While large scale mobility data has become a popular tool to monitor the mobility patterns during the COVID-19 pandemic, the impacts of non-compulsory measures in Tokyo, Japan on human mobility patterns has been under-studied. Here, we analyze the temporal changes in human mobility behavior, social contact rates, and their correlations with the transmissibility of COVID-19, using mobility data collected from more than 200K anonymized mobile phone users in Tokyo. The analysis concludes that by April 15th (1 week into state of emergency), human mobility behavior decreased by around 50%, resulting in a 70% reduction of social contacts in Tokyo, showing the strong relationships with non-compulsory measures. Furthermore, the reduction in data-driven human mobility metrics showed correlation with the decrease in estimated effective reproduction number of COVID-19 in Tokyo. Such empirical insights could inform policy makers on deciding sufficient levels of mobility reduction to contain the disease.
Despite the importance of predicting evacuation mobility dynamics after large scale disasters for effective first response and disaster relief, our general understanding of evacuation behavior remains limited because of the lack of empirical evidence on the evacuation movement of individuals across multiple disaster instances. Here we investigate the GPS trajectories of a total of more than 1 million anonymized mobile phone users whose positions were tracked for a period of 2 months before and after four of the major earthquakes that occurred in Japan. Through a cross comparative analysis between the four disaster instances, we find that in contrast to the assumed complexity of evacuation decision making mechanisms in crisis situations, an individual’s evacuation probability is strongly dependent on the seismic intensity that they experience. In fact, we show that the evacuation probabilities in all earthquakes collapse into a similar pattern, with a critical threshold at around seismic intensity 5.5. This indicates that despite the diversity in the earthquakes profiles and urban characteristics, evacuation behavior is similarly dependent on seismic intensity. Moreover, we found that probability density functions of the distances that individuals evacuate are not dependent on seismic intensities that individuals experience. These insights from empirical analysis on evacuation from multiple earthquake instances using large scale mobility data contributes to a deeper understanding of how people react to earthquakes, and can potentially assist decision makers to simulate and predict the number of evacuees in urban areas with little computational time and cost. This can be achieved by utilizing only the information on population density distribution and seismic intensity distribution, which can be observed instantaneously after the shock.
We propose a new surgical robotic system for intrauterine fetal surgery in an Open MRI. The target disease of the fetal surgery is spina bifida or myelomeningocele that is incomplete closure in the spinal column and one of the common fetal diseases. In the proposed surgical process, the abdominal wall and uterine wall would not widely be opened but rather surgical instruments inserted through the small holes in both walls to perform minimally invasive surgery. In this paper, a prototype of the micro manipulator of diameter is 2.4mm and bending radius 2.45 mm is presented. The diameter and bending radius of this manipulator is one of the smallest ever developed among surgical robots to the best of the knowledge of the investigating authors.The mechanism of the manipulator includes two ball joints and is driven using four wires able to bend through 90 degrees in any direction. The features of the mechanism include a small diameter, small bending radius, ease of fabrication, high rigidity and applicability for other surgical applications. Although the manipulator is not yet MRI compatible, the feature of the prototype demonstrated the feasibility of robotic intrauterine fetal surgery.
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