| 一种耦合LSTM算法和云模型的疫情传播风险预测模型 |
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| 引用本文： | 李照,高惠瑛,代晓奕,孙海.一种耦合LSTM算法和云模型的疫情传播风险预测模型[J].地球信息科学,2021,23(11):1924-1925. |
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| 作者姓名： | 李照 高惠瑛 代晓奕 孙海 |
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| 作者单位： | 中国海洋大学工程学院 城市与工程管理信息化山东高校重点实验室,青岛 266100 |
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| 基金项目： | 国家自然科学基金项目(41906185);国家自然科学基金项目(U1706226);国家自然科学基金项目(52071307) |
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| 摘 要： | 模拟传染病时空传播、定量评估疫情风险对科学防控、精准施策具有重要的现实意义。本文融合多源时空数据,构建了耦合LSTM算法和云模型的疫情传播风险预测模型。该模型首先基于GIS和LSTM算法构建疫情空间演变模拟模型,通过学习历史疫情数据中的规律,以1 km×1 km为空间尺度、天为时间尺度模拟传染病时空传播过程。其次,基于模拟传染病例数据和疫情传播时空影响因素构建风险评价指标,应用云模型和自适应策略构建疫情风险评估模型,实现多空间尺度的疫情风险评价。在实证研究阶段,应用该模型对北京2020年6月份突发COVID-19疫情空间演变过程进行模拟和风险评估,并引入常规机器学习模型作比较验证。结果表明：应用于疫情时空传播模拟,相较其它常规的机器学习模型,考虑时序关系的LSTM模型的模拟精度更高（MAE为0.00261）,拟合度更好（R-square为0.9455）;耦合模型不仅能充分考虑传染源因素、天气因素、疫情扩散因素及疫情防御因素对疫情风险传播的影响,反映风险演变趋势,还能快速量化区域风险等级,实现不同空间分辨率下的疫情风险评估。因此,基于LSTM算法和云模型的耦合模型可有效预测疫情的传播风险,同时,也为传染病时空传播建模与风险评估提供了方法参考。
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| 关 键 词： | 传染病 长短期记忆（LSTM）模型 云模型 空间演变模拟 风险评估 风险预测 耦合模型 COVID-19 |
| 收稿时间： | 2021-09-25 |
An Epidemic Spread Risk Prediction Model Coupled with LSTM Algorithm and Cloud Model |
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| LI Zhao,GAO Huiying,DAI Xiaoyi,SUN Hai.An Epidemic Spread Risk Prediction Model Coupled with LSTM Algorithm and Cloud Model[J].Geo-information Science,2021,23(11):1924-1925. |
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| Authors: | LI Zhao GAO Huiying DAI Xiaoyi SUN Hai |
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| Institution: | Key Laboratory of Modern Management Informationization Universities in Shandong, College of Engineering, Ocean University of China, Qingdao 266100, China |
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| Abstract: | The COVID-19 epidemic poses a great threat to public health and people's lives, which has initiated new challenges to the prevention and control system of the epidemic in China. In all efforts for epidemic control and prevention, predicting the risk of epidemic spread is of great practical importance for scientific prevention and control, and precise strategies. To predict the risk of an epidemic rapidly and quantitatively, this paper fused multi-source spatiotemporal data and established a risk prediction model for epidemic transmission by coupling LSTM algorithm and cloud model. Firstly, a simulation model of the spatiotemporal spread of infectious diseases was built based on GIS and LSTM algorithm, which simulated the infectious disease's spatiotemporal transmission process by learning rules in historical epidemic data. At the same time, to improve the simulation accuracy, this paper took 1 km × 1 km for the spatial scale, and days for the temporal scale as the study scale. Secondly, this paper applied the simulated data of infectious cases and the spatiotemporal influence factors on the spread of the epidemic to construct risk evaluation indicators. Finally, the cloud model and adaptive strategies were applied to construct an epidemic risk assessment model. In this way, the epidemic risk assessment at multiple spatial scales was achieved. In the empirical study phase, based on the Beijing COVID-19 epidemic data from 11 June 2020 to 25 June 2020, this paper simulated the process of the spatial evolution of the epidemic from 26 June 2020 to 1 July 2020. To test the advantage of the LSTM model applied to simulate spatiotemporal spread of infectious diseases, four machine learning models were introduced for comparison, including GA-BP Neural Network, Decision Regression Tree, Random Forest, and Support Vector Machine. The results were as follows: ① Compared with other conventional machine learning models, the LSTM model with time-series relationship had higher simulation accuracy (MAE=0.002 61) and better fitting degree (R-Square=0.9455). This showed that the LSTM model considering the temporal relationship between epidemic data was more suitable for epidemic spatial evolution simulation. ② The application results showed that the coupled model can not only fully consider the influence of infection source factors, weather factors, epidemic spread factors and epidemic prevention factors on the spread of transmission risk and reflect the trend of risk evolution, but also quickly quantify regional risk levels. Therefore, the coupled model based on LSTM algorithm and cloud model can effectively predict the transmission risk of epidemic, and also provide a method reference for establishing spatial-temporal transmission models and assessing epidemic risk. |
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| Keywords: | infectious diseases Long Short-Term Memory (LSTM) model cloud model spatial evolution simulation risk assessment risk prediction coupled model COVID-19 |
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