Using the internet search data to investigate symptom characteristics of COVID-19: A big data study

Revealing the spatiotemporal characteristics of the general public's panic levels during the pandemic crisis in China

The existing crisis management research mostly reveals the patterns of the public's panic levels from the perspectives of public management, sociology, and psychology, only a few studies have revealed the spatiotemporal characteristics. Therefore, this study investigates the spatial distribution and temporal patterns and influencing factors on the general public's panic levels using the Baidu Index data from a geographic perspective. The results show that: (1) The public's panic levels were significantly correlated with the spatial distance between the epicenter and the region of investigation, and with the number of confirmed cases in different regions when the pandemic began to spread. (2) Based on the spatial distance between the epicenter and the region, the public's panic levels in different regions could be divided into three segments: core segment (0-500 km), buffer segment (500-1300 km), and peripheral segment (>1300 km). The panic levels of different people in the three segments were consistent with the Psychological Typhoon Eye Effect and the Ripple Effect can be detected in the buffer segment. (3) The public's panic levels were strongly correlated with whether the spread of the infectious disease crisis occurred and how long it lasted. It is suggested that crisis information management in the future needs to pay more attention to the spatial division of control measures. The type of crisis information released to the general public should depend on the spatial relationship associated with the place where the crisis breaks out.

Construction and validation of a COVID-19 pandemic trend forecast model based on Google Trends data for smell and taste loss

Aim

To explore the role of smell and taste changes in preventing and controlling the COVID-19 pandemic, we aimed to build a forecast model for trends in COVID-19 prediction based on Google Trends data for smell and taste loss.

Methods

Data on confirmed COVID-19 cases from 6 January 2020 to 26 December 2021 were collected from the World Health Organization (WHO) website. The keywords "loss of smell" and "loss of taste" were used to search the Google Trends platform. We constructed a transfer function model for multivariate time-series analysis and to forecast confirmed cases.

Results

From 6 January 2020 to 28 November 2021, a total of 99 weeks of data were analyzed. When the delay period was set from 1 to 3 weeks, the input sequence (Google Trends of loss of smell and taste data) and response sequence (number of new confirmed COVID-19 cases per week) were significantly correlated (P < 0.01). The transfer function model showed that worldwide and in India, the absolute error of the model in predicting the number of newly diagnosed COVID-19 cases in the following 3 weeks ranged from 0.08 to 3.10 (maximum value 100; the same below). In the United States, the absolute error of forecasts for the following 3 weeks ranged from 9.19 to 16.99, and the forecast effect was relatively accurate. For global data, the results showed that when the last point of the response sequence was at the midpoint of the uptrend or downtrend (25 July 2021; 21 November 2021; 23 May 2021; and 12 September 2021), the absolute error of the model forecast value for the following 4 weeks ranged from 0.15 to 5.77. When the last point of the response sequence was at the extreme point (2 May 2021; 29 August 2021; 20 June 2021; and 17 October 2021), the model could accurately forecast the trend in the number of confirmed cases after the extreme points. Our developed model could successfully predict the development trends of COVID-19.

Conclusion

Google Trends for loss of smell and taste could be used to accurately forecast the development trend of COVID-19 cases 1-3 weeks in advance.

Using search trends to analyze web-based users' behavior profiles connected with COVID-19 in mainland China: infodemiology study based on hot words and Baidu Index

Background

Mainland China, the world's most populous region, experienced a large-scale coronavirus disease 2019 (COVID-19) outbreak in 2020 and 2021, respectively. Existing infodemiology studies have primarily concentrated on the prospective surveillance of confirmed cases or symptoms which met the criterion for investigators; nevertheless, the actual impact regarding COVID-19 on the public and subsequent attitudes of different groups towards the COVID-19 epidemic were neglected.

Methods

This study aimed to examine the public web-based search trends and behavior patterns related to COVID-19 outbreaks in mainland China by using hot words and Baidu Index (BI). The initial hot words (the high-frequency words on the Internet) and the epidemic data (2019/12/01-2021/11/30) were mined from infodemiology platforms. The final hot words table was established by two-rounds of hot words screening and double-level hot words classification. Temporal distribution and demographic portraits of COVID-19 were queried by search trends service supplied from BI to perform the correlation analysis. Further, we used the parameter estimation to quantitatively forecast the geographical distribution of COVID-19 in the future.

Results

The final English-Chinese bilingual table was established including six domains and 32 subordinate hot words. According to the temporal distribution of domains and subordinate hot words in 2020 and 2021, the peaks of searching subordinate hot words and COVID-19 outbreak periods had significant temporal correlation and the subordinate hot words in COVID-19 Related and Territory domains were reliable for COVID-19 surveillance. Gender distribution results showed that Territory domain (the male proportion: 67.69%; standard deviation (SD): 5.88%) and Symptoms/Symptom and Public Health (the female proportion: 57.95%, 56.61%; SD: 0, 9.06%) domains were searched more by male and female groups respectively. The results of age distribution of hot words showed that people aged 20-50 (middle-aged people) had a higher online search intensity, and the group of 20-29, 30-39 years old focused more on Media and Symptoms/Symptom (proportion: 45.43%, 51.66%; SD: 15.37%, 16.59%) domains respectively. Finally, based on frequency rankings of searching hot words and confirmed cases in Mainland China, the epidemic situation of provinces and Chinese administrative divisions were divided into 5 levels of early-warning regions. Central, East and South China regions would be impacted again by the COVID-19 in the future.

Hybrid Deep Learning Techniques for Predicting Complex Phenomena: A Review on COVID-19

Complex phenomena have some common characteristics, such as nonlinearity, complexity, and uncertainty. In these phenomena, components typically interact with each other and a part of the system may affect other parts or vice versa. Accordingly, the human brain, the Earth’s global climate, the spreading of viruses, the economic organizations, and some engineering systems such as the transportation systems and power grids can be categorized into these phenomena. Since both analytical approaches and AI methods have some specific characteristics in solving complex problems, a combination of these techniques can lead to new hybrid methods with considerable performance. This is why several types of research have recently been conducted to benefit from these combinations to predict the spreading of COVID-19 and its dynamic behavior. In this review, 80 peer-reviewed articles, book chapters, conference proceedings, and preprints with a focus on employing hybrid methods for forecasting the spreading of COVID-19 published in 2020 have been aggregated and reviewed. These documents have been extracted from Google Scholar and many of them have been indexed on the Web of Science. Since there were many publications on this topic, the most relevant and effective techniques, including statistical models and deep learning (DL) or machine learning (ML) approach, have been surveyed in this research. The main aim of this research is to describe, summarize, and categorize these effective techniques considering their restrictions to be used as trustable references for scientists, researchers, and readers to make an intelligent choice to use the best possible method for their academic needs. Nevertheless, considering the fact that many of these techniques have been used for the first time and need more evaluations, we recommend none of them as an ideal way to be used in their project. Our study has shown that these methods can hold the robustness and reliability of statistical methods and the power of computation of DL ones.

Big Data COVID-19 Systematic Literature Review: Pandemic Crisis

The COVID-19 pandemic has frightened people worldwide, and coronavirus has become the most commonly used phrase in recent years. Therefore, there is a need for a systematic literature review (SLR) related to Big Data applications in the COVID-19 pandemic crisis. The objective is to highlight recent technological advancements. Many studies emphasize the area of the COVID-19 pandemic crisis. Our study categorizes the many applications used to manage and control the pandemic. There is a very limited SLR prospective of COVID-19 with Big Data. Our SLR study picked five databases: Science direct, IEEE Xplore, Springer, ACM, and MDPI. Before the screening, following the recommendation, Preferred Reporting Items for Systematic Reviews and Meta Analyses (PRISMA) were reported for 893 studies from 2019, 2020 and until September 2021. After screening, 60 studies met the inclusion criteria through COVID-19 data statistics, and Big Data analysis was used as the search string. Our research’s findings successfully dealt with COVID-19 healthcare with risk diagnosis, estimation or prevention, decision making, and drug Big Data applications problems. We believe that this review study will motivate the research community to perform expandable and transparent research against the pandemic crisis of COVID-19.

Data and Digital Solutions to Support Surveillance Strategies in the Context of the COVID-19 Pandemic

Background: In order to prevent spread and improve control of infectious diseases, public health experts need to closely monitor human and animal populations. Infectious disease surveillance is an established, routine data collection process essential for early warning, rapid response, and disease control. The quantity of data potentially useful for early warning and surveillance has increased exponentially due to social media and other big data streams. Digital epidemiology is a novel discipline that includes harvesting, analysing, and interpreting data that were not initially collected for healthcare needs to enhance traditional surveillance. During the current COVID-19 pandemic, the importance of digital epidemiology complementing traditional public health approaches has been highlighted. Objective: The aim of this paper is to provide a comprehensive overview for the application of data and digital solutions to support surveillance strategies and draw implications for surveillance in the context of the COVID-19 pandemic and beyond. Methods: A search was conducted in PubMed databases. Articles published between January 2005 and May 2020 on the use of digital solutions to support surveillance strategies in pandemic settings and health emergencies were evaluated. Results: In this paper, we provide a comprehensive overview of digital epidemiology, available data sources, and components of 21st-century digital surveillance, early warning and response, outbreak management and control, and digital interventions. Conclusions: Our main purpose was to highlight the plausible use of new surveillance strategies, with implications for the COVID-19 pandemic strategies and then to identify opportunities and challenges for the successful development and implementation of digital solutions during non-emergency times of routine surveillance, with readiness for early-warning and response for future pandemics. The enhancement of traditional surveillance systems with novel digital surveillance methods opens a direction for the most effective framework for preparedness and response to future pandemics.

The Impact of the COVID-19 Emergency on Local Vehicular Traffic and Its Consequences for the Environment: The Case of the City of Reggio Emilia (Italy)

The COVID-19 health emergency has imposed the need to limit and/or stop non-essential economic and commercial activities and movement of people. The objective of this work is to report an assessment of the change in vehicle flows and in air quality of a specific study area in the north of Italy, comparing the periods February–May 2020 and February–May 2019. Circulating vehicles have been measured at nine characteristic points of the local road network of the city of Reggio Emilia (Italy), while atmospheric pollutant concentrations have been analysed using data extracted from the regional air quality monitoring network. The results highlight a rapid decline in the number of vehicles circulating in 2020 (with values of up to −82%). This has contributed to a reduction in air concentrations of pollutants, in particular for NO2 and CO (over 30% and over 22%, respectively). On the other hand, O3 has increased (by about +13%), but this is expected. Finally, the particulate matter grew (about 30%), with a behaviour similar to the whole regional territory. The empirical findings of this study provide some indications and useful information to assist in understanding the effects of traffic blocking in urban areas on air quality.

Impact of the Inflow Population From Outbreak Areas on the COVID-19 Epidemic in Yunnan Province and the Recommended Control Measures: A Preliminary Study

Background: COVID-19 developed into a global pandemic in 2020 and poses challenges regarding the prevention and control capabilities of countries. A large number of inbound travelers from other regions could lead to a renewed outbreak of COVID-19 in the local regions. Globally, as a result of the imbalance in the control of the epidemic, all countries are facing the risk of a renewed COVID-19 outbreak brought about by travelers from epidemic areas. Therefore, studies on a proper management of the inbound travelers are urgent.

Methods: We collected a total of 4,733,414 inbound travelers and 174 COVID-19 diagnosed patients in Yunnan province from 21 January 2020 to 20 February 2020. Data on place of origin, travel history, age, and gender, as well as whether they had suspected clinical manifestations for inbound travelers in Yunnan were collected. The impact of inbound travelers on the local epidemic was analyzed with a collinear statistical analysis and the effect of the control measures on the epidemic was evaluated with a sophisticated modeling approach.

Results: Of the 174 COVID-19 patients, 60.9% were not from Yunnan, and 76.4% had a history of travel in Hubei. The amount of new daily cases in Yunnan was significant correlated with the number of inbound travelers from Hubei and suspected cases among them. Using Susceptible–Exposed–Infectious–Recovered (SEIR) model analysis, we found that the prevention and control measures dropped the local R0 down to 1.07 in Yunnan province.

Conclusions: Our preliminary analysis showed that the proper management of inbound travelers from outbreak areas has a significantly positive effect on the prevention and control of the virus. In the process of resettlement, some effective measures taken by Yunnan province may provide an important reference for preventing the renewed COVID-19 outbreak in other regions.

Application of Big Data Technology for COVID-19 Prevention and Control in China: Lessons and Recommendations

BACKGROUND

In the prevention and control of infectious diseases, previous research on the application of big data technology has mainly focused on the early warning and early monitoring of infectious diseases. Although the application of big data technology for COVID-19 warning and monitoring remain important tasks, prevention of the disease's rapid spread and reduction of its impact on society are currently the most pressing challenges for the application of big data technology during the COVID-19 pandemic. After the outbreak of COVID-19 in Wuhan, the Chinese government and nongovernmental organizations actively used big data technology to prevent, contain, and control the spread of COVID-19.

OBJECTIVE

The aim of this study is to discuss the application of big data technology to prevent, contain, and control COVID-19 in China; draw lessons; and make recommendations.

METHODS

We discuss the data collection methods and key data information that existed in China before the outbreak of COVID-19 and how these data contributed to the prevention and control of COVID-19. Next, we discuss China's new data collection methods and new information assembled after the outbreak of COVID-19. Based on the data and information collected in China, we analyzed the application of big data technology from the perspectives of data sources, data application logic, data application level, and application results. In addition, we analyzed the issues, challenges, and responses encountered by China in the application of big data technology from four perspectives: data access, data use, data sharing, and data protection. Suggestions for improvements are made for data collection, data circulation, data innovation, and data security to help understand China's response to the epidemic and to provide lessons for other countries' prevention and control of COVID-19.

RESULTS

In the process of the prevention and control of COVID-19 in China, big data technology has played an important role in personal tracking, surveillance and early warning, tracking of the virus's sources, drug screening, medical treatment, resource allocation, and production recovery. The data used included location and travel data, medical and health data, news media data, government data, online consumption data, data collected by intelligent equipment, and epidemic prevention data. We identified a number of big data problems including low efficiency of data collection, difficulty in guaranteeing data quality, low efficiency of data use, lack of timely data sharing, and data privacy protection issues. To address these problems, we suggest unified data collection standards, innovative use of data, accelerated exchange and circulation of data, and a detailed and rigorous data protection system.

CONCLUSIONS

China has used big data technology to prevent and control COVID-19 in a timely manner. To prevent and control infectious diseases, countries must collect, clean, and integrate data from a wide range of sources; use big data technology to analyze a wide range of big data; create platforms for data analyses and sharing; and address privacy issues in the collection and use of big data.