• Baidu
• Mpox
• WeChat
• big data
• early warning
• public concern

• Humans
• China/epidemiology
• Disease Outbreaks
• Big Data

• Arthroscopy
• Baidu index
• Health education
• Nursing care
• Public attention

• Artificial intelligence
• Flu prediction
• Forecasting
• Forecasting healthcare burden
• Influenza-like illness
• Machine learning
• Probabilistic forecast
• Seasonal epidemic
• Severe respiratory diseases

• Humans
• Machine Learning
• Forecasting
• New Zealand/epidemiology
• Hospitalization/statistics & numerical data
• Respiratory Tract Infections/epidemiology
• Neural Networks, Computer

• Baidu index
• multiple linear regression
• prediction
• timely
• tuberculosis

• Humans
• Public Health/methods
• COVID-19/epidemiology
• COVID-19/prevention & control
• Emergencies
• Big Data
• Pandemics/prevention & control
• Grounded Theory

• correlation
• influenza
• scoping review
• surveillance
• syndromic
• timeliness

• Electronic Health Records
• Humans
• Influenza, Human/epidemiology
• Sentinel Surveillance

• 001 World Health Organization

The respiratory syncytial virus (RSV) disease burden is significant, especially in infants and children with an underlying disease. Prophylaxis with palivizumab is recommended for these high-risk groups. Early recognition of a RSV epidemic is important for timely administration of palivizumab. We herein aimed to assess the correlation between national surveillance and Google Trends data pertaining to RSV infections in Japan.

The present, retrospective survey was performed between January 1, 2018 and November 14, 2021 and evaluated the correlation between national surveillance data and Google Trends data. Joinpoint regression was used to identify the points at which changes in trends occurred.

A strong correlation was observed every study year (2018 [r = 0.87, p < 0.01], 2019 [r = 0.83, p < 0.01], 2020 [r = 0.83, p < 0.01], and 2021 [r = 0.96, p < 0.01]). The change-points in the Google Trends data indicating the start of the RSV epidemic were observed earlier than by sentinel surveillance in 2018 and 2021 and simultaneously with sentinel surveillance in 2019. No epidemic surge was observed in either the Google Trends or the surveillance data from 2020.

Our data suggested that Google Trends has the potential to enable the early identification of RSV epidemics. In countries without a national surveillance system, Google Trends may serve as an alternative early warning system.

The online version contains supplementary material available at 10.1186/s12889-022-13899-y.

• Epidemiology
• Google Trends
• RSV
• Surveillance

Early surveillance to prevent the spread of influenza is a major public health concern. If there is an association of influenza epidemics with mobile app data, it may be possible to forecast influenza earlier and more easily.

We aimed to assess the relationship between seasonal influenza and the frequency of mobile app use among children in Osaka Prefecture, Japan.

This was a retrospective observational study that was performed over a three-year period from January 2017 to December 2019. Using a linear regression model, we calculated the R² value of the regression model to evaluate the relationship between the number of “fever” events selected in the mobile app and the number of influenza patients ≤14 years of age. We conducted three-fold cross-validation using data from two years as the training data set and the data of the remaining year as the test data set to evaluate the validity of the regression model. And we calculated Spearman correlation coefficients between the calculated number of influenza patients estimated using the regression model and the number of influenza patients, limited to the period from December to April when influenza is prevalent in Japan.

We included 29,392 mobile app users. The R² value for the linear regression model was 0.944, and the adjusted R² value was 0.915. The mean Spearman correlation coefficient for the three regression models was 0.804. During the influenza season (December–April), the Spearman correlation coefficient between the number of influenza patients and the calculated number estimated using the linear regression model was 0.946 (P<.001).

In this study, the number of times that mobile apps were used was positively associated with the number of influenza patients. In particular, there was a good association of the number of influenza patients with the number of “fever” events selected in the mobile app during the influenza epidemic season.

• children
• epidemic
• influenza
• mobile app
• syndromic surveillance

• Forecast
• Health risk
• LRTIs
• Meteorological factors
• Time series

• Algorithms
• Health Status
• Humans
• Meteorological Concepts
• Respiratory Tract Infections/epidemiology
• Time Factors

• 81773806 National Natural Science Foundation of China
• 81874331 National Natural Science Foundation of China
• CPU2018GY19 Double-Class University project

• Humans
• Neural Networks, Computer
• Forecasting/methods
• Influenza, Human/epidemiology
• Deep Learning
• Epidemiological Models

• Sandia National Laboratories

• Deep learning
• Epidemic
• Influenza risk prediction
• Multi-granularity features
• Spatiotemporal neural network

For epidemics control and prevention, timely insights of potential hot spots are invaluable. Alternative to traditional epidemic surveillance, which often lags behind real time by weeks, big data from the Internet provide important information of the current epidemic trends. Here we present a methodology, ARGOX (Augmented Regression with GOogle data CROSS space), for accurate real-time tracking of state-level influenza epidemics in the United States. ARGOX combines Internet search data at the national, regional and state levels with traditional influenza surveillance data from the Centers for Disease Control and Prevention, and accounts for both the spatial correlation structure of state-level influenza activities and the evolution of people’s Internet search pattern. ARGOX achieves on average 28% error reduction over the best alternative for real-time state-level influenza estimation for 2014 to 2020. ARGOX is robust and reliable and can be potentially applied to track county- and city-level influenza activity and other infectious diseases.

• disease detection
• flu
• sentiment analysis
• social media
• text classification

• Communicable Diseases/diagnosis
• Communicable Diseases/epidemiology
• Disease Outbreaks
• Epidemics
• Humans
• Social Media

• age-adjusted emergency rooms visit rates
• age-period-cohort
• pneumonia

Replacing traditional surveillance with syndromic surveillance is one of the major interests in public health. However, it is unclear whether the number of influenza patients is associated with the number of telephone triages in Japan.

This retrospective, observational study was conducted over the six-year period between January 2012 to December 2017. We used the dataset of a telephone triage service in Osaka, Japan and the data on influenza patients published from the Information Center of Infectious Disease in Osaka prefecture. Using a linear regression model, we calculated Spearman’s rank-order coefficient and R² of the regression model to assess the relationship between the number of telephone triages for fever and the number of influenza patients in Osaka. Furthermore, we calculated Spearman’s rank-order coefficient and R² between the predicted weekly number of influenza patients from the linear regression model and the actual weekly number of influenza patients for influenza outbreak season (December-April).

There were 465,971 patients with influenza, and the number of telephone triages for fever was 420,928 among 1,065,628 total telephone triages during the study period. Our analysis showed that the Spearman rank-order coefficient was 0.932, and R² and adjusted R² were 0.869 and 0.842, respectively. The Spearman rank-order coefficient was 0.923 (P<0.001) and R² was 0.832 in December-April (P<0.001).

We revealed a positive relationship in this population between the number of influenza patients and the number of telephone triages for fever.

• epidemiology
• infection control
• statistics & research methods

• Acquired Immunodeficiency Syndrome/diagnosis
• Acquired Immunodeficiency Syndrome/epidemiology
• China/epidemiology
• Epidemics
• Gonorrhea/diagnosis
• Gonorrhea/epidemiology
• HIV Infections/diagnosis
• HIV Infections/epidemiology
• Humans
• Syphilis/diagnosis
• Syphilis/epidemiology

• China Medical Board
• National Natural Science Foundation of China
• Australian National Health and Medical Research Council Early Career Fellowship
• A Major Infectious Disease Prevention and Control of the National Science and Technique Major Project
• National Science and Technology Major Project of China
• Sanming Project of Medicine in Shenzhen, China

Public health surveillance is based on the continuous and systematic collection, analysis, and interpretation of data. This informs the development of early warning systems to monitor epidemics and documents the impact of intervention measures. The introduction of digital data sources, and specifically sources available on the internet, has impacted the field of public health surveillance. New opportunities enabled by the underlying availability and scale of internet-based sources (IBSs) have paved the way for novel approaches for disease surveillance, exploration of health communities, and the study of epidemic dynamics. This field and approach is also known as infodemiology or infoveillance.

This review aimed to assess research findings regarding the application of IBSs for public health surveillance (infodemiology or infoveillance). To achieve this, we have presented a comprehensive systematic literature review with a focus on these sources and their limitations, the diseases targeted, and commonly applied methods.

A systematic literature review was conducted targeting publications between 2012 and 2018 that leveraged IBSs for public health surveillance, outbreak forecasting, disease characterization, diagnosis prediction, content analysis, and health-topic identification. The search results were filtered according to previously defined inclusion and exclusion criteria.

Spanning a total of 162 publications, we determined infectious diseases to be the preferred case study (108/162, 66.7%). Of the eight categories of IBSs (search queries, social media, news, discussion forums, websites, web encyclopedia, and online obituaries), search queries and social media were applied in 95.1% (154/162) of the reviewed publications. We also identified limitations in representativeness and biased user age groups, as well as high susceptibility to media events by search queries, social media, and web encyclopedias.

IBSs are a valuable proxy to study illnesses affecting the general population; however, it is important to characterize which diseases are best suited for the available sources; the literature shows that the level of engagement among online platforms can be a potential indicator. There is a necessity to understand the population’s online behavior; in addition, the exploration of health information dissemination and its content is significantly unexplored. With this information, we can understand how the population communicates about illnesses online and, in the process, benefit public health.

• chronic diseases
• infectious diseases
• infodemiology
• infoveillance
• medical informatics
• public health
• public health informatics

• emergency department visit prediction
• emergency medicine
• google search
• healthcare big data analytics
• predictive modeling

• Boston
• Emergency Service, Hospital/statistics & numerical data
• Emergency Service, Hospital/trends
• Forecasting/methods
• Hospitals, Pediatric/statistics & numerical data
• Information Storage and Retrieval
• Internet
• Models, Statistical
• Search Engine

• R01 GM130668 NIGMS NIH HHS
• NIH

This study aimed to predict seasonal influenza activity and detection of influenza outbreaks. Data of all registered cases (n = 53,526) of influenza-like illnesses (ILIs) from sentinel sites of healthcare centers in Iran were obtained from the FluNet web-based tool, World Health Organization (WHO), from 2010 to 2015. The status of the ILI activity was obtained from the FluNet and considered as the gold standard of the seasonal activity of influenza during the study period. The cumulative sum (CUSUM) as an outbreak detection method was used to predict the seasonal activity of influenza. Also, time series similarity between the ILI trend and CUSUM was assessed using the cross-correlogram. Of 7684 (14%) positive cases of influenza, about 71% were type A virus and 28% were type B virus. The majority of the outbreaks occurred in winter and autumn. Results of the cross-correlogram showed that there was a considerable similarity between time series graphs of the ILI cases and CUSUM values. However, the CUSUM algorithm did not have a good performance in the timely detection of influenza activity. Despite a considerable similarity between time series of the ILI cases and CUSUM algorithm in weekly lag, the seasonal activity of influenza in Iran could not be predicted by the CUSUM algorithm.

• CUSUM
• Iran
• influenza
• outbreak
• public health surveillance

Social networking sites (SNSs) such as Twitter are widely used by diverse demographic populations. The amount of data within SNSs has created an efficient resource for real-time analysis. Thus, data from SNSs can be used effectively to track disease outbreaks and provide necessary warnings. Current SNS-based flu detection and prediction frameworks apply conventional machine learning approaches that require lengthy training and testing, which is not the optimal solution for new outbreaks with new signs and symptoms.

The objective of this study was to propose an efficient and accurate framework that uses data from SNSs to track disease outbreaks and provide early warnings, even for newest outbreaks, accurately.

We presented a framework of outbreak prediction that included 3 main modules: text classification, mapping, and linear regression for weekly flu rate predictions. The text classification module used the features of sentiment analysis and predefined keyword occurrences. Various classifiers, including FastText (FT) and 6 conventional machine learning algorithms, were evaluated to identify the most efficient and accurate one for the proposed framework. The text classifiers were trained and tested using a prelabeled dataset of flu-related and unrelated Twitter postings. The selected text classifier was then used to classify over 8,400,000 tweet documents. The flu-related documents were then mapped on a weekly basis using a mapping module. Finally, the mapped results were passed together with historical Centers for Disease Control and Prevention (CDC) data to a linear regression module for weekly flu rate predictions.

The evaluation of flu tweet classification showed that FT, together with the extracted features, achieved accurate results with an F-measure value of 89.9% in addition to its efficiency. Therefore, FT was chosen to be the classification module to work together with the other modules in the proposed framework, including a regression-based estimator, for flu trend predictions. The estimator was evaluated using several regression models. Regression results show that the linear regression–based estimator achieved the highest accuracy results using the measure of Pearson correlation. Thus, the linear regression model was used for the module of weekly flu rate estimation. The prediction results were compared with the available recent data from CDC as the ground truth and showed a strong correlation of 96.29% .

The results demonstrated the efficiency and the accuracy of the proposed framework that can be used even for new outbreaks with new signs and symptoms. The classification results demonstrated that the FT-based framework improves the accuracy and the efficiency of flu disease surveillance systems that use unstructured data such as data from SNSs.

• influenza
• infodemiology
• machine learning
• social networking site
• text classification

As the United Kingdom (UK) negotiates its separation from the European Union (EU), it is important to remember the public health mechanisms that are directly facilitated via our relationship with the EU. One such mechanism is the UK’s role within the European Centre for Disease Prevention and Control (ECDC). Global health protection is an area that is currently experiencing an unprecedented wave of innovation, both technologically and ideologically, and we must therefore ensure that our future relationship with ECDC is one that facilitates full involvement with the global health security systems of the future.

• Brexit
• Health protection
• Health security
• Infectious diseases

• Centers for Disease Control and Prevention, U.S./statistics & numerical data
• Data Analysis
• Disease Outbreaks/prevention & control
• Disease Outbreaks/statistics & numerical data
• Forecasting/methods
• Humans
• Influenza, Human/epidemiology
• Influenza, Human/prevention & control
• Models, Statistical
• Social Media/statistics & numerical data
• Support Vector Machine
• United States/epidemiology
• Vaccination

• National Nature Science Foundation of China
• Shanxi Natural Science Foundation

• Biomedical informatics
• Demographics
• Heatmaps
• Infectious disease spreading
• Influenza
• Social networks

• Data Mining
• Epidemiological Monitoring
• Humans
• Influenza, Human/epidemiology
• Internet

• NSF | Directorate for Mathematical &amp; Physical Sciences | Division of Mathematical Sciences (DMS)

• Data Analysis
• Databases, Factual
• Electronic Health Records
• Epidemiological Monitoring
• Humans
• Influenza, Human/epidemiology
• Internet
• Search Engine
• United States/epidemiology

• R01 GM130668 NIGMS NIH HHS

• epidemiology
• influenza
• internet
• pandemics
• participatory epidemiology
• participatory surveillance

This viewpoint paper argues that policy interventions can benefit from the continued use of social media analytics, which can serve as an important complement to traditional social science data collection and analysis. Efforts to improve well-being should provide an opportunity to explore these areas more deeply, and encourage the efforts of those conducting national and local data collection on health to incorporate more of these emerging data sources.

Social media remains a relatively untapped source of information to catalyze policy action and social change. However, the diversity of social media platforms and available analysis techniques provides multiple ways to offer insight for policy making and decision making. For instance, social media content can provide timely information about the impact of policy interventions. Social media location information can inform where to deploy resources or disseminate public messaging. Network analysis of social media connections can reveal underserved populations who may be disconnected from public services. Machine learning can help recognize important patterns for disease surveillance or to model population sentiment. To fully realize these potential policy uses, limitations to social media data will need to be overcome, including data reliability and validity, and potential privacy risks.

Traditional data collection may not fully capture the upstream factors and systemic relationships that influence health and well-being. Policy actions and social change efforts, such as the Robert Wood Johnson Foundation’s effort to advance a culture of health, which are intended to drive change in a network of upstream health drivers, will need to incorporate a broad range of behavioral information, such as health attitudes or physical activity levels. Applying innovative techniques to emerging data has the potential to extract insight from unstructured data or fuse disparate sources of data, such as linking health attitudes that are expressed to health behaviors or broader health and well-being outcomes.

• health knowledge, attitudes, practice
• health policy
• health promotion
• social change
• social media

• Google trends
• asthma
• big data
• forecasting
• health care
• health informatics
• internet behavior
• nowcasting
• online behavior
• smart health

• Flu trend
• Illness Like Influenza (ILI)
• Social media data

• Disease Outbreaks
• Humans
• Influenza, Human/diagnosis
• Influenza, Human/epidemiology
• Internet/statistics & numerical data
• Machine Learning/statistics & numerical data
• Neural Networks, Computer
• Predictive Value of Tests
• Search Engine/statistics & numerical data
• Social Networking

• Combination
• GAM
• HFMD
• Meteorological factors
• Monitoring
• Search engine query data

• Child
• Child, Preschool
• China/epidemiology
• Cities
• Hand, Foot and Mouth Disease/epidemiology
• Humans
• Incidence
• Infant
• Infant, Newborn
• Models, Statistical
• Search Engine
• Weather

• Disease
• G.3. Probability and statistics: Correlation and regression analysis
• H.1.1. Systems and information theory: Value of information
• H.3.5. Online information services: Web-based services
• J.3. Life and medical sciences: Health
• Wikipedia
• epidemiology
• forecasting
• modeling

• U01 GM097658 NIGMS NIH HHS

• Internet data
• disease
• forecasting
• modeling
• weather

• Behavior
• Communicable Diseases/epidemiology
• Computer Simulation
• Epidemics
• Forecasting/methods
• Humans
• Information Storage and Retrieval
• Internet
• Models, Theoretical

• U01 GM097658 NIGMS NIH HHS
• Los Alamos National Laboratory for public release
• National Institute of General Medical Sciences
• National Institutes of Health
• Los Alamos National Security, for the Department of Energy

• Bias
• Clinical Trials Data Monitoring Committees
• Data Interpretation, Statistical
• Databases, Factual
• Hand, Foot and Mouth Disease/epidemiology
• Humans
• Prevalence
• Reproducibility of Results
• Risk Assessment/methods
• Search Engine/methods
• Sensitivity and Specificity
• Sentinel Surveillance

• Ministry of Science and Technology of the People's Republic of China
• National Natural Science Foundation of China

In recent years social and news media have increasingly been used to explain patterns in disease activity and progression. Social media data, principally from the Twitter network, has been shown to correlate well with official disease case counts. This fact has been exploited to provide advance warning of outbreak detection, forecasting of disease levels and the ability to predict the likelihood of individuals developing symptoms. In this paper we introduce DEFENDER, a software system that integrates data from social and news media and incorporates algorithms for outbreak detection, situational awareness and forecasting. As part of this system we have developed a technique for creating a location network for any country or region based purely on Twitter data. We also present a disease nowcasting (forecasting the current but still unknown level) approach which leverages counts from multiple symptoms, which was found to improve the nowcasting accuracy by 37 percent over a model that used only previous case data. Finally we attempt to forecast future levels of symptom activity based on observed user movement on Twitter, finding a moderate gain of 5 percent over a time series forecasting model.

• Cloud Computing
• Electronic Health Records
• Geography
• Humans
• Influenza, Human/epidemiology
• Linear Models
• Population Surveillance
• Seasons

• autoregressive exogenous model
• big data
• digital disease detection
• influenza-like illnesses activity real-time estimation
• seasonal influenza

• Data Mining/methods
• Database Management Systems
• Databases, Factual
• Humans
• Influenza, Human/epidemiology
• Machine Learning
• Natural Language Processing
• Pattern Recognition, Automated/methods
• Population Surveillance/methods
• Prevalence
• Risk Assessment/methods
• Search Engine
• Seasons
• Social Media/statistics & numerical data
• United States/epidemiology
• Vocabulary, Controlled

• K01 ES025438 NIEHS NIH HHS
• R01 LM010812 NLM NIH HHS
• K01ES025438 NIEHS NIH HHS
• R01 LM010812-04 NLM NIH HHS

• clinical pharmacy data base
• patient outcomes
• pharmacy informatics