Chen Xin, Ning Huijun, Guo Liuwang, Diao Dongming, Zhou Xinru, Zhang Xiaoliang
College of Civil Architecture, Henan University of Science and Technology, Luoyang, China.
School of Mathematics and Statistics, Henan University of Science and Technology, Luoyang, China.
PeerJ Comput Sci. 2023 Jul 12;9:e1479. doi: 10.7717/peerj-cs.1479. eCollection 2023.
Building upon the foundational principles of the grid search algorithm and Monte Carlo numerical simulation, this article introduces an innovative epidemic monitoring and prevention plan. The plan offers the capability to accurately identify the sources of infectious diseases and predict the final scale and duration of the epidemic. The proposed plan is implemented in schools and society, utilizing computer simulation analysis. Through this analysis, the plan enables precise localization of infection sources for various demographic groups, with an error rate of less than 3%. Additionally, the plan allows for the estimation of the epidemic cycle duration, which typically spans around 14 days. Notably, higher population density enhances fault tolerance and prediction accuracy, resulting in smaller errors and more reliable simulation outcomes. Overall, this study provides highly valuable theoretical guidance for effective epidemic prevention and control efforts.
基于网格搜索算法和蒙特卡罗数值模拟的基本原理，本文介绍了一种创新的疫情监测与防控方案。该方案能够准确识别传染病源，并预测疫情的最终规模和持续时间。所提出的方案在学校和社会中实施，利用计算机模拟分析。通过这种分析，该方案能够精确确定不同人群感染源的位置，错误率低于3%。此外，该方案还能估算疫情周期的持续时间，通常约为14天。值得注意的是，较高的人口密度提高了容错能力和预测准确性，从而减少了误差，使模拟结果更可靠。总体而言，本研究为有效的疫情防控工作提供了极具价值的理论指导。
