Department of Epidemiology and Health Statistics, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China.
Sichuan Center for Disease Control and Prevention, Chengdu, China.
Front Public Health. 2022 Mar 11;10:774984. doi: 10.3389/fpubh.2022.774984. eCollection 2022.
Timely and accurate forecast of infectious diseases is essential for achieving precise prevention and control. A good forecasting method of infectious diseases should have the advantages of , and . Since previous research had illustrated that the spatial transmission network (STN) showed good interpretability and feasibility, this study further explored its forecasting performance for infectious diseases across multiple regions. Meanwhile, this study also showed whether the STN could overcome the challenges of model rationality and practical needs.
The construction of the STN framework involved three major steps: the spatial kluster analysis by tree edge removal (SKATER) algorithm, structure learning by dynamic Bayesian network (DBN), and parameter learning by the vector autoregressive moving average (VARMA) model. Then, we evaluated the forecasting performance of STN by comparing its accuracy with that of the mechanism models like susceptible-exposed-infectious-recovered-susceptible (SEIRS) and machine-learning algorithm like long-short-term memory (LSTM). At the same time, we assessed the robustness of forecasting performance of STN in high and low incidence seasons. The influenza-like illness (ILI) data in the Sichuan Province of China from 2010 to 2017 were used as an example for illustration.
The STN model revealed that ILI was likely to spread among multiple cities in Sichuan during the study period. During the whole study period, the forecasting accuracy of the STN (mean absolute percentage error [MAPE] = 31.134) was significantly better than that of the LSTM (MAPE = 41.657) and the SEIRS (MAPE = 62.039). In addition, the forecasting performance of STN was also superior to those of the other two methods in either the high incidence season (MAPE = 24.742) or the low incidence season (MAPE = 26.209), and the superiority was more obvious in the high incidence season.
This study applied the STN to the forecast of infectious diseases across multiple regions. The results illustrated that the STN not only had good accuracy in forecasting performance but also indicated the spreading directions of infectious diseases among multiple regions to a certain extent. Therefore, the STN is a promising candidate to improve the surveillance work.
及时、准确地预测传染病对于实现精准防控至关重要。一种好的传染病预测方法应具有 和 的优点。由于先前的研究表明空间传输网络（STN）具有良好的可解释性和可行性，本研究进一步探讨了其在多个地区传染病预测中的性能。同时，本研究还展示了 STN 是否能够克服模型合理性和实际需求的挑战。
STN 框架的构建涉及三个主要步骤：树边移除的空间聚类分析（SKATER）算法、动态贝叶斯网络（DBN）的结构学习以及向量自回归移动平均（VARMA）模型的参数学习。然后，我们通过比较 STN 的准确性与机制模型（如易感-暴露-感染-恢复-易感（SEIRS）和机器学习算法（如长短时记忆（LSTM））来评估 STN 的预测性能。同时，我们评估了 STN 在高发和低发季节的预测性能的稳健性。以中国四川省 2010 年至 2017 年的流感样疾病（ILI）数据为例进行说明。
STN 模型表明，ILI 在研究期间可能在四川省的多个城市之间传播。在整个研究期间，STN 的预测准确性（平均绝对百分比误差[MAPE] = 31.134）明显优于 LSTM（MAPE = 41.657）和 SEIRS（MAPE = 62.039）。此外，STN 的预测性能在高发季节（MAPE = 24.742）或低发季节（MAPE = 26.209）也优于其他两种方法，且在高发季节更为明显。
本研究将 STN 应用于多个地区的传染病预测。结果表明，STN 不仅具有良好的预测性能，而且在一定程度上指示了传染病在多个地区的传播方向。因此，STN 是提高监测工作的有前途的候选方法。
