College of Animal Science and Technology, Guangxi University, Nanning, 530004, China.
Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, 518120, China.
BMC Infect Dis. 2024 Sep 27;24(1):1062. doi: 10.1186/s12879-024-09892-y.
Zoonotic infections, characterized with huge pathogen diversity, wide affecting area and great society harm, have become a major global public health problem. Early and accurate prediction of their outbreaks is crucial for disease control. The aim of this study was to develop zoonotic diseases risk predictive models based on time-series incidence data and three zoonotic diseases in mainland China were employed as cases.
The incidence data for schistosomiasis, echinococcosis, and leptospirosis were downloaded from the Scientific Data Centre of the National Ministry of Health of China, and were processed by interpolation, dynamic curve reconstruction and time series decomposition. Data were decomposed into three distinct components: the trend component, the seasonal component, and the residual component. The trend component was used as input to construct the Long Short-Term Memory (LSTM) prediction model, while the seasonal component was used in the comparison of the periods and amplitudes. Finaly, the accuracy of the hybrid LSTM prediction model was comprehensive evaluated.
This study employed trend series of incidence numbers and incidence rates of three zoonotic diseases for modeling. The prediction results of the model showed that the predicted incidence number and incidence rate were very close to the real incidence data. Model evaluation revealed that the prediction error of the hybrid LSTM model was smaller than that of the single LSTM. Thus, these results demonstrate that using trending sequences as input sequences for the model leads to better-fitting predictive models.
Our study successfully developed LSTM hybrid models for disease outbreak risk prediction using three zoonotic diseases as case studies. We demonstrate that the LSTM, when combined with time series decomposition, delivers more accurate results compared to conventional LSTM models using the raw data series. Disease outbreak trends can be predicted more accurately using hybrid models.
人畜共患传染病具有病原体多样性大、影响范围广、社会危害大等特点，已成为全球重大公共卫生问题。对其暴发进行早期、准确的预测，对疾病控制至关重要。本研究旨在基于时间序列发病率数据，构建人畜共患疾病风险预测模型，以中国大陆的三种人畜共患疾病为例。
从国家卫生健康委科学数据中心下载血吸虫病、包虫病和钩端螺旋体病的发病率数据，采用插值、动态曲线重建和时间序列分解进行处理。将数据分解为三个不同的组成部分：趋势成分、季节成分和残差成分。趋势成分作为输入用于构建长短期记忆（LSTM）预测模型，而季节成分则用于比较周期和幅度。最后，综合评估混合 LSTM 预测模型的准确性。
本研究采用三种人畜共患疾病的发病率趋势序列进行建模。模型的预测结果表明，预测的发病率和发病率与真实发病率数据非常接近。模型评估显示，混合 LSTM 模型的预测误差小于单一 LSTM 模型。因此，这些结果表明，使用趋势序列作为输入序列可以构建拟合度更好的预测模型。
本研究成功地建立了三种人畜共患疾病的 LSTM 混合模型，用于疾病暴发风险预测。研究表明，LSTM 与时间序列分解相结合，与使用原始数据序列的传统 LSTM 模型相比，能够提供更准确的结果。使用混合模型可以更准确地预测疾病暴发趋势。
