Shen Li, Jiang Chenghao, Sun Minghao, Qiu Xuan, Qian Jiaqi, Song Shuxuan, Hu Qingwu, Yelixiati Heilili, Liu Kun
School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, China.
Department of Epidemiology, Ministry of Education Key Lab of Hazard Assessment and Control in Special Operational Environment, School of Public Health, Air Force Medical University, Xi'an, China.
Can J Infect Dis Med Microbiol. 2022 Aug 3;2022:7658880. doi: 10.1155/2022/7658880. eCollection 2022.
Brucellosis is a chronic infectious disease caused by brucellae or other bacteria directly invading human body. Brucellosis presents the aggregation characteristics and periodic law of infectious diseases in temporal and spatial distribution. Taking major European countries as an example, this study established the temporal and spatial distribution sequence of brucellosis, analyzed the temporal and spatial distribution characteristics of brucellosis, and quantitatively predicted its epidemic law by using different traditional or machine learning models. This paper indicates that the epidemic of brucellosis in major European countries has statistical periodic characteristics, and in the same cycle, brucellosis has the characteristics of piecewise trend. Through the comparison of the prediction results of the three models, it is found that the prediction effect of long short-term memory and convolutional long short-term memory models is better than autoregressive integrated moving average model. The first mock exam using Conv layer and data vectorizations predicted that the convolutional long short-term memory model outperformed the traditional long short-term memory model. Compared with the monthly scale, the prediction of the trend stage of brucellosis can achieve better results under the single model prediction. These findings will help understand the development trend and liquidity characteristics of brucellosis, provide corresponding scientific basis and decision support for potential risk assessment and brucellosis epidemic prevention and control, and reduce the loss of life and property.
布鲁氏菌病是由布鲁氏菌或其他细菌直接侵入人体引起的一种慢性传染病。布鲁氏菌病在时间和空间分布上呈现出传染病的聚集特征和周期性规律。以欧洲主要国家为例,本研究建立了布鲁氏菌病的时间和空间分布序列,分析了布鲁氏菌病的时间和空间分布特征,并使用不同的传统或机器学习模型对其流行规律进行了定量预测。本文表明,欧洲主要国家布鲁氏菌病的流行具有统计周期性特征,且在同一周期内,布鲁氏菌病具有分段趋势特征。通过对三种模型预测结果的比较发现,长短期记忆模型和卷积长短期记忆模型的预测效果优于自回归积分滑动平均模型。使用卷积层和数据向量化进行的首次模拟预测表明,卷积长短期记忆模型的性能优于传统的长短期记忆模型。与月度尺度相比,在单模型预测下,对布鲁氏菌病趋势阶段的预测能取得更好的结果。这些研究结果将有助于了解布鲁氏菌病的发展趋势和流动性特征,为潜在风险评估以及布鲁氏菌病的疫情防控提供相应的科学依据和决策支持,减少生命和财产损失。