Li Xiaoming, Xu Xianghui, Wang Jie, Li Jing, Qin Sheng, Yuan Juxiang
1School of Public HealthNorth China University of Science and TechnologyTangshan063210China.
2Hebei Province Key Laboratory of Occupational Health and Safety for Coal IndustryNorth China University of Science and TechnologyTangshan063210China.
IEEE Access. 2020 Mar 10;8:49574-49583. doi: 10.1109/ACCESS.2020.2979859. eCollection 2020.
Acquired Immune Deficiency Syndrome (AIDS) is still one of the most life-threatening diseases in the world. Moreover, new infections are still potentially increasing. This difficult problem must be solved. Early warning is the most effective way to solve this problem. Here, we aim to determine the best performing model to track the epidemic of AIDS, which will provide a methodological basis for testing the time characteristics of the disease. From January 2004 to January 2018, we built four computing methods based on AIDS dataset: BPNN model, RNN model, LSTM model and MHPSO-GRU model. Compare the final estimated performance to determine the preferred method. Result. Considering the root mean square error (RMSE), mean absolute error (MAE), mean error rate (MER) and mean absolute percentage error (MAPE) in the simulation and prediction subsets, the MHPSO-GRU model is determined as the best performance technology. Estimates for the period from May 2018 to December 2020 suggest that the event appears to continue to increase and remain high.
获得性免疫缺陷综合征（艾滋病）仍然是世界上最危及生命的疾病之一。此外，新感染病例仍有可能增加。这个难题必须得到解决。早期预警是解决这个问题的最有效方法。在此，我们旨在确定追踪艾滋病流行情况的最佳表现模型，这将为测试该疾病的时间特征提供方法依据。从2004年1月到2018年1月，我们基于艾滋病数据集构建了四种计算方法：BPNN模型、RNN模型、LSTM模型和MHPSO - GRU模型。比较最终估计性能以确定首选方法。结果。考虑到模拟和预测子集中的均方根误差（RMSE）、平均绝对误差（MAE）、平均误差率（MER）和平均绝对百分比误差（MAPE），MHPSO - GRU模型被确定为性能最佳的技术。对2018年5月至2020年12月期间的估计表明，该事件似乎继续增加且居高不下。
