论文标题
谁是“沉默的播放器”？：在时空记忆模型中联系跟踪
Who Are the `Silent Spreaders'?: Contact Tracing in Spatio-Temporal Memory Models
论文作者
论文摘要
Covid-19-19席卷了世界一年多。但是，大量的传染性无症状COVID-19病例（\ textit {acc} s）仍使传输链的分解非常困难。流行病学研究人员在许多国家的努力已经阐明了ACC的临床特征，但是仍然缺乏检测ACC的实际方法，以帮助遏制大流行。为了解决ACC的问题，本文提出了一个神经网络模型，称为COVID-19（\ textit {stem-Covid}）的称为时空情节内存，以从触点跟踪数据中识别ACC。基于融合自适应共振理论（\ textit {art}），该模型编码了个体的集体时空情节记忆，并结合了对ACC的并行搜索的有效机制。具体而言，使用加权证据合并方法，使用了确定的阳性病例的发作痕迹来绘制可疑ACC的情节痕迹。为了评估茎循环的疗效，基于现实的代理模拟模型是基于ACC的最新流行病学发现，实施了COVID-19扩散。基于严格的模拟场景的实验表现出了COVID-19的当前状况，表明具有加权证据池的干式旋转模型具有更高的准确性和效率，可在与几个基线相比识别ACC。此外，该模型对嘈杂数据和不同的PACT比例表现出强大的鲁棒性，这部分反映了疫苗接种后突破性感染对病毒传播的影响。
The COVID-19 epidemic has swept the world for over a year. However, a large number of infectious asymptomatic COVID-19 cases (\textit{ACC}s) are still making the breaking up of the transmission chains very difficult. Efforts by epidemiological researchers in many countries have thrown light on the clinical features of ACCs, but there is still a lack of practical approaches to detect ACCs so as to help contain the pandemic. To address the issue of ACCs, this paper presents a neural network model called Spatio-Temporal Episodic Memory for COVID-19 (\textit{STEM-COVID}) to identify ACCs from contact tracing data. Based on the fusion Adaptive Resonance Theory (\textit{ART}), the model encodes a collective spatio-temporal episodic memory of individuals and incorporates an effective mechanism of parallel searches for ACCs. Specifically, the episodic traces of the identified positive cases are used to map out the episodic traces of suspected ACCs using a weighted evidence pooling method. To evaluate the efficacy of STEM-COVID, a realistic agent based simulation model for COVID-19 spreading is implemented based on the recent epidemiological findings on ACCs. The experiments based on rigorous simulation scenarios, manifesting the current situation of COVID-19 spread, show that the STEM-COVID model with weighted evidence pooling has a higher level of accuracy and efficiency for identifying ACCs when compared with several baselines. Moreover, the model displays strong robustness against noisy data and different ACC proportions, which partially reflects the effect of breakthrough infections after vaccination on the virus transmission.
