The COVID-19 pandemic has created unprecedented challenges for governments and healthcare systems worldwide, highlighting the critical importance of understanding the factors that contribute to virus transmission. This study aimed to identify the most influential age groups in COVID-19 infection rates at the US county level using the Modified Morris Method and deep learning for time series. Our approach involved training the state-of-the-art time-series model Temporal Fusion Transformer on different age groups as a static feature and the population vaccination status as the dynamic feature. We analyzed the impact of those age groups on COVID-19 infection rates by perturbing individual input features and ranked them based on their Morris sensitivity scores, which quantify their contribution to COVID-19 transmission rates. The findings are verified using ground truth data from the CDC and US Census, which provide the true infection rates for each age group. The results suggest that young adults were the most influential age group in COVID-19 transmission at the county level between March 1, 2020, and November 27, 2021. Using these results can inform public health policies and interventions, such as targeted vaccination strategies, to better control the spread of the virus. Our approach demonstrates the utility of feature sensitivity analysis in identifying critical factors contributing to COVID-19 transmission and can be applied in other public health domains.

翻译：新冠疫情给全球各国政府和医疗系统带来了前所未有的挑战，凸显了理解病毒传播影响因素的关键重要性。本研究旨在利用改进莫里斯方法和深度学习时间序列分析，识别美国县级层面新冠感染率中最具影响力的年龄组。我们的方法是将最先进的时间序列模型Temporal Fusion Transformer应用于不同年龄组（作为静态特征）和人口疫苗接种状态（作为动态特征）进行训练。通过扰动单个输入特征，我们分析了这些年龄组对新冠感染率的影响，并根据其莫里斯敏感性得分（可量化其对新冠传播率的贡献）进行排序。研究结果使用来自美国疾控中心和人口普查局的真实数据（提供了每个年龄组的真实感染率）进行了验证。结果表明，在2020年3月1日至2021年11月27日期间，年轻人是县级层面新冠传播中最具影响力的年龄组。利用这些结果可为公共卫生政策与干预措施（如针对性疫苗接种策略）提供信息，从而更好地控制病毒传播。我们的方法证明了特征敏感性分析在识别新冠传播关键影响因素方面的实用性，并可应用于其他公共卫生领域。