PDF(Pubmed)
Abstract：
Social distancing interrupted transmission patterns of contact-driven infectious agents such as norovirus during the Covid-19 pandemic. Since routine surveillance of norovirus was additionally disrupted during the pandemic, traditional naïve forecasts that rely only on past public health surveillance data may not reliably represent norovirus activity. This study investigates the use of statistical modelling to predict the number of norovirus laboratory reports in England 4-weeks ahead of time before and during Covid-19 pandemic thus providing insights to inform existing practices in norovirus surveillance in England. We compare the predictive performance from three forecasting approaches that assume different underlying structure of the norovirus data and utilized various external data sources including mobility, air temperature and relative internet searches (Time Series and Regularized Generalized Linear Model, and Quantile Regression Forest). The performance of each approach was evaluated using multiple metrics, including a relative prediction error against the traditional naive forecast of a five-season mean. Our data suggest that all three forecasting approaches improve predictive performance over the naïve forecasts, especially in the 2020/21 season (30-45% relative improvement) when the number of norovirus reports reduced. The improvement ranged from 7 to 22% before the pandemic. However, performance varied: regularized regression incorporating internet searches showed the best forecasting score pre-pandemic and the time series approach achieved the best results post pandemic onset without external data. Overall, our results demonstrate that there is a significant value for public health in considering the adoption of more sophisticated forecasting tools, moving beyond traditional naïve methods, and utilizing available software to enhance the precision and timeliness of norovirus surveillance in England.
摘要：
在Covid-19大流行期间，社会距离中断了接触驱动的传染性病原体如诺如病毒的传播模式。由于在大流行期间对诺如病毒的常规监测也被中断，仅依赖于过去的公共卫生监测数据的传统天真预测可能无法可靠地代表诺如病毒的活动。这项研究调查了使用统计模型来预测Covid-19大流行之前和期间英格兰诺如病毒实验室报告的数量提前4周，从而为英格兰诺如病毒监测的现有做法提供见解。我们比较了三种预测方法的预测性能，这些方法假设诺如病毒数据的基础结构不同，并利用了各种外部数据源，包括移动性，气温和相对互联网搜索(时间序列和正则化广义线性模型，和分位数回归森林)。使用多个指标评估每种方法的性能，包括相对于传统的五个季节平均值的幼稚预测的相对预测误差。我们的数据表明，这三种预测方法都比幼稚预测提高了预测性能，特别是在2020/21赛季（30-45％相对改善），当诺如病毒报告数量减少。大流行前的改善幅度为7％至22％。然而,性能各不相同:结合互联网搜索的正则回归显示了大流行前的最佳预测分数，时间序列方法在大流行后没有外部数据。总的来说,我们的结果表明，考虑采用更复杂的预测工具对公共卫生有重要的价值，超越传统的幼稚方法，并利用可用的软件来提高英格兰诺如病毒监测的准确性和及时性。
