ABSTRACT
Objective To analyze the epidemiological characteristics of community transmission of the coronavirus disease 2019 (COVID-19) caused by four imported cases in Hebei Province, and to provide a scientific basis for the prevention and control of the disease. Methods Descriptive epidemiological methods were used to analyze the epidemiological characteristics of four community-transmitted COVID-19 outbreaks reported in the China Disease Control and Prevention Information System from January 1, 2020 to December 31, 2021 in Hebei Province. Results From January 1, 2020 to December 31, 2021, four community-transmitted COVID-19 outbreaks caused by imported COVID-19 occurred in Hebei Province, respectively related of Hubei (Wuhan) Province, Beijing Xinfadi market, Overseas cases and Ejina banner of Inner Mongolia Autonomous Region. Total of 1 656 cases (1 420 confirmed cases and 236 asymptomatic cases) were reported, including 375 cases in phase A (From January 22 to April 16, 2020), and phase B (from June 14 to June 24, 2020) 27 cases were reported, with 1 116 cases reported in the third phase (Phase C, January 2 to February 14, 2021), and 138 cases reported in the fourth phase (Phase D, October 23 to November 14, 2021). The 1 656 cases were distributed in 104 counties of 11 districts (100.00%), accounting for 60.46% of the total number of counties in the province. There were 743 male cases and 913 female cases, with a male to female ratio of 0.81:1. The minimum age was 13 days, the maximum age was 94 years old, and the average age (median) was 40.3 years old. The incidence was 64.01% between 30 and 70 years old. Farmers and students accounted for 54.41% and 14.73% of the total cases respectively. Of the 1 420 confirmed cases, 312 were mild cases, accounting for 21.97%;Common type 1 095 cases (77.11%);There was 1 severe case and 12 critical cases, accounting for 0.07% and 0.85%, respectively. 7 patients died from 61.0 to 85.7 years old. The mean (median) time from onset to diagnosis was 1.9 days (0-31 days), and the mean (median) time of hospital stay was 15 days (1.5-56 days). Conclusions Four times in Hebei province COVID-19 outbreak in scale, duration, population, epidemic and type of input source, there are some certain difference, but there are some common characteristics, such as the outbreak occurs mainly during the legal holidays or after starting and spreading epidemic area is mainly in rural areas, aggregation epidemic is the main mode of transmission, etc. To this end, special efforts should be made to strengthen the management of people moving around during holidays, and strengthen the implementation of epidemic prevention and control measures in places with high concentration of people. To prevent the spread of the epidemic, we will step up surveillance in rural areas, farmers' markets, medical workers and other key areas and groups, and ensure early detection and timely response.Copyright © 2022 China Tropical Medicine. All rights reserved.
ABSTRACT
In this paper, we proposed COVID-19 lung CT (computed tomography) images recognition with superscalar winograd circuit based on VGG19. We adopt the VGG-19 machine learning architecture to recognize lung CT images and speed up neural network operations through Superscalar Winograd Circuit. After a series of experiments, our proposed method has a high pneumonia recognition rate and high computational efficiency. © 2022 IEEE.
ABSTRACT
Objective To analyze the epidemiological characteristics of community transmission of the coronavirus disease 2019 (COVID-19) caused by four imported cases in Hebei Province, and to provide a scientific basis for the prevention and control of the disease. Methods Descriptive epidemiological methods were used to analyze the epidemiological characteristics of four community-transmitted COVID-19 outbreaks reported in the China Disease Control and Prevention Information System from January 1, 2020 to December 31, 2021 in Hebei Province. Results From January 1, 2020 to December 31, 2021, four community-transmitted COVID-19 outbreaks caused by imported COVID-19 occurred in Hebei Province, respectively related of Hubei (Wuhan) Province, Beijing Xinfadi market, Overseas cases and Ejina banner of Inner Mongolia Autonomous Region. Total of 1 656 cases (1 420 confirmed cases and 236 asymptomatic cases) were reported, including 375 cases in phase A (From January 22 to April 16, 2020), and phase B (from June 14 to June 24, 2020) 27 cases were reported, with 1 116 cases reported in the third phase (Phase C, January 2 to February 14, 2021), and 138 cases reported in the fourth phase (Phase D, October 23 to November 14, 2021). The 1 656 cases were distributed in 104 counties of 11 districts (100.00%), accounting for 60.46% of the total number of counties in the province. There were 743 male cases and 913 female cases, with a male to female ratio of 0.81∶1. The minimum age was 13 days, the maximum age was 94 years old, and the average age (median) was 40.3 years old. The incidence was 64.01% between 30 and 70 years old. Farmers and students accounted for 54.41% and 14.73% of the total cases respectively. Of the 1 420 confirmed cases, 312 were mild cases, accounting for 21.97%;Common type 1 095 cases (77.11%);There was 1 severe case and 12 critical cases, accounting for 0.07% and 0.85%, respectively. 7 patients died from 61.0 to 85.7 years old. The mean (median) time from onset to diagnosis was 1.9 days (0-31 days), and the mean (median) time of hospital stay was 15 days (1.5-56 days). Conclusions Four times in Hebei province COVID-19 outbreak in scale, duration, population, epidemic and type of input source, there are some certain difference, but there are some common characteristics, such as the outbreak occurs mainly during the legal holidays or after starting and spreading epidemic area is mainly in rural areas, aggregation epidemic is the main mode of transmission, etc. To this end, special efforts should be made to strengthen the management of people moving around during holidays, and strengthen the implementation of epidemic prevention and control measures in places with high concentration of people. To prevent the spread of the epidemic, we will step up surveillance in rural areas, farmers′ markets, medical workers and other key areas and groups, and ensure early detection and timely response. © 2022 China Tropical Medicine. All rights reserved.
ABSTRACT
As the safety problems and economic losses caused by traffic accidents are becoming more and more serious, intelligent transportation system (ITS) came into being. After the outbreak of COVID-19, how to achieve effective traffic scheduling and macro command under less contact has attracted more attention. Therefore, the location estimation of traffic objectives is a key issue. In the developed framework, for the target parameter estimation in traffic, frequency diversity array multiple-input multiple-output (FDA-MIMO) radar is introduced into ITS, and tensor decomposition is used to process transportation big data (TBD) to improve the real-time performance of target location estimation. Unfortunately, spatial colored noise and array gain-phase error will affect the performance of FDA-MIMO radar in ITS. An algorithm that can solve the angle-range estimation problem of FDA-MIMO radar in the co-existence of array gain-phase error and spatial colored noise is proposed. Firstly, the four-dimensional tensor is constructed by using the temporal un-correlation of colored noise. Therefore, the influence of colored noise in ITS is removed. Secondly, the direction matrix containing target information is obtained by parallel factor (PARAFAC) decomposition. For the array gain-phase error, the optimization problem is constructed, and the Lagrange multiplier is employed to calculate the optimal solution. The effect of gain-phase error is eliminated by utilizing the optimal solution and the direction matrices. Finally, the location information of motor vehicle is achieved by calculating the solution of least square (LS) fitting. The developed scheme can achieve the location information of motor vehicles in the co-existence of array gain-phase error and spatial colored noise. Comprehensive numerical experiments illustrate that the developed scheme in ITS can efficiently obtain the location information of motor vehicles.
ABSTRACT
The coronavirus disease 19 (COVID-19) pandemic that has been raging in 2020 does affect not only the physical state but also the mental health of the general population, particularly, that of the healthcare workers. Given the unprecedented large-scale impacts of the COVID-19 pandemic, digital technology has gained momentum as invaluable social interaction and health tracking tools in this time of great turmoil, in part due to the imposed state-wide mobilization limitations to mitigate the risk of infection that might arise from in-person socialization or hospitalization. Over the last five years, there has been a notable increase in the demand and usage of mobile and wearable devices as well as their adoption in studies of mental fitness. The purposes of this scoping review are to summarize evidence on the sweeping impact of COVID-19 on mental health as well as to evaluate the merits of the devices for remote psychological support. We conclude that the COVID-19 pandemic has inflicted a significant toll on the mental health of the population, leading to an upsurge in reports of pathological stress, depression, anxiety, and insomnia. It is also clear that mobile and wearable devices (e.g., smartwatches and fitness trackers) are well placed for identifying and targeting individuals with these psychological burdens in need of intervention. However, we found that most of the previous studies used research-grade wearable devices that are difficult to afford for the normal consumer due to their high cost. Thus, the possibility of replacing the research-grade wearable devices with the current smartwatch is also discussed.