Since the emergence of the novel COVID-19 virus pandemic in December 2019, numerous mathematical models were published to assess the transmission dynamics of the disease, predict its future course, and evaluate the impact of different control measures. The simplest models make the basic assumptions that individuals are perfectly and evenly mixed and have the same social structures. Such assumptions become problematic for large developing countries that aggregate heterogeneous COVID-19 outbreaks in local areas. Thus, this paper proposes a spatial SEIRDV model that includes spatial vaccination coverage, spatial vulnerability, and level of mobility, to take into account the spatial–temporal clustering pattern of COVID-19 cases. The conclusion of this study is that immunity, government interventions, infectiousness and virulence are the main drivers of the spread of COVID-19. These factors should be taken into consideration when scientists, public policy makers and other stakeholders in the health community analyse, create and project future disease prevention scenarios. Such a model has a place for disease outbreaks that may occur in future, allowing for the inclusion of vaccination rates in a spatial manner.

自 2019 年 12 月出现新型 COVID-19 病毒大流行以来，已发布了许多数学模型来评估该疾病的传播动态，预测其未来的进程，并评估不同控制措施的影响。最简单的模型做出的基本假设是，个体是完美且均匀混合的，并且具有相同的社会结构。对于在局部地区聚集不同种类的 COVID-19 疫情的发展中大国来说，这种假设会产生问题。因此，本文提出了一个空间 SEIRDV 模型，其中包括空间疫苗接种覆盖率、空间脆弱性和流动性水平，以考虑到 COVID-19 病例的时空聚集模式。这项研究的结论是，免疫力、政府干预、传染性和毒力是COVID-19传播的主要驱动因素。当科学家、公共政策制定者和卫生界的其他利益相关者分析、创建和预测未来疾病预防方案时，应考虑这些因素。这种模型适用于未来可能发生的疾病爆发，允许以空间方式包含疫苗接种率。