  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.
