Dong Wen, Yao Henan, Wang Wei-Na
Faculty of Geography, Yunnan Normal University, Kunming, China.
Geographic Information System (GIS) Technology Engineering Research Centre for West-China Resources and Environment of Educational Ministry, Yunnan Normal University, Kunming, China.
Front Cell Infect Microbiol. 2025 May 13;15:1547601. doi: 10.3389/fcimb.2025.1547601. eCollection 2025.
Since the first discovery and reporting of the COVID - 19 pandemic towards the end of 2019, the virus has rapidly propagated across the world. This has led to a remarkable spike in the number of infections. Even now, doubt lingers over whether it has completely disappeared. Moreover, the issue of restoring normal life while ensuring safety continues to be a crucial challenge that public health agencies and people globally are eager to tackle.
To thoroughly understand the epidemic's outbreak and transmission traits and formulate timely prevention measures to fully safeguard human lives and property, this paper presents an agent - based model incorporating individual - level factors.
The model designates Xi'an-where a characteristic disease outbreak occurred-as the research area. The simulation results demonstrate substantial consistency with official records, effectively validating the model's applicability, adaptability, and generalizability. This validated capacity enables accurate prediction of epidemic trends and comprehensive assessment of disease risks.
From late 2021 to early 2022, it employs a one - to - one population simulation approach and simulates epidemic impacts and disease risks. Initially, using building statistical data in the study area, the model reconstructs the local real - world geographical environment. Leveraging data from the seventh national population census, it also replicates the study area's population characteristics. Next, the model takes into account population mobility, contact tracing, patient treatment, and the diagnostic burden of COVID - 19 - like influenza symptoms. It integrates epidemic transmission impact parameters into the model framework. Eventually, the model's results are compared with official data for validation, and it's applied to hypothetical scenarios. It provides scientific theoretical tools to support the implementation of government - driven prevention and control measures. Additionally, it facilitates the adjustment of individual behavioral guidelines, promoting more effective epidemic management.
自2019年末首次发现并报告新冠疫情以来，该病毒已在全球迅速传播。这导致感染人数显著激增。即便到现在，人们仍怀疑它是否已完全消失。此外，在确保安全的同时恢复正常生活的问题，仍是全球公共卫生机构和民众急于应对的关键挑战。
为深入了解疫情的爆发及传播特征，并及时制定预防措施以全面保障人民生命财产安全，本文提出了一个纳入个体层面因素的基于主体的模型。
该模型将发生过典型疾病爆发的西安市指定为研究区域。模拟结果与官方记录显示出高度一致性，有效验证了模型的适用性、适应性和通用性。这种经过验证的能力能够准确预测疫情趋势并全面评估疾病风险。
从2021年末到2022年初，它采用一对一的人口模拟方法，模拟疫情影响和疾病风险。首先，利用研究区域的建筑统计数据，该模型重建当地真实的地理环境。借助第七次全国人口普查数据，它还复制了研究区域的人口特征。接下来，该模型考虑了人口流动、接触者追踪、患者治疗以及类似新冠流感症状的诊断负担。它将疫情传播影响参数整合到模型框架中。最终，将模型结果与官方数据进行比较以进行验证，并应用于假设情景。它提供了科学的理论工具，以支持政府主导的防控措施的实施。此外，它有助于调整个人行为准则，促进更有效的疫情管理。
