Roster K O, Martinelli T, Connaughton C, Santillana M, Rodrigues F A
Institute of Mathematics and Computer Science, University of São Paulo, São Carlos, SP, Brazil.
Mathematics Institute, University of Warwick, Coventry, United Kingdom.
Res Sq. 2023 Feb 9:rs.3.rs-2548491. doi: 10.21203/rs.3.rs-2548491/v1.
Atypical dengue prevalence was observed in 2020 in many dengue-endemic countries, including Brazil. Evidence suggests that the pandemic disrupted not only dengue dynamics due to changes in mobility patterns, but also several aspects of dengue surveillance, such as care seeking behavior, care availability, and monitoring systems. However, we lack a clear understanding of the overall impact on dengue in different parts of the country as well as the role of individual causal drivers. In this study, we estimated the gap between expected and observed dengue cases in 2020 using an interrupted time series design with forecasts from a neural network and a structural Bayesian time series model. We also decomposed the gap into the impacts of climate conditions, pandemic-induced changes in reporting, human susceptibility, and human mobility. We find that there is considerable variation across the country in both overall pandemic impact on dengue and the relative importance of individual drivers. Increased understanding of the causal mechanisms driving the 2020 dengue season helps mitigate some of the data gaps caused by the COVID-19 pandemic and is critical to developing effective public health interventions to control dengue in the future.
2020年,在包括巴西在内的许多登革热流行国家都观察到了非典型登革热的流行情况。有证据表明,新冠疫情不仅因流动模式的变化扰乱了登革热的流行动态,还影响了登革热监测的多个方面,如就医行为、医疗可及性和监测系统。然而,我们对其在该国不同地区对登革热的总体影响以及各个因果驱动因素的作用仍缺乏清晰的认识。在本研究中,我们使用中断时间序列设计,并结合神经网络和结构化贝叶斯时间序列模型的预测,估算了2020年预期登革热病例与实际观察到的登革热病例之间的差距。我们还将这一差距分解为气候条件、疫情导致的报告变化、人群易感性和人员流动的影响。我们发现,该国各地在疫情对登革热的总体影响以及各个驱动因素的相对重要性方面存在很大差异。加强对2020年登革热季节因果机制的理解,有助于弥补新冠疫情造成的一些数据缺口,对于未来制定有效的公共卫生干预措施以控制登革热至关重要。