Lima L L, Atman A P F
Programa de Pós-Graduação em Modelagem Matemática e Computacional, Centro Federal de Educação Tecnológica de Minas Gerais-CEFET-MG, Belo Horizonte, Minas Gerais, Brazil.
Departamento de Física, Centro Federal de Educação Tecnológica de Minas Gerais-CEFET-MG, Belo Horizonte, Minas Gerais, Brazil.
PLoS One. 2021 Mar 18;16(3):e0248708. doi: 10.1371/journal.pone.0248708. eCollection 2021.
COVID-19 pandemic is an immediate major public health concern. The search for the understanding of the disease spreading made scientists around the world turn their attention to epidemiological studies. An interesting approach in epidemiological modeling nowadays is to use agent-based models, which allow to consider a heterogeneous population and to evaluate the role of superspreaders in this population. In this work, we implemented an agent-based model using probabilistic cellular automata to simulate SIR (Susceptible-Infected-Recovered) dynamics using COVID-19 infection parameters. Differently to the usual studies, we did not define the superspreaders individuals a priori, we only left the agents to execute a random walk along the sites. When two or more agents share the same site, there is a probability to spread the infection if one of them is infected. To evaluate the spreading, we built the transmission network and measured the degree distribution, betweenness, and closeness centrality. The results displayed for different levels of mobility restriction show that the degree reduces as the mobility reduces, but there is an increase of betweenness and closeness for some network nodes. We identified the superspreaders at the end of the simulation, showing the emerging behavior of the model since these individuals were not initially defined. Simulations also showed that the superspreaders are responsible for most of the infection propagation and the impact of personal protective equipment in the spreading of the infection. We believe that this study can bring important insights for the analysis of the disease dynamics and the role of superspreaders, contributing to the understanding of how to manage mobility during a highly infectious pandemic as COVID-19.
新冠疫情是当下主要的重大公共卫生问题。为了深入了解疾病传播情况，世界各地的科学家都将注意力转向了流行病学研究。如今，流行病学建模中一种有趣的方法是使用基于主体的模型，这种模型能够考虑异质人群，并评估超级传播者在该人群中的作用。在这项工作中，我们使用概率细胞自动机实现了一个基于主体的模型，以利用新冠病毒感染参数模拟易感 - 感染 - 康复（SIR）动态。与通常的研究不同，我们没有事先定义超级传播者个体，只是让主体在各个位置上执行随机游走。当两个或更多主体处于同一位置时，如果其中一个被感染，就存在传播感染的可能性。为了评估传播情况，我们构建了传播网络并测量了度分布、介数和接近中心性。针对不同程度的行动限制所展示的结果表明，随着行动能力降低，度会减小，但某些网络节点的介数和接近中心性会增加。我们在模拟结束时确定了超级传播者，这显示了模型的涌现行为，因为这些个体最初并未被定义。模拟还表明，超级传播者对大多数感染传播负责，以及个人防护装备在感染传播中的影响。我们相信这项研究能够为疾病动态分析和超级传播者的作用带来重要见解，有助于理解在像新冠这样的高传染性大流行期间如何管理人员流动。
