Application of the Internet Platform in Monitoring Chinese Public Attention to the Outbreak of COVID-19

The internet usage increases fear of infection with Covid-19

During the Covid-19 pandemic, because of convenience and efficiency, the internet has emerged as an important channel for both acquiring information and engaging in social interaction.The internet plays a crucial role in keeping individuals informed and connected during the Covid-19 pandemic. However, the effects of the massive and untruthful information on the internet, as well as the excessive use of the internet during the Covid-19 pandemic, especially on the individuals fear and panic of infection with Covid-19, have not been adequately addressed. We utilize the data of Chinese General Social Survey (CGSS) in the year of 2021, as one of a national, comprehensive and continuous survey projects which is conducted a cross-sectional survey among provinces in mainland China each year, to test the relationship between the internet usage and the individuals fear of infection with Covid-19. By utilizing Ordered-Probit model, we find that the higher frequency of the internet usage significantly increases the individuals fear of infection with Covid-19. On average, when individuals use the internet from never use to very often, the probability of feeling not too fearful to be infected with Covid-19 decreases by 2%. Additionally, the findings reveal that the impact is particularly pronounced among females, middle-income individuals, older individuals and those with lower educational levels. The results also indicate that the effect is greater among the individuals with chronic diseases, those who spend less time studying, and feel they are unlikely to be infected with Covid-19. Furthermore, as the frequency of internet usage increases, we observe a corresponding rise in the likelihood of receiving a vaccine, along with a change in attitudes towards the measures implemented by the government.

Internet-based Surveillance Systems and Infectious Diseases Prediction: An Updated Review of the Last 10 Years and Lessons from the COVID-19 Pandemic

The last decade has seen major advances and growth in internet-based surveillance for infectious diseases through advanced computational capacity, growing adoption of smart devices, increased availability of Artificial Intelligence (AI), alongside environmental pressures including climate and land use change contributing to increased threat and spread of pandemics and emerging infectious diseases. With the increasing burden of infectious diseases and the COVID-19 pandemic, the need for developing novel technologies and integrating internet-based data approaches to improving infectious disease surveillance is greater than ever. In this systematic review, we searched the scientific literature for research on internet-based or digital surveillance for influenza, dengue fever and COVID-19 from 2013 to 2023. We have provided an overview of recent internet-based surveillance research for emerging infectious diseases (EID), describing changes in the digital landscape, with recommendations for future research directed at public health policymakers, healthcare providers, and government health departments to enhance traditional surveillance for detecting, monitoring, reporting, and responding to influenza, dengue, and COVID-19.

A potential tool for predicting epidemic trends and outbreaks of scrub typhus based on Internet search big data analysis in Yunnan Province, China

Introduction

Scrub typhus, caused by Orientia tsutsugamushi, is a neglected tropical disease. The southern part of China is considered an important epidemic and conserved area of scrub typhus. Although a surveillance system has been established, the surveillance of scrub typhus is typically delayed or incomplete and cannot predict trends in morbidity. Internet search data intuitively expose the public's attention to certain diseases when used in the public health area, thus reflecting the prevalence of the diseases.

Methods

In this study, based on the Internet search big data and historical scrub typhus incidence data in Yunnan Province of China, the autoregressive integrated moving average (ARIMA) model and ARIMA with external variables (ARIMAX) model were constructed and compared to predict the scrub typhus incidence.

Results

The results showed that the ARIMAX model produced a better outcome than the ARIMA model evaluated by various indexes and comparisons with the actual data.

Conclusions

The study demonstrates that Internet search big data can enhance the traditional surveillance system in monitoring and predicting the prevalence of scrub typhus and provides a potential tool for monitoring epidemic trends of scrub typhus and early warning of its outbreaks.

Characteristics of Collective Resilience and Its Influencing Factors from the Perspective of Psychological Emotion: A Case Study of COVID-19 in China

Collective resilience is the ability of human beings to adapt and collectively cope with crises in adversity. Emotional expression is the core element with which to characterize the psychological dimension of collective resilience. This research proposed a stage model of collective resilience based on the temporal evolution of the public opinions of COVID-19 in China's first anti-pandemic cycle; using data from hot searches and commentaries on Sina Weibo, the changes in the emotional patterns of social groups are revealed through analyses of the sentiments expressed in texts. A grounded theory approach is used to elucidate the factors influencing collective resilience. The research results show that collective resilience during the pandemic exhibited an evolutionary process that could be termed, "preparation-process-recovery". Analyses of expressed sentiments reveal an evolutionary pattern of "positive emotion prevailing-negative emotion appearing-positive emotion recovering Collective resilience from a psycho-emotional perspective is the result of "basic cognition-intermediary condition-consequence" positive feedback, in which the basic cognition is expressed as will embeddedness and the intermediary conditions include the subject behavior and any associated derived behavioral characteristics and spiritual connotation. These results are significant both theoretically and practically with regard to the reconstruction of collective resilience when s' force majeure' event occur.

Evaluation of the vulnerability to public health events in the Guangdong-Hong Kong-Macao Greater Bay Area

With the continuous improvement in the integration of urban agglomeration, a multi-functional, socialized, and complex dynamic system, effective prevention and control of emergent public health events have become increasingly important. Based on the Public-Health Vulnerability-Assessment-System of Urban Agglomeration (PVUA), the temporal and spatial differentiation characteristics of vulnerability in the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) for the period of 2015-2019 are explored, and the vulnerable cities to public health events are identified in this area. The results can be summarized as follows: (1) The overall vulnerability to public health events in GBA decreases in the investigated period. (2) In the temporal dimension, accompanied by social and economic development, the sensitivity to public health events increases in GBA, and the coping capacity change from stable fluctuation to rapid improvement. (3) From the spatial dimension, the sensitivity level in GBA is low in the west, relatively high in the middle, and high in the southeast; the coping capacity is high in the southeast and low in the northwest; the collaborative governance capacity presents a spatial pattern of being low in the south and high in the north. (4) In the period of study, the vulnerability to public health events in Guangzhou and Jiangmen is stable at the lowest level, while that in Zhaoqing, Foshan, and Hong Kong SAR (Special Administrative Region) gradually reduces; the vulnerability in Shenzhen, Zhuhai, and Dongguan is fluctuating, and that in Huizhou, Zhongshan, and Macao SAR is continually maintained at a higher and the highest level.

Spatiotemporal evolution of online attention to vaccines since 2011: An empirical study in China

Since the outbreak of Coronavirus Disease 2019 (COVID-19), the Chinese government has taken a number of measures to effectively control the pandemic. By the end of 2021, China achieved a full vaccination rate higher than 85%. The Chinese Plan provides an important model for the global fight against COVID-19. Internet search reflects the public's attention toward and potential demand for a particular thing. Research on the spatiotemporal characteristics of online attention to vaccines can determine the spatiotemporal distribution of vaccine demand in China and provides a basis for global public health policy making. This study analyzes the spatiotemporal characteristics of online attention to vaccines and their influencing factors in 31 provinces/municipalities in mainland China with Baidu Index as the data source by using geographic concentration index, coefficient of variation, GeoDetector, and other methods. The following findings are presented. First, online attention to vaccines showed an overall upward trend in China since 2011, especially after 2016. Significant seasonal differences and an unbalanced monthly distribution were observed. Second, there was an obvious geographical imbalance in online attention to vaccines among the provinces/municipalities, generally exhibiting a spatial pattern of “high in the east and low in the west.” Low aggregation and obvious spatial dispersion among the provinces/municipalities were also observed. The geographic distribution of hot and cold spots of online attention to vaccines has clear boundaries. The hot spots are mainly distributed in the central-eastern provinces and the cold spots are in the western provinces. Third, the spatiotemporal differences in online attention to vaccines are the combined result of socioeconomic level, socio-demographic characteristics, and disease control level.