Influencing factors for public risk perception of COVID-19 --perspective of the pandemic whole life cycle

Mechanism of risk perception diffusion in public health emergencies: Based on the dual perspectives of cross-evolution and emotional difference

The high-level risk perception diffusion caused by public health emergencies seriously threatens public mental health and social stability. Much scholarly attention focused on the traditional epidemic models or simply combined content and social attributes, overlooking the differences in public individual characteristics. This paper proposes an S1S2EIposIneuInegR model of risk perception diffusion by innovatively subdividing susceptible people and infectious people. Then, taking the Xi'an epidemic as an example (N = 105,417), this paper employs the sentiment analysis model of Word2Vec and Bi-LSTM to calculate the emotional value of microblog text to quantify public risk perception. Finally, numerical experiments are conducted to explore the effects of cross-evolution and emotional difference on risk perception diffusion under different scenarios. Findings reveal that a larger initial density of infectious people accelerates diffusion, with negative emotions playing a dominant role. In addition, the higher the risk perception level and the lower the heterogeneity, the greater the maximum impact and the final scale of diffusion. When the public health emergency deteriorates, the cross-evolution tends to shift to a high-risk perception. Otherwise, it tends to tilt to a low-risk perception. These findings provide critical insights for developing precise risk perception guidance strategies and enhancing public health governance capabilities.

Main factors influencing the perceived health risk of endocrine-disrupting chemicals: A systematic literature review

Endocrine-disrupting chemicals (EDCs) are linked to rising health issues such as infertility, childhood obesity, and asthma. While some research exists on health risk perceptions of EDCs, a comprehensive understanding across different populations and contexts is needed. We performed a systematic literature review, examining 45 articles published between 1985 and 2023, focusing on both the risk perception of EDCs as a whole as well as individual EDCs found in the environment (e.g., pesticides, bisphenol A, and phthalates). We identified four major categories of factors influencing EDC risk perception: sociodemographic factors (with age, gender, race, and education as significant determinants), family-related factors (highlighting increased concerns in households with children), cognitive factors (indicating that increased EDC knowledge generally led to increased risk perception), and psychosocial factors (with trust in institutions, worldviews, and health-related concerns as primary determinants). This review highlights the complex nature of EDC risk perception, shaped by sociodemographic, family, cognitive, and psychosocial factors, essential for policymakers in crafting educational and communication strategies. Future research should expand to cover more EDCs, use representative samples, and explore the influence of psychosocial factors on risk perception more deeply.

Exploring People's Perception of COVID-19 Risk: A Case Study of Greater Jakarta, Indonesia

This study aims to understand people's perceptions of COVID-19 risk in Greater Jakarta, Indonesia. In response to the COVID-19 pandemic, the Indonesian government enacted a health protocol campaign and highlighted the community as an important unit of protocol compliance. We hypothesized that people's perception of the likelihood of being infected with COVID-19 is associated with health protocol compliance at the community level and their perception of community resilience. As the number of infected persons drastically increased, the "family cluster" also became a significant issue in the pandemic response, especially in Indonesia. In this study, we explored both community and family aspects that influence people's perceptions. We conducted an online survey in March 2021 with 370 respondents residing in the Greater Jakarta area. The respondents were classified into four age groups (20s, 30s, 40s, and 50-and-over), with gender-balanced samples allocated to each group. We used a questionnaire to measure the perception of COVID-19 risk along with the Conjoint Community Resiliency Assessment Measure (CCRAM). Multiple regression analysis revealed that family factors have a much larger influence on the individual perception of the likelihood of contracting COVID-19 than community factors. The results suggest that the link between family-level efforts against COVID-19 and individual-level perceptions cannot be separated in response to the pandemic.

Deep learning modeling of public's sentiments towards temporal evolution of COVID-19 transmission

Public sentiments towards global pandemics are important for public health assessment and disease control. This study develops a modularized deep learning framework to quantify public sentiments towards COVID-19, followed by leveraging the predicted sentiments to model and forecast the daily growth rate of confirmed COVID-19 cases globally, via a proposed G parameter. In the proposed framework, public sentiments are first modeled via a valence dimensional indicator, instead of discrete schemas, and are classified into 4 primary emotional categories: (a) neutral; (b) negative; (c) positive; (d) ambivalent, by using multiple word embedding models and classifiers for text sentiments analyses and classification. The trained model is subsequently applied to analyze large volumes (millions in quantity) of daily Tweets pertaining to COVID-19, ranging from 22 Jan 2020 to 10 May 2020. The results demonstrate that the global community gradually evokes both positive and negative sentiments towards COVID-19 over time compared to the dominant neural emotion at its inception. The predicted time-series sentiments are then leveraged to train a deep neural network (DNN) to model and forecast the G parameter by achieving the lowest possible mean absolute percentage error (MAPE) score of around 17.0% during the model's testing step with the optimal model configuration.

Public Risk Perception Attribution Model and Governance Path in COVID-19: A Perspective Based on Risk Information

Background

Risk perception is a key factor influencing the public’s behavioral response to major public health events. The research on public risk perception promotes the emergency management system to adapt to the needs of modern development. This article is based on a risk information perspective, using the COVID-19 event as an example. From the micro and macro perspectives, the influencing factors of public risk perception in major public health events in China are extracted, and the attribution model and index system of public risk perception are established.

Methods

In this paper, the five-level Likert scale is used to collect and measure the risk perception variable questionnaire through the combination of online and offline methods (a total of 550 questionnaires, the overall Alpha coefficient of the questionnaire is 0.955, and the KMO test coefficient t=0.941), and through independent samples t-test, correlation analysis, multiple regression analysis and other methods to draw relevant conclusions.

Results

The results showed that gender and age were significantly associated with risk perception (p<0.005), and education level was significantly negatively associated with risk perception (p <0 0.005). Risk information attention and risk perception were significantly positively correlated (p<0.005), media credibility was significantly positively correlated with risk perception (p<0.005), while risk information identification and media exposure had no significant interaction with risk perception (p=0.125, p=0.352).

Conclusion

Factors such as gender, age, education level, place of residence, media exposure, media credibility, risk information attention, and recognition lead to different levels of risk perception. This conclusion helps to provide a basis for relevant departments to conduct public risk management of major public health events based on differences in risk perceptions.

Spatiotemporal disparities in regional public risk perception of COVID-19 using Bayesian Spatiotemporally Varying Coefficients (STVC) series models across Chinese cities

Regional public attention has been critical during the COVID-19 pandemic, impacting the effectiveness of sub-national non-pharmaceutical interventions. While studies have focused on public attention at the national level, sub-national public attention has not been well investigated. Understanding sub-national public attention can aid local governments in designing regional scientific guidelines, especially in large countries with substantial spatiotemporal disparities in the spread of infections. Here, we evaluated the online public attention to the COVID-19 pandemic using internet search data and developed a regional public risk perception index (PRPI) that depicts heterogeneous associations between local pandemic risk and public attention across 366 Chinese cities. We used the Bayesian Spatiotemporally Varying Coefficients (STVC) model, a full-map local regression for estimating spatiotemporal heterogeneous relationships of variables, and improved it to the Bayesian Spatiotemporally Interacting Varying Coefficients (STIVC) model to incorporate space-time interaction non-stationarity at spatial or temporal stratified scales. COVID-19 daily cases (median contribution 82.6%) was the most critical factor affecting public attention, followed by urban socioeconomic conditions (16.7%) and daily population mobility (0.7%). After adjusting national and provincial impacts, city-level influence factors accounted for 89.4% and 58.6% in spatiotemporal variations of public attention. Spatiotemporal disparities were substantial among cities and provinces, suggesting that observing national-level public dynamics alone was insufficient. Multi-period PRPI maps revealed clusters and outlier cities with potential public panic and low health literacy. Bayesian STVC series models are systematically proposed and provide a multi-level spatiotemporal heterogeneous analytical framework for understanding collective human responses to major public health emergencies and disasters.