Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Republic of Singapore.
PLoS Comput Biol. 2022 Apr 1;18(4):e1009979. doi: 10.1371/journal.pcbi.1009979. eCollection 2022 Apr.
As the most widespread viral infection transmitted by the Aedes mosquitoes, dengue has been estimated to cause 51 million febrile disease cases globally each year. Although sustained vector control remains key to reducing the burden of dengue, current understanding of the key factors that explain the observed variation in the short- and long-term vector control effectiveness across different transmission settings remains limited. We used a detailed individual-based model to simulate dengue transmission with and without sustained vector control over a 30-year time frame, under different transmission scenarios. Vector control effectiveness was derived for different time windows within the 30-year intervention period. We then used the extreme gradient boosting algorithm to predict the effectiveness of vector control given the simulation parameters, and the resulting machine learning model was interpreted using Shapley Additive Explanations. According to our simulation outputs, dengue transmission would be nearly eliminated during the early stage of sustained and intensive vector control, but over time incidence would gradually bounce back to the pre-intervention level unless the intervention is implemented at a very high level of intensity. The time point at which intervention ceases to be effective is strongly influenced not only by the intensity of vector control, but also by the pre-intervention transmission intensity and the individual-level heterogeneity in biting risk. Moreover, the impact of many transmission model parameters on the intervention effectiveness is shown to be modified by the intensity of vector control, as well as to vary over time. Our study has identified some of the critical drivers for the difference in the time-varying effectiveness of sustained vector control across different dengue endemic settings, and the insights obtained will be useful to inform future model-based studies that seek to predict the impact of dengue vector control in their local contexts.
登革热是由伊蚊传播的最广泛的病毒性感染病，据估计，每年在全球范围内会导致 5100 万例发热病例。尽管持续的病媒控制仍然是减轻登革热负担的关键，但目前对于解释不同传播环境下短期和长期病媒控制效果观察到的差异的关键因素的理解仍然有限。我们使用详细的基于个体的模型，在不同的传播场景下，模拟了 30 年内有和没有持续病媒控制的登革热传播情况。在 30 年的干预期间，我们为不同的时间窗口计算了病媒控制的效果。然后，我们使用极端梯度增强算法根据模拟参数预测病媒控制的效果，并用 Shapley 加法解释来解释得到的机器学习模型。根据我们的模拟结果，在持续和密集的病媒控制的早期阶段，登革热传播几乎会被消除，但随着时间的推移，除非干预强度非常高，否则发病率将逐渐回升到干预前的水平。干预停止有效的时间点不仅受到病媒控制强度的影响，还受到干预前传播强度和个体叮咬风险异质性的影响。此外，许多传播模型参数对干预效果的影响不仅受到病媒控制强度的影响，而且还随时间而变化。我们的研究确定了一些关键驱动因素，这些因素导致了不同登革热流行环境下持续病媒控制效果随时间的变化不同，获得的见解将有助于为未来旨在预测其在当地环境中影响的基于模型的研究提供信息。
