Using mobile phone-based text message to recruit representative samples: Assessment of a cross-sectional survey about the COVID-19 vaccine hesitation

Real-time infectious disease endurance indicator system for scientific decisions using machine learning and rapid data processing

The SARS-CoV-2 virus, which induces an acute respiratory illness commonly referred to as COVID-19, had been designated as a pandemic by the World Health Organization due to its highly infectious nature and the associated public health risks it poses globally. Identifying the critical factors for predicting mortality is essential for improving patient therapy. Unlike other data types, such as computed tomography scans, x-radiation, and ultrasounds, basic blood test results are widely accessible and can aid in predicting mortality. The present research advocates the utilization of machine learning (ML) methodologies for predicting the likelihood of infectious disease like COVID-19 mortality by leveraging blood test data. Age, LDH (lactate dehydrogenase), lymphocytes, neutrophils, and hs-CRP (high-sensitivity C-reactive protein) are five extremely potent characteristics that, when combined, can accurately predict mortality in 96% of cases. By combining XGBoost feature importance with neural network classification, the optimal approach can predict mortality with exceptional accuracy from infectious disease, along with achieving a precision rate of 90% up to 16 days before the event. The studies suggested model's excellent predictive performance and practicality were confirmed through testing with three instances that depended on the days to the outcome. By carefully analyzing and identifying patterns in these significant biomarkers insightful information has been obtained for simple application. This study offers potential remedies that could accelerate decision-making for targeted medical treatments within healthcare systems, utilizing a timely, accurate, and reliable method.

Understanding COVID-19 vaccine hesitancy of different regions in the post-epidemic era: A causality deep learning approach

Objective

This paper aims to understand vaccine hesitancy in the post-epidemic era by analyzing texts related to vaccine reviews and public attitudes toward three prominent vaccine brands: Sinovac, AstraZeneca, and Pfizer, and exploring the relationship of vaccine hesitancy with the prevalence of epidemics in different regions.

Methods

We collected 165629 Twitter user comments associated with the vaccine brands. The comments were labeled based on willingness and attitude toward vaccination. We utilize a causality deep learning model, the Bert multi-channel convolutional neural network (BertMCNN), to predict users' willingness and attitude mutually.

Results

When applied to the provided dataset, the proposed BertMCNN model demonstrated superior performance to traditional machine learning algorithms and other deep learning models. It is worth noting that after March 2022, the public was more hesitant about the Sinovac vaccines.

Conclusions

This study reveals a connection between vaccine hesitancy and the prevalence of the epidemic in different regions. The analytical results obtained from this method can assist governmental health departments in making informed decisions regarding vaccination strategies.

Factors Affecting Response Rates of the Web Survey with Teachers

Although web survey has been a popular method of data collection in the academic community, it presents meagre response rates, which primarily affect the validity of the results as well as the reliability of the outcomes. Surveys worldwide that study the response rate only of teachers have not been found in the relevant literature. In this survey, with a sample of 263 Greek teachers, we investigate possible factors that explain teachers’ intention to participate in web surveys that are conducted by online questionnaires indicating, therefore, the factors that probably influence the response rate of web surveys. Our findings support those factors such as (a) authority, (b) incentives, (c) survey structure/form, (d) ethical issues, (e) reminders and pre-notifications, and (f) survey time received, which seem to explain the teachers’ intention to participate in web surveys with questionnaires. Based on the findings, methodology implications and limitations for researchers are discussed.