Research on Key technologies of network public opinion warning based on improved Stacking algorithm

Online public opinion warning for emergencies can help people understand the real situation, avoid panic, timely remind people not to go to high-risk areas, and help the government to carry out epidemic work.In this paper, key technologies of network public opinion warning were studied based on improved Stacking algorithm. COVID-19, herpangina, hand, foot and mouth, varicella and several emergency outbreaks were selected as public opinion research objects, and rough set was used to screen indicators and determine the final warning indicators.Finally, the warning model was established by the 50% fold Stacking algorithm, and the training accuracy and prediction accuracy experiments were carried out.According to the empirical study, the prediction accuracy of 50% Stacking is good, and the early warning model is practical and robust.This study has strong practicability in the early warning of the online public opinion of the sudden epidemic. © 2023 SPIE.

A mental health survey among young front-line clinicians in high-risk areas during the COVID-19 sporadic epidemic in China.

Background: The sporadic coronavirus disease (COVID-19) epidemic has placed enormous psychological stress on people, especially clinicians. The objective of this study was to examine depression, anxiety, quality of life (QOL), and related social psychological factors among young front-line clinicians in high-risk areas during the COVID-19 sporadic epidemic in China and to provide a reference for formulating reasonable countermeasures. Methods: In this cross-sectional study, demographic information, COVID-19-related questions, anxiety (Generalized Anxiety Disorder-7, GAD-7), depression (Patient Health Questionnaire-9, PHQ-9), insomnia (Insomnia Severity Index, ISI), stress (Perceived Stress Scale-10, PSS-10), and QOL (World Health Organization Quality of Life-brief version, WHOQOL-BREF) were collected. Binary logistic regression analysis was used to test the relationships between anxiety and/or depression and other related problems. Multiple linear regression analysis was used to test the relationships among factors influencing QOL. Results: A total of 146 young front-line clinicians were included. The prevalence rates of depression, anxiety, and anxiety-depression comorbidity were 37.7% (95% CI = 29.7-45.6%), 26.0% (95% CI = 18.8-33.2%), and 24.0% (95% CI = 17.0-31.0%), respectively. Severe stress (OR = 1.258, 95% CI = 1.098-1.442, P < 0.01) and insomnia (OR = 1.282, 95% CI = 1.135-1.447, P < 0.01) were positively correlated with depression. Severe stress (OR = 1.487, 95% CI = 1.213-1.823, P < 0.01) and insomnia (OR = 1.131, 95% CI = 1.003-1.274, P < 0.05) were positively correlated with anxiety. Severe stress (OR = 1.532, 95% CI = 1.228-1.912, P < 0.01) was positively correlated with anxiety-depression comorbidity. However, insomnia (OR = 1.081, 95% CI = 0.963-1.214, P > 0.05) was not correlated with anxiety-depression comorbidity. The belief that the vaccine will stop the COVID-19 pandemic (OR = 0.099, 95% CI = 0.014-0.715, P < 0.05) was negatively correlated with anxiety and anxiety-depression comorbidity (OR = 0.101, 95% CI = 0.014-0.744, P < 0.05). Severe stress (B = -0.068, 95% CI = -0.129 to -0.007, P < 0.05) and insomnia (B = -0.127, 95% CI = -0.188 to -0.067, P < 0.01) were negatively correlated with QOL. The belief that the vaccine could provide protection (B = 1.442, 95% CI = 0.253-2.631, P < 0.05) was positively correlated with QOL. Conclusions: The prevalence of depression, anxiety, and even anxiety-depression comorbidity was high among young front-line clinicians in high-risk areas during the COVID-19 sporadic epidemic in China. Various biological and psychological factors as well as COVID-19-related factors were associated with mental health issues and QOL. Psychological intervention should evaluate these related factors and formulate measures for these high-risk groups.

COVID-19 Classification using CT Scan Images with Resize-MobileNet

Coronavirus disease 2019 broke out in early 2020 and quickly spread to over 200 countries, leading to a severe health crisis for people all over the world. In high-risk areas of the epidemic, the shortage of testing reagents and medical facilities have become essential factors restricting the treatment of COVID-19 patients. Computed tomography (CT) has helped doctors make medical diagnoses in many areas as a vital technology in medical field. At present, due to personal privacy issues, it isn't easy to compare different networks because they are all conducted on different data sets, using other metrics, and can not make good use of high-resolution CT images. Based on iCTCF's public data set, 4000 photos from 61 patients are used to propose a network of high-resolution inputs for diagnosing disease using lung CT images of COVID-19 patients. Our work makes better results than traditional image classification methods in limited data sets, contributing to the advancement of deep neural networks in the field of COVID-19CT image recognition. © 2021 IEEE.

Classification Schemes of COVID-19 High Risk Areas and Resulting Policies: A Rapid Review.

The COVID-19 pandemic has posed a significant global health threat since January 2020. Policies to reduce human mobility have been recognized to effectively control the spread of COVID-19; although the relationship between mobility, policy implementation, and virus spread remains contentious, with no clear pattern for how countries classify each other, and determine the destinations to- and from which to restrict travel. In this rapid review, we identified country classification schemes for high-risk COVID-19 areas and associated policies which mirrored the dynamic situation in 2020, with the aim of identifying any patterns that could indicate the effectiveness of such policies. We searched academic databases, including PubMed, Scopus, medRxiv, Google Scholar, and EMBASE. We also consulted web pages of the relevant government institutions in all countries. This rapid review's searches were conducted between October 2020 and December 2021. Web scraping of policy documents yielded additional 43 country reports on high-risk area classification schemes. In 43 countries from which relevant reports were identified, six issued domestic classification schemes. International classification schemes were issued by the remaining 38 countries, and these mainly used case incidence per 100,000 inhabitants as key indicator. The case incidence cut-off also varied across the countries, ranging from 20 cases per 100,000 inhabitants in the past 7 days to more than 100 cases per 100,000 inhabitants in the past 28 days. The criteria used for defining high-risk areas varied across countries, including case count, positivity rate, composite risk scores, community transmission and satisfactory laboratory testing. Countries either used case incidence in the past 7, 14 or 28 days. The resulting policies included restrictions on internal movement and international travel. The quarantine policies can be summarized into three categories: (1) 14 days self-isolation, (2) 10 days self-isolation and (3) 14 days compulsory isolation.

Forecasting High-risk Areas of COVID-19 Infection Through Socioeconomic and Static Spatial Analysis

Existing COVID-19 prediction models focus on studying the dynamic nature of the virus spread by using pandemic-related temporal data. In this paper, we present a work that exclusively uses comprehensive socioeconomic factors to predict the high risk areas of COVID-19 infection based on fine-grained static spatial analysis. Moreover, the most and least influential socioeconomic factors on COVID-19 spread are identified. This paper uses a uniquely built dataset by combining local states' cumulative COVID-19 statistics and their associated socioeconomic features on the zip code level. Further, the work solves the lack of data by augmentation. To evaluate the work, four case studies are conducted on Florida, Illinois, Minnesota, and Virginia. Experimental results show that the study provides accurate predictions with respect to ground truth data. By identifying high risk areas and socioeconomic factors, policymakers can use this study to take necessary measures to help disadvantaged communities. © 2021 IEEE.