Dynamical variations of the COVID-19 with the SARS-CoV-2 Omicron of Kazakhstan and Pakistan (preprint)

Background The ongoing coronavirus disease 2019 (COVID-19) pandemic with the Severe Acute Respiratory Syndrome-Coronavirus 2 (SARS-CoV-2) Omicron virus bring a formidable challenge to control and prevention for all the countries, especially for the low development countries. Hence, taking Kazakhstan and Pakistan as examples, this study aims to explore the COVID-19 transmissions with the omicron virus at different contact rates, quarantined rates and test rates.  Methods A disease dynamical model is applied in which the population is divided into different individuals. Three time stages are divided according to the omicron transmission characteristics: first outbreak period, calm period and the second outbreak period. The population contact, population quarantine and disease test are considered with five different scenarios to analysis their impacts on the disease. Four statistical metrics are employed to quantify the model's performance, including the correlation coefficient (CC), normalized absolute error (NAE), normalized root mean square error (RMSE) and distance between indices of simulation and observation (DISO).  Results Our model has high performance in simulating the COVID-19 transmission in the two countries with high CC bigger than 0.9, and the DISO smaller than 0.5. Compared with the present measures (base line), decreasing (increasing) the contact rates or increasing (decreasing) the quarantined rates can reduce (increase) the peak values of daily new cases and forward (delay) the peak value times. The impacts of the test rates on the disease are weak. When the start times of stage II is brought forward 6 days, the daily new cases are more than 8 and 5 times for Kazakhstan and Pakistan. The impacts of the start times of the stage III on the disease are contradict with the stage II.  Conclusions For the two low development countries: Kazakhstan and Pakistan, stronger intensity of control and prevention measures can be more effective on the COVID-19 control. Therefore, to reduce or cut down the omicron transmission, the strict management of the population movement should be employed. Moreover, the timely application of the strategies also plays a key role in the disease control.

Deep‐Learning‐Assisted Noncontact Gesture‐Recognition System for Touchless Human‐Machine Interfaces

Human‐machine interfaces (HMIs) play important role in the communication between humans and robots. Touchless HMIs with high hand dexterity and hygiene hold great promise in medical applications, especially during the pandemic of coronavirus disease 2019 (COVID‐19) to reduce the spread of virus. However, current touchless HMIs are mainly restricted by limited types of gesture recognition, the requirement of wearing accessories, complex sensing platforms, light conditions, and low recognition accuracy, obstructing their practical applications. Here, an intelligent noncontact gesture‐recognition system is presented through the integration of a triboelectric touchless sensor (TTS) and deep learning technology. Combined with a deep‐learning‐based multilayer perceptron neural network, the TTS can recognize 16 different types of gestures with a high average accuracy of 96.5%. The intelligent noncontact gesture‐recognition system is further applied to control a robot for collecting throat swabs in a noncontact mode. Compared with present touchless HMIs, the proposed system can recognize diverse complex gestures by utilizing charges naturally carried on human fingers without the need of wearing accessories, complicated device structures, adequate light conditions, and achieves high recognition accuracy. This system could provide exciting opportunities to develop a new generation of touchless medical equipment, as well as touchless public facilities, smart robots, virtual reality, metaverse, etc. [ FROM AUTHOR] Copyright of Advanced Functional Materials is the property of John Wiley & Sons, Inc. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Analyses of pregnant women's susceptibility to COVID-19 and treatment strategies

Corona virus disease-19(COVID-19) has stronger infectivity and pathogenicity. As a special population, pregnant women have different immune states and body functions from the general population. Pregnant women might be in a state of immunosuppression, and the expressions and activities of angiotensin converting enzyme 2(ACE2) are up-regulated and increased during pregnancy. The above suggests that pregant women may be more susceptible to COVID-19. Present clinical reports suggested that COVID-19 might not increase the incidence of severe maternal diseases and adverse pregnancy outcomes. The authors believed that pregnant women infected by COVID-19 should be timely diagnosed and treated, and their medication should be more careful, as compared with that for ordinary patients. Based on previous clinical experience, Diagnosis and Treatment Program Tongji Recommended for TCM Treatment of Pregnant Patients with COVID-19 was developed for clinical reference.