Department of Preventive Medicine, Shantou University Medical College, Shantou, China.
Department of Medical Quality Management, Nanfang Hospital, Guangzhou, China.
PLoS Negl Trop Dis. 2023 Jun 7;17(6):e0011418. doi: 10.1371/journal.pntd.0011418. eCollection 2023 Jun.
Predicting the specific magnitude and the temporal peak of the epidemic of individual local outbreaks is critical for infectious disease control. Previous studies have indicated that significant differences in spatial transmission and epidemic magnitude of dengue were influenced by multiple factors, such as mosquito population density, climatic conditions, and population movement patterns. However, there is a lack of studies that combine the above factors to explain their complex nonlinear relationships in dengue transmission and generate accurate predictions. Therefore, to study the complex spatial diffusion of dengue, this research combined the above factors and developed a network model for spatiotemporal transmission prediction of dengue fever using metapopulation networks based on human mobility. For improving the prediction accuracy of the epidemic model, the ensemble adjusted Kalman filter (EAKF), a data assimilation algorithm, was used to iteratively assimilate the observed case data and adjust the model and parameters. Our study demonstrated that the metapopulation network-EAKF system provided accurate predictions for city-level dengue transmission trajectories in retrospective forecasts of 12 cities in Guangdong province, China. Specifically, the system accurately predicts local dengue outbreak magnitude and the temporal peak of the epidemic up to 10 wk in advance. In addition, the system predicted the peak time, peak intensity, and total number of dengue cases more accurately than isolated city-specific forecasts. The general metapopulation assimilation framework presented in our study provides a methodological foundation for establishing an accurate system with finer temporal and spatial resolution for retrospectively forecasting the magnitude and temporal peak of dengue fever outbreaks. These forecasts based on the proposed method can be interoperated to better support intervention decisions and inform the public of potential risks of disease transmission.
预测个别局部疫情的具体规模和时间峰值对于传染病控制至关重要。先前的研究表明，登革热的空间传播和疫情规模存在显著差异，受到多种因素的影响，如蚊子种群密度、气候条件和人口流动模式。然而，目前缺乏将上述因素结合起来解释其在登革热传播中的复杂非线性关系并生成准确预测的研究。因此，为了研究登革热的复杂空间扩散，本研究结合了上述因素，基于人口流动构建了基于泛种群网络的登革热时空传播预测网络模型。为了提高疫情模型的预测精度，使用集合调整卡尔曼滤波（EAKF）数据同化算法来迭代同化观测病例数据并调整模型和参数。我们的研究表明，泛种群网络-EAKF 系统为中国广东省 12 个城市的回溯预测提供了准确的城市级登革热传播轨迹预测。具体来说，该系统可以提前 10 周准确预测局部登革热爆发规模和疫情时间峰值。此外，该系统预测的峰值时间、峰值强度和总登革热病例数比孤立的城市特定预测更准确。本研究提出的一般泛种群同化框架为建立具有更精细时间和空间分辨率的准确系统提供了方法学基础，用于回溯预测登革热疫情的规模和时间峰值。基于该方法的预测可以相互协作，以更好地支持干预决策并向公众通报疾病传播的潜在风险。
