PDF(Pubmed)
Abstract：
Accurate prediction of the temporal and spatial characteristics of COVID-19 infection is of paramount importance for effective epidemic prevention and control. In order to accomplish this objective, we incorporated individual antibody dynamics into an agent-based model and devised a methodology that encompasses the dynamic behaviors of each individual, thereby explicitly capturing the count and spatial distribution of infected individuals with varying symptoms at distinct time points. Our model also permits the evaluation of diverse prevention and control measures. Based on our findings, the widespread employment of nucleic acid testing and the implementation of quarantine measures for positive cases and their close contacts in China have yielded remarkable outcomes in curtailing a less transmissible yet more virulent strain; however, they may prove inadequate against highly transmissible and less virulent variants. Additionally, our model excels in its ability to trace back to the initial infected case (patient zero) through early epidemic patterns. Ultimately, our model extends the frontiers of traditional epidemiological simulation methodologies and offers an alternative approach to epidemic modeling.
摘要：
准确预测COVID-19感染的时空特征对于有效预防和控制疫情至关重要。为了实现这一目标,我们将个体抗体动力学纳入基于代理的模型，并设计了一种涵盖每个个体动态行为的方法，从而明确地捕获在不同时间点具有不同症状的受感染个体的计数和空间分布。我们的模型还允许评估各种预防和控制措施。根据我们的发现,在中国，核酸检测的广泛使用以及对阳性病例及其密切接触者实施检疫措施，在减少传染性较低但毒性较大的菌株方面取得了显著成果；然而，它们可能不足以对抗高度传染性和毒性较小的变体。此外,我们的模型能够通过早期流行模式追溯到最初的感染病例(零患者)。最终,我们的模型扩展了传统流行病学模拟方法的前沿,为流行病学建模提供了一种替代方法.
