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

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

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.

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

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.

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

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.

Biases in using social media data for public health surveillance: A scoping review

OBJECTIVES

A landscape scan of the methods that are used to either assess or mitigate biases when using social media data for public health surveillance, through a scoping review.

MATERIALS AND METHODS

Following best practices, we searched two literature databases (i.e., PubMed and Web of Science) and covered literature published up to July 2021. Through two rounds of screening (i.e., title/abstract screening, and then full-text screening), we extracted study objectives, analysis methods, and the methods used to assess or address the different biases from the eligible articles.

RESULTS

We identified a total of 2,856 articles from the two databases. After the screening processes, we extracted and synthesized 20 studies that either assessed or mitigated biases when leveraging social media data for public health surveillance. Researchers have tried to assess or address several different types of biases such as demographic bias, keyword bias, and platform bias. In particular, we found 11 studies that tried to measure the reliability of the research findings from social media data by comparing them with other data sources.

DISCUSSION AND CONCLUSION

We synthesized the types of biases and the methods used to assess or address the biases in studies that use social media data for public health surveillance. We found very few studies, despite the large number of publications using social media data, considered the various bias issues that are present from data collection to analysis methods. Overlooking bias can distort the study results and lead to unintended consequences, especially in the field of public health surveillance. These research gaps warrant further investigations more systematically. Strategies from other fields for addressing biases can be introduced for future public health surveillance systems that use social media data.

Mining long-COVID symptoms from Reddit: characterizing post-COVID syndrome from patient reports

Our objective was to mine Reddit to discover long-COVID symptoms self-reported by users, compare symptom distributions across studies, and create a symptom lexicon. We retrieved posts from the /r/covidlonghaulers subreddit and extracted symptoms via approximate matching using an expanded meta-lexicon. We mapped the extracted symptoms to standard concept IDs, compared their distributions with those reported in recent literature and analyzed their distributions over time. From 42 995 posts by 4249 users, we identified 1744 users who expressed at least 1 symptom. The most frequently reported long-COVID symptoms were mental health-related symptoms (55.2%), fatigue (51.2%), general ache/pain (48.4%), brain fog/confusion (32.8%), and dyspnea (28.9%) among users reporting at least 1 symptom. Comparison with recent literature revealed a large variance in reported symptoms across studies. Temporal analysis showed several persistent symptoms up to 15 months after infection. The spectrum of symptoms identified from Reddit may provide early insights about long-COVID.

Can social media data be used to evaluate the risk of human interactions during the COVID-19 pandemic?

The U.S. has taken multiple measures to contain the spread of COVID-19, including the implementation of lockdown orders and social distancing practices. Evaluating social distancing is critical since it reflects the risk of close human interactions. While questionnaire surveys or mobility data-based systems have provided valuable insights, social media data can contribute as an additional instrument to help monitor the risk of human interactions during the pandemic. For this reason, this study introduced a social media-based approach that quantifies the pro/anti-lockdown ratio as an indicator of the risk of human interactions. With the aid of natural language processing and machine learning techniques, this study classified the lockdown-related tweets and quantified the pro/anti-lockdown ratio for each state over time. The anti-lockdown ratio showed a moderate and negative correlation with the state-level social distancing index on a weekly basis, suggesting that people are more likely to travel out of the state where the higher anti-lockdown level is observed. The study further showed that the perception expressed on social media could reflect people's behaviors. The findings of the study are of significance for government agencies to assess the risk of close human interactions and to evaluate their policy effectiveness in the context of social distancing and lockdown.