Comparison of Social Media, Syndromic Surveillance, and Microbiologic Acute Respiratory Infection Data: Observational Study

Global infectious disease early warning models: An updated review and lessons from the COVID-19 pandemic

An early warning model for infectious diseases is a crucial tool for timely monitoring, prevention, and control of disease outbreaks. The integration of diverse multi-source data using big data and artificial intelligence techniques has emerged as a key approach in advancing these early warning models. This paper presents a comprehensive review of widely utilized early warning models for infectious diseases around the globe. Unlike previous review studies, this review encompasses newly developed approaches such as the combined model and Hawkes model after the COVID-19 pandemic, providing a thorough evaluation of their current application status and development prospects for the first time. These models not only rely on conventional surveillance data but also incorporate information from various sources. We aim to provide valuable insights for enhancing global infectious disease surveillance and early warning systems, as well as informing future research in this field, by summarizing the underlying modeling concepts, algorithms, and application scenarios of each model.

Ethical Issues in AI-Enabled Disease Surveillance: Perspectives from Global Health

Infectious diseases, as COVID-19 is proving, pose a global health threat in an interconnected world. In the last 20 years, resistant infectious diseases such as severe acute respiratory syndrome (SARS), Middle East respiratory syndrome (MERS), H1N1 influenza (swine flu), Ebola virus, Zika virus, and now COVID-19 have been impacting global health defences, and aggressively flourishing with the rise of global travel, urbanization, climate change, and ecological degradation. In parallel, this extraordinary episode in global human health highlights the potential for artificial intelligence (AI)-enabled disease surveillance to collect and analyse vast amounts of unstructured and real-time data to inform epidemiological and public health emergency responses. The uses of AI in these dynamic environments are increasingly complex, challenging the potential for human autonomous decisions. In this context, our study of qualitative perspectives will consider a responsible AI framework to explore its potential application to disease surveillance in a global health context. Thus far, there is a gap in the literature in considering these multiple and interconnected levels of disease surveillance and emergency health management through the lens of a responsible AI framework.

Forecasting the Potential Number of Influenza-like Illness Cases by Fusing Internet Public Opinion

As influenza viruses mutate rapidly, a prediction model for potential outbreaks of influenza-like illnesses helps detect the spread of the illnesses in real time. In order to create a better prediction model, in this study, in addition to using the traditional hydrological and atmospheric data, features, such as popular search keywords on Google Trends, public holiday information, population density, air quality indices, and the numbers of COVID-19 confirmed cases, were also used to train the model in this research. Furthermore, Random Forest and XGBoost were combined and used in the proposed prediction model to increase the prediction accuracy. The training data used in this research were the historical data taken from 2016 to 2021. In our experiments, different combinations of features were tested. The results show that features, such as popular search keywords on Google Trends, the numbers of COVID-19 confirmed cases, and air quality indices can improve the outcome of the prediction model. The evaluation results showed that the error rate between the predicted results and the actual number of influenza-like cases form Week 15 to Week 18 fell to less than 5%. The outbreak of COVID-19 in Taiwan began in Week 19 and resulted in a sharp rise in the number of clinic or hospital visits by patients of influenza-like illnesses. After that, from Week 21 to Week 26, the error rate between the predicted and actual numbers of influenza-like cases in the later period dropped down to 13%. It can be confirmed from the actual experimental results in this research that the use of the ensemble learning prediction model proposed in this research can accurately predict the trend of influenza-like cases.

Relationship between internet research data of oral neoplasms and public health programs in the European Union

BACKGROUND

Tobacco and alcohol are the main risk factors for oral squamous cell carcinoma, the low survival rate of which is a public health problem. European-wide health policies (a prevention campaign, tobacco packaging) have been put in place to inform the population of the risks associated with consumption. Due to the increase in smoking among women, the incidence of this disease remains high. The identification of internet research data on the population could help to measure the impact of and better position these preventive measures. The objective was to analyze a potential temporal association between public health programs and interest in oral cancers on the internet in the European Union (EU).

METHODS

A search of data from Google ©, Wikipedia © and Twitter © users in 28 European countries relating to oral cancer between 2004 and 2019 was completed. Bibliometric analysis of press and scientific articles over the same period was also performed. The association between these data and the introduction of public health programs in Europe was studied.

RESULTS

There was a temporal association between changes in tobacco packaging and a significant increase in internet searches for oral cancer in seven countries. Unlike national policies and ad campaigns, the European awareness program Make Sense has had no influence on internet research. There was an asymmetric correlation in internet searches between publications on oral cancer from scientific articles or "traditional" media (weak association) and those from internet media such as Twitter © or Wikipedia © (strong association).

CONCLUSION

Our work highlights seven areas around which oral cancer awareness in Europe could be refocused, such as a change in the communication of health warnings on cigarette packs, the establishment of a more explicit campaign name regarding oral cancer, the involvement of public figures and associations in initiatives to be organized at the local level and the strengthening of awareness of the dangers of tobacco in the development of oral cancer.

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.

Role of Participatory Health Informatics in Detecting and Managing Pandemics: Literature Review

OBJECTIVES

Using participatory health informatics (PHI) to detect disease outbreaks or learn about pandemics has gained interest in recent years. However, the role of PHI in understanding and managing pandemics, citizens' role in this context, and which methods are relevant for collecting and processing data are still unclear, as is which types of data are relevant. This paper aims to clarify these issues and explore the role of PHI in managing and detecting pandemics.

METHODS

Through a literature review we identified studies that explore the role of PHI in detecting and managing pandemics. Studies from five databases were screened: PubMed, CINAHL (Cumulative Index to Nursing and Allied Health Literature), IEEE Xplore, ACM (Association for Computing Machinery) Digital Library, and Cochrane Library. Data from studies fulfilling the eligibility criteria were extracted and synthesized narratively.

RESULTS

Out of 417 citations retrieved, 53 studies were included in this review. Most research focused on influenza-like illnesses or COVID-19 with at least three papers on other epidemics (Ebola, Zika or measles). The geographic scope ranged from global to concentrating on specific countries. Multiple processing and analysis methods were reported, although often missing relevant information. The majority of outcomes are reported for two application areas: crisis communication and detection of disease outbreaks.

CONCLUSIONS

For most diseases, the small number of studies prevented reaching firm conclusions about the utility of PHI in detecting and monitoring these disease outbreaks. For others, e.g., COVID-19, social media and online search patterns corresponded to disease patterns, and detected disease outbreak earlier than conventional public health methods, thereby suggesting that PHI can contribute to disease and pandemic monitoring.

Mining and Validating Social Media Data for COVID-19-Related Human Behaviors Between January and July 2020: Infodemiology Study

Background

Health authorities can minimize the impact of an emergent infectious disease outbreak through effective and timely risk communication, which can build trust and adherence to subsequent behavioral messaging. Monitoring the psychological impacts of an outbreak, as well as public adherence to such messaging, is also important for minimizing long-term effects of an outbreak.

Objective

We used social media data from Twitter to identify human behaviors relevant to COVID-19 transmission, as well as the perceived impacts of COVID-19 on individuals, as a first step toward real-time monitoring of public perceptions to inform public health communications.

Methods

We developed a coding schema for 6 categories and 11 subcategories, which included both a wide number of behaviors as well codes focused on the impacts of the pandemic (eg, economic and mental health impacts). We used this to develop training data and develop supervised learning classifiers for classes with sufficient labels. Classifiers that performed adequately were applied to our remaining corpus, and temporal and geospatial trends were assessed. We compared the classified patterns to ground truth mobility data and actual COVID-19 confirmed cases to assess the signal achieved here.

Results

We applied our labeling schema to approximately 7200 tweets. The worst-performing classifiers had F1 scores of only 0.18 to 0.28 when trying to identify tweets about monitoring symptoms and testing. Classifiers about social distancing, however, were much stronger, with F1 scores of 0.64 to 0.66. We applied the social distancing classifiers to over 228 million tweets. We showed temporal patterns consistent with real-world events, and we showed correlations of up to –0.5 between social distancing signals on Twitter and ground truth mobility throughout the United States.

Conclusions

Behaviors discussed on Twitter are exceptionally varied. Twitter can provide useful information for parameterizing models that incorporate human behavior, as well as for informing public health communication strategies by describing awareness of and compliance with suggested behaviors.

WITHDRAWN: Can Traditional Chinese Medicine provide insights into controlling the COVID-19 pandemic: Serpentinization-induced lithospheric long-wavelength magnetic anomalies in Proterozoic bedrocks in a weakened geomagnetic field mediate the aberrant transformation of biogenic molecules in COVID-19 via magnetic catalysis

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