Department of Epidemiology, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan 250012, Shandong, China; Shandong University Climate Change and Health Center, Jinan 250012, Shandong, China.
Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan 250012, Shandong, China; Shandong University Climate Change and Health Center, Jinan 250012, Shandong, China.
Sci Total Environ. 2021 Mar 20;761:144093. doi: 10.1016/j.scitotenv.2020.144093. Epub 2020 Dec 14.
Accurate and timely forecasts of bacillary dysentery (BD) incidence can be used to inform public health decision-making and response preparedness. However, our ability to detect BD dynamics and outbreaks remains limited in China.
This study aims to explore the impacts of meteorological factors on BD transmission in four representative regions in China and to forecast weekly number of BD cases and outbreaks.
Weekly BD and meteorological data from 2014 to 2016 were collected for Beijing (Northern China), Shenyang (Northeast China), Chongqing (Southwest China) and Shenzhen (Southern China). A boosted regression tree (BRT) model was conducted to assess the impacts of meteorological factors on BD transmission. Then a real-time forecast and early warning model based on BRT was developed to track the dynamics of BD and detect the outbreaks. The forecasting methodology was compared with generalized additive model (GAM) and seasonal autoregressive integrated moving average model (SARIMA) that have been used to model the BD case data previously.
Ambient temperature was the most important meteorological factor contributing to the transmission of BD (80.81%-92.60%). A positive effect of temperature was observed when weekly mean temperature exceeded 4 °C, -3 °C, 9 °C and 16 °C in Beijing (Northern China), Shenyang (Northeast China), Chongqing (Southwest China) and Shenzhen (Southern China), respectively. BD incidence (Beijing and Shenyang) in temperate cities was more sensitive to high temperature than that in subtropical cities (Chongqing and Shenzhen). The dynamics and outbreaks of BD can be accurately forecasted and detected by the BRT model. Compared to GAM and SARIMA, BRT model showed more accurate forecasting for 1-, 2-, 3-weeks ahead forecasts in Beijing, Shenyang and Shenzhen.
Temperature plays the most important role in weather-attributable BD transmission. The BRT model achieved a better performance in comparison with GAM and SARIMA in most study cities, which could be used as a more accurate tool for forecasting and outbreak alert of BD in China.
准确及时的细菌性痢疾（BD）发病率预测可用于为公共卫生决策和应对准备提供信息。然而，我们在中国检测 BD 动态和暴发的能力仍然有限。
本研究旨在探讨气象因素对中国四个代表性地区 BD 传播的影响，并预测每周 BD 病例和暴发的数量。
收集了 2014 年至 2016 年北京（中国北方）、沈阳（中国东北）、重庆（中国西南）和深圳（中国南方）的每周 BD 和气象数据。采用增强回归树（BRT）模型评估气象因素对 BD 传播的影响。然后，建立了基于 BRT 的实时预测和预警模型，以跟踪 BD 的动态并检测暴发。该预测方法与之前用于模拟 BD 病例数据的广义加性模型（GAM）和季节性自回归综合移动平均模型（SARIMA）进行了比较。
环境温度是影响 BD 传播的最重要气象因素（80.81%-92.60%）。当每周平均温度分别超过 4°C、-3°C、9°C 和 16°C 时，北京（中国北方）、沈阳（中国东北）、重庆（中国西南）和深圳（中国南方）的温度对 BD 的影响呈正相关。温带城市的 BD 发病率（北京和沈阳）对高温比亚热带城市（重庆和深圳）更敏感。BRT 模型可以准确预测和检测 BD 的动态和暴发。与 GAM 和 SARIMA 相比，BRT 模型在北京、沈阳和深圳的 1 周、2 周和 3 周的预测中表现出更高的准确性。
温度在天气归因 BD 传播中起着最重要的作用。与 GAM 和 SARIMA 相比，BRT 模型在大多数研究城市中的表现更好，可作为中国 BD 预测和暴发预警的更准确工具。
