Internet search data with spatiotemporal analysis in infectious disease surveillance: Challenges and perspectives

Sun, Hua; Zhang, Yuzhou; Gao, Guang; Wu, Dun

doi:10.3389/fpubh.2022.958835

Cited by 4 publications

(3 citation statements)

References 71 publications

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“…First, the strong temporal correlations observed suggest that online search data could serve as an early warning system for infectious disease outbreaks and therefore future research can focus on refining models to improve the accuracy of early detection, enabling more timely public health interventions [ 26 , 27 ]. Particularly, further research and analysis are needed to tease apart the individual contributions of these factors and to develop robust models for effectively utilizing online search data in disease surveillance [ 28 , 29 , 30 ]. Additionally, ongoing monitoring and adaptation of the methodologies will be essential as the landscape of online information and public health evolves [ 31 , 32 ].…”

Section: Discussionmentioning

confidence: 99%

Infodemiology and Infoveillance of the Four Most Widespread Arbovirus Diseases in Italy

Santangelo,

Provenzano,

Vella

et al. 2024

Epidemiologia

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The purpose of this observational study was to evaluate the potential epidemiological trend of arboviral diseases most reported in Italy by the dedicated national surveillance system (ISS data) compared to searches on the internet, assessing whether a correlation/association between users’ searches in Google and Wikipedia and real cases exists. The study considers a time interval from June 2012 to December 2023. We used the following Italian search terms: “Virus Toscana”, “Virus del Nilo occidentale” (West Nile Virus in English), “Encefalite trasmessa da zecche” (Tick Borne encephalitis in English), and “Dengue”. We overlapped Google Trends and Wikipedia data to perform a linear regression and correlation analysis. Statistical analyses were performed using Pearson’s correlation coefficient (r) or Spearman’s rank correlation coefficient (rho) as appropriate. All the correlations between the ISS data and Wikipedia or GT exhibited statistical significance. The correlations were strong for Dengue GT and ISS (rho = 0.71) and TBE GT and ISS (rho = 0.71), while the remaining correlations had values of r and rho between 0.32 and 0.67, showing a moderate temporal correlation. The observed correlations and regression models provide a foundation for future research, encouraging a more nuanced exploration of the dynamics between digital information-seeking behavior and disease prevalence.

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Section: Discussionmentioning

confidence: 99%

Infodemiology and Infoveillance of the Four Most Widespread Arbovirus Diseases in Italy

Santangelo,

Provenzano,

Vella

et al. 2024

Epidemiologia

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“…In the eld of conceptual de nition, Chinese scholars have summarized the concept of public health emergencies from the perspectives of their characteristics, nature, and classi cation (4,6). In the eld of emergency capacity building, Chinese research has mainly focused on public health emergency systems and mechanisms, information system construction, media response, and crisis public relations, including current situations, issues, and countermeasures (7)(8)(9)(10)(11)(12). In the eld of policy research, studies have analyzed the content of policies for response to public health emergencies (13,14), the evolution of policy themes (15,17), policy evaluation (18,19), policy system improvement (20,21), policy comparison and reference (22,23), and other aspects.…”

Section: Literature Reviewmentioning

confidence: 99%

Quantitative Evaluation of China’s Public Health Emergencies Response Policies：A PMC index model approach

Yong-wen

Jiao

Wang

et al. 2023

Preprint

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Background Public health emergencies response polices (PHERPs) provide a basis and institutional guarantee for the implementation of emergency response work. At present, China has made certain achievements in emergency response for public health emergencies (PHE). In order to further improve the emergency response capability, it is necessary to conduct a systematic analysis of relevant policies, understand their content and role, and clarify directions for policy improvement.Methods Based on 33 comprehensive public health emergencies response policies (PHERPs) released by the central level of China from 2003 to 2019, text mining and content analysis were used to construct a PMC index model with 10 first-lever and 46 second-level variables. The policies were quantitatively evaluated to analyze their strengths and weaknesses.Results In this study, finds that the average PMC index value for the 33 policies was 6.43, with an average rating of acceptable. Among them, 10 policies were rated as excellent, 23 policies were rated as acceptable, and there were no poor policies. For the first-level variables, the mean scores for policy openness, policy structure, policy area, policy focus, and policy nature were relatively high, at 1.00, 0.88, 0.87, 0.85, and 0.86, respectively. However, the scores for policy timeliness, policy issuing agency, incentive measures, policy target, and policy function were relatively low, at 0.25, 0.27, 0.41, 0.54, and 0.50, respectively, which need to be given more attention.Conclusions In this study, we also emphasized that the use of the PMC index model policy evaluation method in researching policies related to emergency response to PHE is relatively rare. These findings contribute to a more comprehensive understanding of the strengths and weaknesses of PHERPs, and provide a reference for further research.

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“…The prospective and promising usage of the Internet data sources in infectious disease and epidemic surveillance is realized increasingly as an effective tool. Data's spatial and temporal distribution can be utilized for internet-based surveillance, disease forecasting and modeling ( 40 ).…”

Section: Introductionmentioning

confidence: 99%

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

Stefanis,

Giorgi,

Kalentzis

et al. 2023

Front. Public Health

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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.

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Internet search data with spatiotemporal analysis in infectious disease surveillance: Challenges and perspectives

Cited by 4 publications

References 71 publications

Infodemiology and Infoveillance of the Four Most Widespread Arbovirus Diseases in Italy

Infodemiology and Infoveillance of the Four Most Widespread Arbovirus Diseases in Italy

Quantitative Evaluation of China’s Public Health Emergencies Response Policies：A PMC index model approach

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

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