Human Dense Avoidance Based on Coverage Control through Robots - A Patrol Algorithm Taking into Account of Visible Region

In this paper, we propose an efficient method for human dense avoidance based on a coverage control of multi-agent system. Our motivation is to contribute to an avoidance of human density in the current situation of COVID-19. We also aim to avoid crowding in social events and public spaces. Firstly, we consider a situation in which a robot autonomously patrols a region where human density occurs. As a main result, we propose a patrol algorithm in which robots distribute a cluster of humans by a coverage control if they discover them. Finally, we show an efficiency of the method based on a numerical simulation. © 2022 The Society of Instrument and Control Engineers - SICE.

Detection of Influenza and Other Respiratory Viruses in Air Sampled From a University Campus: A Longitudinal Study.

BACKGROUND: Respiratory virus-laden particles are commonly detected in the exhaled breath of symptomatic patients or in air sampled from healthcare settings. However, the temporal relationship of detecting virus-laden particles at nonhealthcare locations vs surveillance data obtained by conventional means has not been fully assessed. METHODS: From October 2016 to June 2018, air was sampled weekly from a university campus in Hong Kong. Viral genomes were detected and quantified by real-time reverse-transcription polymerase chain reaction. Logistic regression models were fitted to examine the adjusted odds ratios (aORs) of ecological and environmental factors associated with the detection of virus-laden airborne particles. RESULTS: Influenza A (16.9% [117/694]) and influenza B (4.5% [31/694]) viruses were detected at higher frequencies in air than rhinovirus (2.2% [6/270]), respiratory syncytial virus (0.4% [1/270]), or human coronaviruses (0% [0/270]). Multivariate analyses showed that increased crowdedness (aOR, 2.3 [95% confidence interval {CI}, 1.5-3.8]; P < .001) and higher indoor temperature (aOR, 1.2 [95% CI, 1.1-1.3]; P < .001) were associated with detection of influenza airborne particles, but absolute humidity was not (aOR, 0.9 [95% CI, .7-1.1]; P = .213). Higher copies of influenza viral genome were detected from airborne particles >4 µm in spring and <1 µm in autumn. Influenza A(H3N2) and influenza B viruses that caused epidemics during the study period were detected in air prior to observing increased influenza activities in the community. CONCLUSIONS: Air sampling as a surveillance tool for monitoring influenza activity at public locations may provide early detection signals on influenza viruses that circulate in the community.