Infectious or Recovered? Optimizing the Infectious Disease Detection Process for Epidemic Control and Prevention Based on Social Media

Shan, Shiguang; Yan, Qi; Wei, Yigang

doi:10.3390/ijerph17186853

Cited by 8 publications

(5 citation statements)

References 65 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…At present, China has entered a period of normalized prevention and control of infectious diseases, and the management of infectious diseases has also entered a stage of normalized prevention and control [ 6 ]. However, in the turbulent situation of global epidemic, domestic epidemic prevention and control still face the pressure of external prevention of importation and internal prevention of rebound, and it is imperative to strengthen the construction of fever clinics and comprehensive control of nosocomial infections.…”

Section: Introductionmentioning

confidence: 99%

Infection Control-Based Construction of a Fever Outpatient Routine Management Model

Wang

Zhang

et al. 2022

Emergency Medicine International

View full text Add to dashboard Cite

Purpose. Outbreaks caused by infectious diseases are now serious public health events. At present, most hospitals have a high number of fever clinic attendances. In order to improve the efficiency of fever clinic screening, timely detection and control of infection sources, early detection, early isolation, and early treatment, our hospital explored the construction and effect of our fever clinic management model during the response period by constructing a fever clinic regular management model based on the principles of infection control. Methods. 1300 cases (September 2021 to February 2022) with or without epidemiological history were divided into the control group (without epidemiological history) and the observation group (with epidemiological history) and patients were given differentiated management. A model of permanent management of a fever clinic during the epidemic was set up and evaluated by implementing the person responsible for epidemic positions, optimizing tertiary care, and strengthening nosocomial infection protection for health care workers. Results. The results showed that patients in the observation group had a lower age of onset, a longer consultation time, and a higher proportion of patients with fever, which was different from the control group ( P < 0.05 ). Compared with the control group, the proportion of routine blood tests, the proportion of four respiratory virus tests, and the per capita cost were higher in the observation group, and the differences were statistically significant ( P < 0.05 ). There were no missed diagnoses, underreporting, cross-infections, or nosocomial infections in either group, and there were no significant differences between the two groups in terms of patients’ evaluation of management quality and satisfaction with management ( P > 0.05 ). The skill level, management attitude, and standardized operation of outpatient clinic managers improved after the construction of a fever clinic standing management model based on infection control, and the recognition of patients was higher in the observation group ( P < 0.05 ). Conclusion. The construction of a fever outpatient routine management model based on the principle of infection control is conducive to the standardized implementation of the management and treatment of health care workers, early detection of the source of transmission to cut off the transmission route, avoiding cross-infection and nosocomial infection, and ensuring the safety of patients and health care workers.

show abstract

Section: Introductionmentioning

confidence: 99%

Infection Control-Based Construction of a Fever Outpatient Routine Management Model

Wang

Zhang

et al. 2022

Emergency Medicine International

View full text Add to dashboard Cite

show abstract

“…Also, the data set could be more extended, but extracting comments from Facebook is relatively more complex than other platforms, although Facebook is the most popular platform in Greece. Topics of sarcasm, irony, informal expressions, humor, and slang are challenging to detect by computer programs ( 49 , 76 , 77 ). In addition, a large data set could improve the accuracy of our results.…”

Section: Resultsmentioning

confidence: 99%

Sentiment analysis of epidemiological surveillance reports on COVID-19 in Greece using machine learning models

Stefanis,

Giorgi,

Kalentzis

et al. 2023

Front. Public Health

View full text Add to dashboard Cite

The present research deals with sentiment analysis performed with Microsoft Azure Machine Learning Studio to classify Facebook posts on the Greek National Public Health Organization (EODY) from November 2021 to January 2022 during the pandemic. Positive, negative and neutral sentiments were included after processing 300 reviews. This approach involved analyzing the words appearing in the comments and exploring the sentiments related to daily surveillance reports of COVID-19 published on the EODY Facebook page. Moreover, machine learning algorithms were implemented to predict the classification of sentiments. This research assesses the efficiency of a few popular machine learning models, which is one of the initial efforts in Greece in this domain. People have negative sentiments toward COVID surveillance reports. Words with the highest frequency of occurrence include government, vaccinated people, unvaccinated, telephone communication, health measures, virus, COVID-19 rapid/molecular tests, and of course, COVID-19. The experimental results disclose additionally that two classifiers, namely two class Neural Network and two class Bayes Point Machine, achieved high sentiment analysis accuracy and F1 score, particularly 87% and over 35%. A significant limitation of this study may be the need for more comparison with other research attempts that identified the sentiments of the EODY surveillance reports of COVID in Greece. Machine learning models can provide critical information combating public health hazards and enrich communication strategies and proactive actions in public health issues and opinion management during the COVID-19 pandemic.

show abstract

“…In a social network, users will share their health-related information, which can be used to infer health status and incidence rates for specific conditions or symptoms. 45,46 First, through the classifier in the previous step, we obtain the Weibo messages that describe authors with influenza. Then, we mine all Weibo messages of these authors containing the keywords "influenza" (gan mao).…”

Section: Modeling Correlation: Social Network Characteristics and Sus...mentioning

confidence: 99%

Monitoring the Relationship between Social Network Status and Influenza Based on Social Media Data

Yan,

Shan,

Zhang

et al. 2023

Disaster med. public health prep.

Self Cite

View full text Add to dashboard Cite

Background: This article aims to analyze the relationship between user characteristics on social networks and influenza. Methods: Three specific research questions are investigated: (1) we classify Weibo updates to recognize influenza-related information based on machine learning algorithms and propose a quantitative model for influenza susceptibility in social networks; (2) we adopt in-degree indicator from complex networks theory as social media status to verify its coefficient correlation with influenza susceptibility; (3) we also apply the LDA topic model to explore users’ physical condition from Weibo to further calculate its coefficient correlation with influenza susceptibility. From the perspective of social networking status, we analyze and extract influenza-related information from social media, with many advantages including efficiency, low cost, and real time. Results: We find a moderate negative correlation between the susceptibility of users to influenza and social network status, while there is a significant positive correlation between physical condition and susceptibility to influenza. Conclusions: Our findings reveal the laws behind the phenomenon of online disease transmission, and providing important evidence for analyzing, predicting, and preventing disease transmission. Also, this study provides theoretical and methodological underpinnings for further exploration and measurement of more factors associated with infection control and public health from social networks.

show abstract

Infectious or Recovered? Optimizing the Infectious Disease Detection Process for Epidemic Control and Prevention Based on Social Media

Cited by 8 publications

References 65 publications

Infection Control-Based Construction of a Fever Outpatient Routine Management Model

Infection Control-Based Construction of a Fever Outpatient Routine Management Model

Sentiment analysis of epidemiological surveillance reports on COVID-19 in Greece using machine learning models

Monitoring the Relationship between Social Network Status and Influenza Based on Social Media Data

Contact Info

Product

Resources

About