利用医护人员感染数据探索漏报病例对COVID-19时空分布的影响
Exploring the impact of under-reported cases on the COVID-19 spatiotemporal distribution using healthcare worker infection data
AI总结：
本文提出一种利用医护人员感染数据评估漏报对COVID-19时空分布影响的新框架，并以武汉和湖北为例揭示了漏报在疫情早期和早期发病地区影响更大、报告病例与医护人员病例存在高空间相关性等规律。
摘要：
及时了解COVID-19的时空格局和发展趋势对于及时防控至关重要。然而，病例漏报在与公共卫生相关的领域中普遍存在。如果不考虑漏报现象，就有可能得出有偏的推断并制定不恰当的防控政策。因此，本文提出了一种新的框架来探索漏报对COVID-19时空分布的影响，并利用武汉和湖北（除武汉外）的医护人员感染数据进行了实证分析。结果表明：（1）对数正态分布最适合描述疫情随时间的演变；（2）报告病例的估计感染高峰时间滞后于医护人员病例的感染高峰时间，且报告病例的估计感染时间区间小于医护人员病例的感染时间区间；（3）漏报病例对疫情早期阶段的影响大于对后期阶段的影响，对早期发病地区的影响大于对晚期发病地区的影响；（4）尽管报告病例数低于实际病例数，但累计报告病例与医护人员病例之间存在较高的空间相关性。本研究提出的框架具有高度可扩展性，相关研究人员可以利用其他县的数据来源开展类似研究。
英文摘要：
A timely understanding of the spatiotemporal pattern and development trend of COVID-19 is critical for timely prevention and control. However, the under-reporting of cases is widespread in fields associated with public health. It is also possible to draw biased inferences and formulate inappropriate prevention and control policies if the phenomenon of under-reporting is not taken into account. Therefore, in this paper, a novel framework was proposed to explore the impact of under-reporting on COVID-19 spatiotemporal distributions, and empirical analysis was carried out using infection data of healthcare workers in Wuhan and Hubei (excluding Wuhan). The results show that (1) the lognormal distribution was the most suitable to describe the evolution of epidemic with time; (2) the estimated peak infection time of the reported cases lagged the peak infection time of the healthcare worker cases, and the estimated infection time interval of the reported cases was smaller than that of the healthcare worker cases. (3) The impact of under-reporting cases on the early stages of the pandemic was greater than that on its later stages, and the impact on the early onset area was greater than that on the late onset area. (4) Although the number of reported cases was lower than the actual number of cases, a high spatial correlation existed between the cumulatively reported cases and healthcare worker cases. The proposed framework of this study is highly extensible, and relevant researchers can use data sources from other counties to carry out similar research.
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