A Spatial-Temporal Method to Detect Global Influenza Epidemics Using Heterogeneous Data Collected from the Internet

A novel graph neural network based approach for influenza-like illness nowcasting: exploring the interplay of temporal, geographical, and functional spatial features

BACKGROUND

Accurate and timely monitoring of influenza prevalence is essential for effective healthcare interventions. This study proposes a graph neural network (GNN)-based method to address the issue of cross-regional connectivity in predicting influenza outbreaks, aiming to achieve real-time and accurate influenza prediction.

METHODS

We proposed a GNN-based approach with dual topology processing, capturing both geographical and socio-economic associations among counties/cities. The model inputs consist of weekly matrices of influenza-like illness (ILI) rates at city level, along with geographical topology and functional topology. The model construction involves temporal feature extraction through 1-dimensional gated causal convolution, spatial feature embedding through graph convolution, and additional adjustments to enhance spatiotemporal interaction exploration. Evaluation metrics include four commonly used measures: root mean square error (RMSE), mean absolute percentage error (MAPE), mean absolute error (MAE), and Pearson correlation (Corr).

RESULTS

Our approach for predicting influenza outbreaks achieves competitive performance on real-world datasets (Corr = 0.8202; RMSE = 0.0017; MAE = 0.0013; MAPE = 0.0966), surpassing established baselines. Notably, our approach exhibits excellent capability in accurately and timely capturing short-term influenza outbreaks during the flu season, outperforming competitors across all evaluation metrics.

CONCLUSION

The incorporation of dual topology processing and the subsequent fusion mechanism allows the model to explore in-depth spatiotemporal feature interactions. Demonstrating superior performance, our approach shows great potential in early detection of flu trends for facilitating public health decisions and resource optimization.

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.

Spatio-Temporal Clustering of Multi-Location Time Series to Model Seasonal Influenza Spread

Although seasonal influenza disease spread is a spatio-temporal phenomenon, public surveillance systems aggregate data only spatially, and are rarely predictive. We develop a hierarchical clustering-based machine learning tool to anticipate flu spread patterns based on historical spatio-temporal flu activity, where we use historical influenza-related emergency department records as a proxy for flu prevalence. This analysis replaces conventional geographical hospital clustering with clusters based on both spatial and temporal distance between hospital flu peaks to generate a network illustrating whether flu spreads between pairs of clusters (direction) and how long that spread takes (magnitude). To overcome data sparsity, we take a model-free approach, treating hospital clusters as a fully-connected network, where arcs indicate flu transmission. We perform predictive analysis on the clusters' time series of flu ED visits to determine direction and magnitude of flu travel. Detection of recurrent spatio-temporal patterns may help policymakers and hospitals better prepare for outbreaks. We apply this tool to Ontario, Canada using a five-year historical dataset of daily flu-related ED visits, and find that in addition to expected flu spread between major cities/airport regions, we were able to illuminate previously unsuspected patterns of flu spread between non-major cities, providing new insights for public health officials. We showed that while a spatial clustering outperforms a temporal clustering in terms of the direction of the spread (81% spatial v. 71% temporal), the opposite is true in terms of the magnitude of the time lag (20% spatial v. 70% temporal).

Applications of Technological Solutions in Primary Ways of Preventing Transmission of Respiratory Infectious Diseases-A Systematic Literature Review

With the growing concern about the spread of new respiratory infectious diseases, several studies involving the application of technology in the prevention of these diseases have been carried out. Among these studies, it is worth highlighting the importance of those focused on the primary forms of prevention, such as social distancing, mask usage, quarantine, among others. This importance arises because, from the emergence of a new disease to the production of immunizers, preventive actions must be taken to reduce contamination and fatalities rates. Despite the considerable number of studies, no records of works aimed at the identification, registration, selection, and rigorous analysis and synthesis of the literature were found. For this purpose, this paper presents a systematic review of the literature on the application of technological solutions in the primary ways of respiratory infectious diseases transmission prevention. From the 1139 initially retrieved, 219 papers were selected for data extraction, analysis, and synthesis according to predefined inclusion and exclusion criteria. Results enabled the identification of a general categorization of application domains, as well as mapping of the adopted support mechanisms. Findings showed a greater trend in studies related to pandemic planning and, among the support mechanisms adopted, data and mathematical application-related solutions received greater attention. Topics for further research and improvement were also identified such as the need for a better description of data analysis and evidence.

A Comprehensive Survey of Enabling and Emerging Technologies for Social Distancing-Part II: Emerging Technologies and Open Issues

This two-part paper aims to provide a comprehensive survey on how emerging technologies, e.g., wireless and networking, artificial intelligence (AI) can enable, encourage, and even enforce social distancing practice. In Part I, an extensive background of social distancing is provided, and enabling wireless technologies are thoroughly surveyed. In this Part II, emerging technologies such as machine learning, computer vision, thermal, ultrasound, etc., are introduced. These technologies open many new solutions and directions to deal with problems in social distancing, e.g., symptom prediction, detection and monitoring quarantined people, and contact tracing. Finally, we discuss open issues and challenges (e.g., privacy-preserving, scheduling, and incentive mechanisms) in implementing social distancing in practice. As an example, instead of reacting with ad-hoc responses to COVID-19-like pandemics in the future, smart infrastructures (e.g., next-generation wireless systems like 6G, smart home/building, smart city, intelligent transportation systems) should incorporate a pandemic mode in their standard architectures/designs.

Syndromic surveillance using web data: a systematic review

During the recent years, a lot of debate is taken place about the evolution of Smart Healthcare systems. Particularly, how these systems can help people improve human conditions of health, by taking advantages of the new Information and Communication Technologies (ICT), regarding early prediction and efficient treatment. The purpose of this study is to provide a systematic review of the current literature available that focuses on information systems on syndromic surveillance using web data. All published items concern articles, books, reviews, reports, conference announcements, and dissertations. We used a variation of PRISMA Statements methodology to conduct a systematic review. The review identifies the relevant published papers from the year 2004 to 2018, systematically includes and explores them to extract similarities, gaps, and conclusions on the research that has been done so far. The results presented concern the year, the examined disease, the web data source, the geographic location/country, and the data analysis method used. The results show that influenza is the most examined infectious disease. The internet tools most used are Twitter and Google. Regarding the geographical areas explored in the published papers, the most examined country is the United States, since many scientists come from this country. There is a significant growth of articles since 2009. There are also various statistical methods used to correlate the data retrieved from the internet to the data from national authorities. The conclusion of all researches is that the Web can be a useful tool for the detection of serious epidemics and for a creation of a syndromic surveillance system using the Web, since we can predict epidemics from web data before they are officially detected in population. With the advance of ICT, Smart Healthcare can benefit from the monitoring of epidemics and the early prediction of such a system, improving national or international health strategies and policy decision. This can be achieved through the provision of new technology tools to enhance health monitoring systems toward the new innovations of Smart Health or eHealth, even with the emerging technologies of Internet of Things. The challenges and impacts of an electronic system based on internet data include the social, medical, and technological disciplines. These can be further extended to Smart Healthcare, as the data streaming can provide with real-time information, awareness on epidemics and alerts for both patients or medical scientists. Finally, these new systems can help improve the standards of human life.

Effects of the Ambient Fine Particulate Matter on Public Awareness of Lung Cancer Risk in China: Evidence from the Internet-Based Big Data Platform

Background

In October 2013, the International Agency for Research on Cancer classified the particulate matter from outdoor air pollution as a group 1 carcinogen and declared that particulate matter can cause lung cancer. Fine particular matter (PM_2.5) pollution is becoming a serious public health concern in urban areas of China. It is essential to emphasize the importance of the public’s awareness and knowledge of modifiable risk factors of lung cancer for prevention.

Objective

The objective of our study was to explore the public’s awareness of the association of PM_2.5 with lung cancer risk in China by analyzing the relationship between the daily PM_2.5 concentration and searches for the term “lung cancer” on an Internet big data platform, Baidu.

Methods

We collected daily PM_2.5 concentration data and daily Baidu Index data in 31 Chinese capital cities from January 1, 2014 to December 31, 2016. We used Spearman correlation analysis to explore correlations between the daily Baidu Index for lung cancer searches and the daily average PM_2.5 concentration. Granger causality test was used to analyze the causal relationship between the 2 time-series variables.

Results

In 23 of the 31 cities, the pairwise correlation coefficients (Spearman rho) between the daily Baidu Index for lung cancer searches and the daily average PM_2.5 concentration were positive and statistically significant (P<.05). However, the correlation between the daily Baidu Index for lung cancer searches and the daily average PM_2.5 concentration was poor (all r²_s<.1). Results of Granger causality testing illustrated that there was no unidirectional causality from the daily PM_2.5 concentration to the daily Baidu Index for lung cancer searches, which was statistically significant at the 5% level for each city.

Conclusions

The daily average PM_2.5 concentration had a weak positive impact on the daily search interest for lung cancer on the Baidu search engine. Well-designed awareness campaigns are needed to enhance the general public’s awareness of the association of PM_2.5 with lung cancer risk, to lead the public to seek more information about PM_2.5 and its hazards, and to cope with their environment and its risks appropriately.