Comparative analysis of prevention and control measures toward COVID-19 epidemic between Shanghai and Beijing.

Purpose: By serving and providing a guide for other regional places, this study aims to advance and guide the epidemic prevention and control methods, and practices and strengthen people's ability to respond to COVID-19 and other future potential public health risks. Design/methodology/approach: A comparative analysis was conducted that the COVID-19 epidemic development trend and prevention and control effects both in Beijing and Shanghai. In fact, regarding the COVID-19 policy and strategic areas, the differences between governmental, social, and professional management were discussed and explored. To prevent and be ready for potential pandemics, experience and knowledge were used and summarized. Findings: The strong attack of the Omicron variant in early 2022 has posed challenges to epidemic prevention and control practices in many Chinese cities. Shanghai, which had achieved relatively good performance in the fight against the epidemic, has exposed limitations in its epidemic prevention and control system in the face of Omicron. In fact, the city of Beijing has undertaken prompt and severe lockdown measures and achieved rather good results in epidemic prevention and control because of learning from Shanghai's experience and lessons; adhering to the overall concept of "dynamic clearing," implementing precise prevention and monitoring, enhancing community control, and making emergency plans and preparations. All these actions and measures are still essential in the shift from pandemic response to pandemic control. Research limitations/implications: Different places have introduced different urgent policies to control the spread of the pandemic. Strategies to control COVID-19 have often been based on preliminary and limited data and have tended to be slow to evolve as new evidence emerges. Hence, the effects of these anti-epidemic policies need to be further tested.

Strategies for the Emergency Treatment of Pregnant Women with Neurological Symptoms during the COVID-19 Pandemic.

Coronavirus disease-19 (COVID-19) has been spreading all over the world for more than two years. Though several kinds of vaccines are currently available, emergence of new variants, spike mutations and immune escape have raised new challenges. Pregnant women are vulnerable to respiratory infections due to their altered immune defence and surveillance functions. Besides, whether pregnant persons should receive a COVID-19 vaccine is still under debate because limited data are available on the efficacy and safety of receiving a vaccine during pregnancy. Physiological features and lack of effective protection making pregnant women at high risk of getting infected. Another concern is that pregnancy may trigger the onset of underlying existing neurological disease, which is highly similar to those neurological symptoms of pregnant women caused by COVID-19. These similarities interfere with diagnosis and delay timely and effective management. Therefore, providing efficient emergency support for pregnant women suffering from neurological symptoms caused by COVID-19 remains a challenge among neurologists and obstetricians. To improve the diagnosis and treatment efficiency of pregnant women with neurological symptoms, we propose an emergency management framework based on the clinicians' experience and available resources. This emergency care system aimed at addressing the conundrums faced by the emergency guarantee system under COVID-19 pandemic and could serve as a potential multisystem project for clinical practice and medical education.

Constructing big data prevention and control model for public health emergencies in China: A grounded theory study.

Big data technology plays an important role in the prevention and control of public health emergencies such as the COVID-19 pandemic. Current studies on model construction, such as SIR infectious disease model, 4R crisis management model, etc., have put forward decision-making suggestions from different perspectives, which also provide a reference basis for the research in this paper. This paper conducts an exploratory study on the construction of a big data prevention and control model for public health emergencies by using the grounded theory, a qualitative research method, with literature, policies, and regulations as research samples, and makes a grounded analysis through three-level coding and saturation test. Main results are as follows: (1) The three elements of data layer, subject layer, and application layer play a prominent role in the digital prevention and control practice of epidemic in China and constitute the basic framework of the "DSA" model. (2) The "DSA" model integrates cross-industry, cross-region, and cross-domain epidemic data into one system framework, effectively solving the disadvantages of fragmentation caused by "information island". (3) The "DSA" model analyzes the differences in information needs of different subjects during an outbreak and summarizes several collaborative approaches to promote resource sharing and cooperative governance. (4) The "DSA" model analyzes the specific application scenarios of big data technology in different stages of epidemic development, effectively responding to the disconnection between current technological development and realistic needs.

Practice of big data and artificial intelligence in epidemic surveillance and containment.

Faced with the current time-sensitive COVID-19 pandemic, the overburdened healthcare systems resulted in a strong demand to develop newer methods to control the spread of the pandemic. Big data and artificial intelligence (AI) have been leveraged amid the COVID-19 pandemic; however, little is known about its use for supporting public health efforts. In epidemic surveillance and containment, efforts are needed to treat critical patients, track and manage the health status of residents, isolate suspected cases, develop vaccines and antiviral drugs. The applications of emerging practices of artificial intelligence and big data have become powerful "weapons" to fight against the pandemic and provide strong support in pandemic prevention and control, such as early warning, analysis and judgment, interruption and intervention of epidemic, to achieve goals of early detection, early report, early diagnosis, early isolation and early treatment, and these are the decisive factors to control the spread of the epidemic and reduce the mortality. This paper systematically summarizes the application of big data and AI in epidemic, and describes practical cases and challenges with emphasis in epidemic prevention and control. The included studies showed that big data and AI have the potential strength to fight against COVID-19. However, many of the proposed methods are not yet widely accepted. Thus, the most rewarding research will be on methods promising value beyond COVID-19. More efforts are needed for developing standardized reporting protocols or guidelines for practice.

Driving mechanism of consumer migration behavior under the COVID-19 pandemic.

Introduction: China is now in the post-period of COVID-19 epidemic prevention and control. While facing normalized epidemic prevention and control, consumers behavioral intention and decision-making will still be influenced by the epidemic's development and the implementation of specific epidemic prevention measures in the medium to long term. With the impact of external epidemic prevention environment and measures, consumers' channel behavior has changed. How to better promote channel integration by adopting consumers' channel migration behavior is important for channel coordination strategies. Methods: This paper takes fresh product retailing under normal epidemic prevention and control as an example and examines the change in channel migration behavior. Based on the value-based adoption model (VAM), this paper discusses the influence of channel characteristics and channel switching costs on channel migration intention, the mediating effect of perceived value between various influencing factors and channel migration intention, and the moderating effect of channel switching cost on perceived value and channel migration intention. Thus, an empirical study was carried out with 292 samples to verify the hypotheses. Results: The results show that under normal epidemic prevention and control, the influencing factors in the VAM model have a significant impact on channel migration intention; perceived value plays a mediating role between various influencing factors and channel migration intention. Discussions: The COVID-19 pandemic has had a significant effect on daily life and purchasing behavior. In the context of this pandemic, we have confirmed that consumers will probably change to other retailers when the usefulness, entertainment, and cost meet their expectation for purchasing fresh products. Channel characteristics have versatile features, such as channel structure and supply chain mode, which affect consumer behaviors in different ways. The perceived value comes from expectations and experience. Retailers should try to keep their products fresh and provide consumers with a high-level shopping experience during sale.

Reflections on the Pain Points of Elderly Care during the Epidemic Prevention and Control Stage from Two Cases of Disease Narration

The COVID-19 has had a profound impact on human society, the elderly, as a vulnerable group, are the most affected. Based on two cases of disease narrative collected by the department of neurology of a hospital in Guiyang, this paper analyzed the shortcomings of elderly care in the context of epidemic prevention and control. The overall health information literacy of the elderly was low, which made it difficult to obtain correct epidemic related information. The lifestyle of the elderly has changed during the epidemic prevention and control stag. The long time isolation at home has reduced their constitution and made them prone to illness, thus affecting the quality of healthy elderly care. In this stage, the psychological burden of the middle-aged and elderly people has been increasing, which reduced the life satisfaction and subjective well-being of the elderly, and increased the risk of depression. In view of these outstanding problems, this paper proposed corresponding measures, aiming to improve the quality of life and physical and mental health of the elderly during the epidemic prevention and control stage, and provide reference for realizing healthy aging in China. © 2023, Editorial department of Chinese Medical Ethics. All rights reserved.

Designing the intelligent epidemic control robot control system for epidemic prevention and control

In response to the current problem of highly contagious new coronavirus and repeated epidemics, which cause great threat and inconvenience to people's production and life, In this paper, a multifunctional intelligent epidemic prevention robot control system based on a single chip microcomputer is designed to realize the intelligent management of community epidemic prevention and control. Stm32 microcontroller is used as the control core. In order to improve the efficiency of prevention and control management and reduce contact, the Jetson Nano controller is designed to provide map reproduction, positioning navigation, and path planning functions. It is used to summarize patient status information quickly and efficiently, the design provides face recognition and remote monitoring functions to realize real-time uploading of accurate data to cell phone console APP and computer terminal integrated monitoring platform. Through the map reconstruction and positioning simulation test, an optimal path is selected to ensure the stable movement of the epidemic prevention robot. The face_recognition algorithm's error reception rate, error rejection rate, and accuracy rate are 0.35%, 11.12%, and 88.53%, respectively, which are better than the face-net algorithm in three aspects and can well meet the needs of small communities. The face recognition needs of small community areas can be well met. This epidemic prevention and control system can realize efficient community epidemic prevention and control management, reduce contact transmission, and lower the difficulty of epidemic prevention and control. © 2022 IEEE.