Integrated surveillance and early warning system for emerging infectious diseases based on One Health concept: Structures and innovations.

One Health is an upgrade and optimization of health concepts, which recognizes the integrated health of the human-animal-environment. It emphasizes the use of interdisciplinary collaboration, multi-sectoral coordination, and multi-organizational One Health approaches to solve scientific questions. The surveillance and early warning system is the basis of public health emergency prevention and control. The COVID-19 pandemic and the emerging infectious disease (EID) have put great challenges on the existing surveillance and early warning systems worldwide. Guided by the concept of One Health, we attempt to build a new pattern of integrated surveillance and early warning system for EID. We will detail the system including the One Health-based organizational structure, zoonotic and environmental science surveillance network, EID reporting process, and support and guarantee from education and policy. The integrated surveillance and early warning system for EID constructed here has practical and application prospects, and will provide guidance for the prevention and control of COVID-19 and the possible EID in the future.Copyright © 2022, National Institute of Parasitic Diseases. All rights reserved.

Integrated surveillance and early warning system for emerging infectious diseases based on One Health concept: Structures and innovations

One Health is an upgrade and optimization of health concepts, which recognizes the integrated health of the human-animal-environment. It emphasizes the use of interdisciplinary collaboration, multi-sectoral coordination, and multi-organizational One Health approaches to solve scientific questions. The surveillance and early warning system is the basis of public health emergency prevention and control. The COVID-19 pandemic and the emerging infectious disease (EID) have put great challenges on the existing surveillance and early warning systems worldwide. Guided by the concept of One Health, we attempt to build a new pattern of integrated surveillance and early warning system for EID. We will detail the system including the One Health-based organizational structure, zoonotic and environmental science surveillance network, EID reporting process, and support and guarantee from education and policy. The integrated surveillance and early warning system for EID constructed here has practical and application prospects, and will provide guidance for the prevention and control of COVID-19 and the possible EID in the future. © 2022, National Institute of Parasitic Diseases. All rights reserved.

Time phased scheduling and Disease grading management to achieve efficient rescue of batches of COVID-19 infected patients in shelter hospital

Objective To introduce how to quickly set up a doctor team to achieve efficient treatment of batchs COVID-19 patients in Changchun GongTi shelter hospital. Methods A cross-sectional study was conducted to analyze the basic situation of the doctors who supported the Changchun Gongti shelter hospital. The workload is the total number of patients from April 3 to 28, 2022. At the beginning of the task, the first week and the third week of the task, the five point scoring method was used to reflect the doctor's physical and mental state, stress state and rescue achievement. The time phased scheduling and disease grading management were fully implemented after 10 days of operation in the shelter. The doctors' ward round efficiency and self scoring changes before and after the implementation of the plan were compared, and the rescue results were summarized. Results Total of 56 doctors (the Sichuan medical assistance team to Changchun), who undertook the work of Changchun Gongti shelter Hospital, came from 12 professional departments of 14 hospitals. By internal and external linkage-time phased scheduling and information-based patient zoning and grading management, the admission time of batch patients was shortened from (14.64±10.09) min to (6.80±5.10) min per person(P<0.05), the number of patients that each doctor can view per hour ranges from (28.50±12.26) to (68.43±19.95) (P<0.01). A total of 1 293 patients were treated. There were no deaths, no accidents and no mild illness to severe illness in shelter hospital. 35 doctors completed a continuous survey. Before and after the implementation of those measures, the average physical state scores and the psychological state scores of doctors improved (P=0.03), the self-score of stress feeling decreased (P<0.01), and the self-score of professional achievement increased (P<0.01). Conclusions To adapt to the characteristics of emergency treatment for batch COVID-19 patients, the internal and external linkage-time phased scheduling and information-based patient zoning and grading management could help the temporarily convened doctors deal with a large number of patients efficiently, reduce work stress and exposure risk in shelter hospital. © 2022 Chinese Medical Association. All rights reserved.

Design and Implementation of Pork Freshness Grading Based on Deep Learning

China is the world's largest pork production and consumption country, with the improvement of people's living standards and consumption upgrade, people's demand for fresh pork and other fresh products is stronger. With the outbreak of African Swine Fever and COVID-19 in China in the past two years, cold chain transportation of pork will replace live pigs as the main mode of pork supply chain. As one of the most important branches of machine learning, deep learning has developed rapidly in recent years and attracted extensive attention at home and abroad. In order to improve the real-time detection of pork freshness, this paper experimented with a variety of deep learning frameworks to achieve pork freshness classification. In this paper, pork freshness is divided into 5 levels according to TVB-N content, and the pictures taken are trained by different deep learning networks, including VGG, GoogLeNet and RestNet. After analyzing the training situation of each network, the advantages of different networks are absorbed and a new improved neural network is built to predict pork freshness. The final classification accuracy reached 97%, Indicating that this is a very efficient and accurate pork freshness classification method. © 2021 IEEE.

Expert consensus on emergency medicine procedure optimization guided by routine prevention and control strategy for COVID-19

The outbreak of coronavirus disease 2019 (COVID-19) was declared a global public health emergency on 31 January 2020. Emergency medicine procedures in Emergency Department should be optimized to cope with the current COVID-19 pandemic by providing subspecialty services, reducing the spread of nosocomial infections, and promoting its capabilities to handle emerging diseases. Thus, the Chinese Society of Emergency Medicine and Wuhan Society of Emergency Medicine drafted this consensus together to address concerns of medical staffs who work in Emergency Department. Based on in-depth review of COVID-19 diagnosis and treatment plans, literatures, as well as management approval, this consensus proposes recommendations for improving the rationalization and efficiency of emergency processes, reducing the risk of nosocomial infections, preventing hospital viral transmission, and ensuring patient safety. © 2021 Asian Pacific Journal of Tropical Medicine Produced by Wolters KluwerMedknow. All rights reserved.