🤖 AI Summary
研究使用Variable Band-pass Periodic Block Bootstrap方法，有效识别了纽约市麻疹病例中的周期性成分，为疾病预测与防控提供新工具。
📝 Abstract
Measles, a highly contagious, deadly virus, is at risk of losing its eradication status in the Unites States. Understanding the pattern, including seasonality, of measles could provide great benefit for forecasting, prevention, and public health preparedness as the virus re-emerges. The novel Variable Band-pass Periodic Block Bootstrap (VBPBB), which suppresses noise and interfering signals, is more efficient and statistically powerful to find periodic components compared to other existing methods. Using this method, we have found several significant periodic components in historical New York City measles cases which other methods were incapable of identifying. These findings give us a reusable method for understanding measles and other diseases and greater insight into what may occur should measles vaccine rates continue to fall.