulates the spread of COVID-19 in a country where we can apply different NPIs at different times, and we can supply different kinds of vaccines using different strategies. In this study, we tested the outcomes of four vaccination strategies: older first, younger first, a mixed strategy, and a random strategy. We simulated these strategies in two different countries: France and Colombia. Then, we performed a comparative analysis to find which strategy might be the best for each country. Our results show that what is good for a country is not necessarily the best for the other one. Therefore, we proved that a vaccination strategy should be adapted to the structure of the population we are vaccinating. The system we built helps countries in this direction by allowing them to test the outcomes of their strategies before applying them in real life to select the one that minimizes human losses (deaths and infections).

摘要

自新冠疫情爆发以来,疫苗接种一直是人们期待已久的解决方案。但问题在于,由于疫苗数量有限且需求巨大,无法同时为所有人群接种。因此,各国需要一种方法在应用不同的分配策略之前测试其效果。但他们该如何做到这一点呢?为协助各国完成这项任务,我们构建了一个基于智能体的模型,该模型运行在蒙特卡洛算法之上。此模型模拟了新冠病毒在一个国家的传播情况,在这个国家我们可以在不同时间应用不同的非药物干预措施,并且可以使用不同策略供应不同种类的疫苗。在本研究中,我们测试了四种疫苗接种策略的结果:先接种老年人、先接种年轻人、混合策略和随机策略。我们在两个不同的国家模拟了这些策略:法国和哥伦比亚。然后,我们进行了比较分析,以找出哪种策略可能对每个国家都是最佳的。我们的结果表明,对一个国家有益的策略对另一个国家不一定是最佳的。因此,我们证明了疫苗接种策略应根据我们正在接种疫苗的人群结构进行调整。我们构建的系统通过允许各国在实际应用之前测试其策略的结果,从而在这方面帮助各国选择能将人员损失(死亡和感染)降至最低的策略。

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