School of Information Science and Technology, Yunnan Normal University, Kunming 650500, China.
Faculty of Geography, Yunnan Normal University, Kunming 650500, China.
Int J Environ Res Public Health. 2022 Aug 18;19(16):10242. doi: 10.3390/ijerph191610242.
Since the COVID-19 outbreak was detected and reported at the end of 2019, the pandemic continues worldwide, with public health authorities and the general public in each country struggling to balance safety and normal travel activities. However, the complex public health environment and the complexity of human behaviors, as well as the constant mutation of the COVID-19 virus, requires the development of theoretical and simulation tools to accurately model all segments of society. In this paper, an agent-based model is proposed, the model constructs the real geographical environment of Shanghai Huangpu District based on the building statistics data of Shanghai Huangpu District, and the real population data of Shanghai Huangpu District based on the data of China's seventh Population census in 2020. After incorporating the detailed elements of COVID-19 transmission and the real data of WHO, the model forms various impact parameters. Finally, the model was validated according to the COVID-19 data reported by the official, and the model is applied to a hypothetical scenario. Shanghai is one of the places hardest hit by the current outbreak, Huangpu District is the "heart, window and name card" of Shanghai, and its importance to Shanghai is self-evident. so we used one-to-one population modeling to simulate the spread of COVID-19 in Huangpu District of Shanghai, In addition to the conventional functions of crowd movement, detection and treatment, the model also takes into account the burden of nucleic acid detection on the model caused by diseases similar to COVID-19, such as seasonal cold. The model validation results show that we have constructed a COVID-19 epidemic agent risk assessment system suitable for the individual epidemiological characteristics of COVID-19 in China, which can adjust and reflect on the existing COVID-19 epidemic intervention strategies and individual health behaviors. To provide scientific theoretical basis and information decision-making tools for effective prevention and control of COVID-19 and public health intervention in China.
自 2019 年底发现并报告 COVID-19 疫情以来，该疫情在全球范围内持续存在，每个国家的公共卫生当局和公众都在努力平衡安全和正常的旅行活动。然而，复杂的公共卫生环境和人类行为的复杂性，以及 COVID-19 病毒的不断变异，需要开发理论和模拟工具来准确模拟社会的各个部分。在本文中，提出了一种基于代理的模型，该模型基于上海黄浦区的建筑统计数据构建了上海黄浦区的真实地理环境，并基于 2020 年中国第七次人口普查的数据构建了上海黄浦区的真实人口数据。在纳入 COVID-19 传播的详细要素和世界卫生组织的真实数据后，该模型形成了各种影响参数。最后，根据官方报告的 COVID-19 数据对模型进行了验证，并将模型应用于假设情景。上海是当前疫情受影响最严重的地区之一，黄浦区是上海的“心脏、窗口和名片”，其对上海的重要性不言而喻。因此，我们使用一对一的人口建模来模拟 COVID-19 在上海黄浦区的传播。除了常规的人群流动、检测和治疗功能外，该模型还考虑了与 COVID-19 类似的疾病（如季节性感冒）对模型造成的核酸检测负担。模型验证结果表明，我们已经构建了一个适合中国 COVID-19 个体流行病学特征的 COVID-19 疫情代理风险评估系统，该系统可以调整和反映现有的 COVID-19 疫情干预策略和个体健康行为。为中国 COVID-19 的有效防控和公共卫生干预提供科学的理论基础和信息决策工具。
