scholarly journals Influenza forecasting for the French regions by using EHR, web and climatic data sources with an ensemble approach ARGONet

2019 ◽  
Author(s):  
Canelle Poirier ◽  
Yulin Hswen ◽  
Guillaume Bouzillé ◽  
Marc Cuggia ◽  
Audrey Lavenu ◽  
...  
Keyword(s):  
Public Health ◽  
Machine Learning ◽  
Disease Activity ◽  
Real Time ◽  
Data Sources ◽  
Monitoring Systems ◽  
Surveillance Systems ◽  
Influenza Activity ◽  
Public Health Officials ◽  
The Right

AbstractEffective and timely disease surveillance systems have the potential to help public health officials design interventions to mitigate the effects of disease outbreaks. Currently, healthcare-based disease monitoring systems in France offer influenza activity information that lags real-time by 1 to 3 weeks. This temporal data gap introduces uncertainty that prevents public health officials from having a timely perspective on the population-level disease activity. Here, we present a machine-learning modeling approach that produces real-time estimates and short-term forecasts of influenza activity for the 12 continental regions of France by leveraging multiple disparate data sources that include, Google search activity, real-time and local weather information, flu-related Twitter micro-blogs, electronic health records data, and historical disease activity synchronicities across regions. Our results show that all data sources contribute to improving influenza surveillance and that machine-learning ensembles that combine all data sources lead to accurate and timely predictions.Author summaryThe role of public health is to protect the health of populations by providing the right intervention to the right population at the right time. In France and all around the world, Influenza is a major public health problem. Traditional surveillance systems produce estimates of influenza-like illness (ILI) incidence rates, but with one-to three-week delay. Accurate real-time monitoring systems of influenza outbreaks could be useful for public health decisions. By combining different data sources and different statistical models, we propose an accurate and timely forecasting platform to track the flu in France at a spatial resolution that, to our knowledge, has not been explored before.

scholarly journals Influenza forecasting for French regions combining EHR, web and climatic data sources with a machine learning ensemble approach

PLoS ONE ◽  
2021 ◽  
Vol 16 (5) ◽  
pp. e0250890
Author(s):  
Canelle Poirier ◽  
Yulin Hswen ◽  
Guillaume Bouzillé ◽  
Marc Cuggia ◽  
Audrey Lavenu ◽  
...  
Keyword(s):  
Public Health ◽  
Machine Learning ◽  
Disease Activity ◽  
Real Time ◽  
Data Sources ◽  
Influenza Surveillance ◽  
Climatic Data ◽  
Influenza Activity ◽  
Public Health Officials ◽  
Google Search

Effective and timely disease surveillance systems have the potential to help public health officials design interventions to mitigate the effects of disease outbreaks. Currently, healthcare-based disease monitoring systems in France offer influenza activity information that lags real-time by one to three weeks. This temporal data gap introduces uncertainty that prevents public health officials from having a timely perspective on the population-level disease activity. Here, we present a machine-learning modeling approach that produces real-time estimates and short-term forecasts of influenza activity for the twelve continental regions of France by leveraging multiple disparate data sources that include, Google search activity, real-time and local weather information, flu-related Twitter micro-blogs, electronic health records data, and historical disease activity synchronicities across regions. Our results show that all data sources contribute to improving influenza surveillance and that machine-learning ensembles that combine all data sources lead to accurate and timely predictions.

scholarly journals Machine Learning Solutions for Bridge Scour Forecast Based on Monitoring Data

2021 ◽  
pp. 036119812110126
Author(s):  
Negin Yousefpour ◽  
Steve Downie ◽  
Steve Walker ◽  
Nathan Perkins ◽  
Hristo Dikanski
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Monitoring Data ◽  
Sensor Data ◽  
Monitoring Systems ◽  
Bridge Scour ◽  
Scour Monitoring ◽  
Novel Approach ◽  
Bayesian Inference Method ◽  
Water Level Variations

Bridge scour is a challenge throughout the U.S.A. and other countries. Despite the scale of the issue, there is still a substantial lack of robust methods for scour prediction to support reliable, risk-based management and decision making. Throughout the past decade, the use of real-time scour monitoring systems has gained increasing interest among state departments of transportation across the U.S.A. This paper introduces three distinct methodologies for scour prediction using advanced artificial intelligence (AI)/machine learning (ML) techniques based on real-time scour monitoring data. Scour monitoring data included the riverbed and river stage elevation time series at bridge piers gathered from various sources. Deep learning algorithms showed promising in prediction of bed elevation and water level variations as early as a week in advance. Ensemble neural networks proved successful in the predicting the maximum upcoming scour depth, using the observed sensor data at the onset of a scour episode, and based on bridge pier, flow and riverbed characteristics. In addition, two of the common empirical scour models were calibrated based on the observed sensor data using the Bayesian inference method, showing significant improvement in prediction accuracy. Overall, this paper introduces a novel approach for scour risk management by integrating emerging AI/ML algorithms with real-time monitoring systems for early scour forecast.

EdgeDIPN

2020 ◽  
Vol 14 (3) ◽  
pp. 320-328
Author(s):  
Long Guo ◽  
Lifeng Hua ◽  
Rongfei Jia ◽  
Fei Fang ◽  
Binqiang Zhao ◽  
...  
Keyword(s):  
Real Time ◽  
Large Scale ◽  
Attention Mechanism ◽  
Data Sources ◽  
User Intent ◽  
Multiple User ◽  
Shopping Experience ◽  
Data Source ◽  
Intent Prediction ◽  
The Right

With the rapid growth of e-commerce in recent years, e-commerce platforms are becoming a primary place for people to find, compare and ultimately purchase products. To improve online shopping experience for consumers and increase sales for sellers, it is important to understand user intent accurately and be notified of its change timely. In this way, the right information could be offered to the right person at the right time. To achieve this goal, we propose a unified deep intent prediction network, named EdgeDIPN, which is deployed at the edge, i.e., mobile device, and able to monitor multiple user intent with different granularity simultaneously in real-time. We propose to train EdgeDIPN with multi-task learning, by which EdgeDIPN can share representations between different tasks for better performance and saving edge resources in the meantime. In particular, we propose a novel task-specific attention mechanism which enables different tasks to pick out the most relevant features from different data sources. To extract the shared representations more effectively, we utilize two kinds of attention mechanisms, where the multi-level attention mechanism tries to identify the important actions within each data source and the inter-view attention mechanism learns the interactions between different data sources. In the experiments conducted on a large-scale industrial dataset, EdgeDIPN significantly outperforms the baseline solutions. Moreover, EdgeDIPN has been deployed in the operational system of Alibaba. Online A/B testing results in several business scenarios reveal the potential of monitoring user intent in real-time. To the best of our knowledge, EdgeDIPN is the first full-fledged real-time user intent understanding center deployed at the edge and serving hundreds of millions of users in a large-scale e-commerce platform.

scholarly journals The U.S. COVID-19 Trends and Impact Survey, 2020-2021: Continuous real-time measurement of COVID-19 symptoms, risks, protective behaviors, testing and vaccination

2021 ◽  
Author(s):  
Joshua A Salomon ◽  
Alex Reinhart ◽  
Alyssa Bilinski ◽  
Eu Jing Chua ◽  
Wichida La Motte-Kerr ◽  
...  
Keyword(s):  
Public Health ◽  
Real Time ◽  
Large Scale ◽  
Survey Methods ◽  
Health Priorities ◽  
Surveillance Systems ◽  
Cross Sectional ◽  
Essential Information ◽  
Public Health Reporting ◽  
The U.S

The U.S. COVID-19 Trends and Impact Survey (CTIS) is a large, cross-sectional, Internet-based survey that has operated continuously since April 6, 2020. By inviting a random sample of Facebook active users each day, CTIS collects information about COVID-19 symptoms, risks, mitigating behaviors, mental health, testing, vaccination, and other key priorities. The large scale of the survey -- over 20 million responses in its first year of operation -- allows tracking of trends over short timescales and allows comparisons at fine demographic and geographic detail. The survey has been repeatedly revised to respond to emerging public health priorities. In this paper, we describe the survey methods and content and give examples of CTIS results that illuminate key patterns and trends and help answer high-priority policy questions relevant to the COVID-19 epidemic and response. These results demonstrate how large online surveys can provide continuous, real-time indicators of important outcomes that are not subject to public health reporting delays and backlogs. The CTIS offers high value as a supplement to official reporting data by supplying essential information about behaviors, attitudes toward policy and preventive measures, economic impacts, and other topics not reported in public health surveillance systems.

scholarly journals The US COVID-19 Trends and Impact Survey: Continuous real-time measurement of COVID-19 symptoms, risks, protective behaviors, testing, and vaccination

2021 ◽  
Vol 118 (51) ◽  
pp. e2111454118 ◽  
Author(s):  
Joshua A. Salomon ◽  
Alex Reinhart ◽  
Alyssa Bilinski ◽  
Eu Jing Chua ◽  
Wichada La Motte-Kerr ◽  
...  
Keyword(s):  
Public Health ◽  
Real Time ◽  
Large Scale ◽  
Survey Methods ◽  
Health Priorities ◽  
Surveillance Systems ◽  
Cross Sectional ◽  
Essential Information ◽  
Public Health Reporting ◽  
The Us

The US COVID-19 Trends and Impact Survey (CTIS) is a large, cross-sectional, internet-based survey that has operated continuously since April 6, 2020. By inviting a random sample of Facebook active users each day, CTIS collects information about COVID-19 symptoms, risks, mitigating behaviors, mental health, testing, vaccination, and other key priorities. The large scale of the survey—over 20 million responses in its first year of operation—allows tracking of trends over short timescales and allows comparisons at fine demographic and geographic detail. The survey has been repeatedly revised to respond to emerging public health priorities. In this paper, we describe the survey methods and content and give examples of CTIS results that illuminate key patterns and trends and help answer high-priority policy questions relevant to the COVID-19 epidemic and response. These results demonstrate how large online surveys can provide continuous, real-time indicators of important outcomes that are not subject to public health reporting delays and backlogs. The CTIS offers high value as a supplement to official reporting data by supplying essential information about behaviors, attitudes toward policy and preventive measures, economic impacts, and other topics not reported in public health surveillance systems.

scholarly journals Public health delivery in the information age: the role of informatics and technology

2019 ◽  
Vol 139 (5) ◽  
pp. 236-254 ◽  
Author(s):  
F Williams ◽  
A Oke ◽  
I Zachary
Keyword(s):  
Public Health ◽  
Information Technology ◽  
Real Time ◽  
Health Informatics ◽  
Education And Training ◽  
Surveillance Systems ◽  
Public Health Informatics ◽  
Electronic Surveillance ◽  
Health Delivery ◽  
And Training

Aim: Public health systems have embraced health informatics and information technology as a potential transformational tool to improve real-time surveillance systems, communication, and sharing of information among various agencies. Global pandemic outbreaks like Zika and Ebola were quickly controlled due to electronic surveillance systems enabling efficient information access and exchange. However, there is the need for a more robust technology to enhance adequate epidemic forecasting, data sharing, and effective communication. The purpose of this review was to examine the use of informatics and information technology tools and its impact on public health delivery. Method: Investigators searched six electronic databases. These were MEDLINE, Cumulative Index to Nursing and Allied Health Literature (CINAHL) Complete, Cochrane Database of Systematic Reviews, COMPENDEX, Scopus, and Academic Search Premier from January 2000 to 31 March 2016. Results: A total of 60 articles met the eligibility criteria for inclusion. These studies were organized into three areas as (1) definition of the term public health informatics; (2) type of public health surveillance systems and implications for public health; and (3) electronic surveillance systems functionality, capability, training, and challenges. Our analysis revealed that due to the growing expectations to provide real-time response and population-centered evidence-based public health in this information-driven age there has been a surge in informatics and information technology adoption. Education and training programs are now available to equip public health students and professionals with skills in public health informatics. However, obstacles including interoperability, data standardization, privacy, and technology transfer persist. Conclusion: Re-engineering the delivery of public health is necessary to meet the demands of the 21st century and beyond. To meet this expectation, public health must invest in workforce development and capacity through education and training in informatics.

Predictive Modeling of Vaccination Uptake in U.S. Counties: A Machine Learning-based Approach (Preprint)

2021 ◽  
Author(s):  
Queena Cheong ◽  
Martin Au-yeung ◽  
Stephanie Quon ◽  
Katsy Concepcion ◽  
Jude Dzevela Kong
Keyword(s):  
Public Health ◽  
Machine Learning ◽  
Predictive Modelling ◽  
Sociodemographic Factors ◽  
Vaccine Uptake ◽  
Vaccination Rates ◽  
Vaccination Uptake ◽  
Sociodemographic Data ◽  
Public Health Officials ◽  
The U.S

BACKGROUND While the COVID-19 pandemic has left an unprecedented impact globally, countries such as the United States of America have reported the most significant incidence of COVID-19 cases worldwide. Within the U.S., various sociodemographic factors have played an essential role in the creation of regional disparities. Regional disparities have resulted in the unequal spread of disease between U.S. counties, underscoring the need for efficient and accurate predictive modelling strategies to inform public health officials and reduce the burden on healthcare systems. Furthermore, despite the widespread accessibility of COVID-19 vaccines across the U.S., vaccination rates have become stagnant, necessitating predictive modelling to identify important factors impacting vaccination uptake. OBJECTIVE To determine the association between sociodemographic factors and vaccine uptake across counties in the U.S. METHODS Sociodemographic data on fully vaccinated and unvaccinated individuals were sourced from several online databases, such as the U.S. Centre for Disease Control and U.S. Census Bureau COVID-19 Site. Machine learning analysis was performed using XGBoost and sociodemographic data. RESULTS Our model predicted COVID-19 vaccination uptake across U.S. countries with 59% accuracy. In addition, it identified location, education, ethnicity, and income as the most critical sociodemographic features in predicting vaccination uptake in U.S. counties. Lastly, the model produced a choropleth demonstrating areas of low and high vaccination rates, which can be used by healthcare authorities in future pandemics to visualize and prioritize areas of low vaccination and design targeted vaccination campaigns. CONCLUSIONS Our study reveals that sociodemographic characteristics are predictors of vaccine uptake rate across counties in the U.S. and if leveraged appropriately can assist policy makers and public health officials to understand vaccine uptake rates and craft policies to improve them.

Cooperation, Conflict, and Reaction

2001 ◽  
Author(s):  
John T. Cumbler
Keyword(s):  
Public Health ◽  
New England ◽  
The Other ◽  
Potential Conflict ◽  
Stream Pollution ◽  
The Public ◽  
Public Health Officials ◽  
The One ◽  
The Right

When James Olcott spoke before Connecticut farmers for “anti-stream pollution,” he urged the public to mobilize to stop water pollution by “ignorant or reckless capitalists.” In identifying the “ignorant and reckless capitalists,” Olcott focused the attention of the farmers on industrial waste and the role of manufacturers in their search for profits in causing pollution. Although manufacturers and the courts argued that industrialization brought wealth and prosperity to New England and hence was a general good, Olcott challenged this idea. He saw the issue as a conflict between industrialization and its costs on the one hand and the public good on the other. Concern over industrial pollution and the potential conflict between it and public health had already arisen in Massachusetts. Although the Massachusetts State Board of Health realized that the interests of the “capitalists” and those of the public health officials might be in conflict, in 1872 it hoped that with improved knowledge, “a way will be eventually found to joining them into harmonious relations,” much as Lyman believed science and technology would resolve the conflict between fishers and mill owners. The board's interest in “harmonious relations” also reflected a realization that at least for the last several years, the courts had seen pollution as an inevitable consequence of civilization and had been favorable toward industrialists, especially if no obvious alternative to dumping pollution existed. In 1866, William Merrifield sued Nathan Lombard because Lombard had dumped “Vitriol and other noxious substances” into the stream above Merrifield's factory, “corrupting” the water so badly that it destroyed his boiler. Chief Justice Bigelow ruled that Lombard had invaded Merrifield's rights. “Each riparian owner,” the judge wrote, “has the right to use the water for any reasonable and proper purpose. . . . An injury to the purity or quality of the water to the detriment of the other riparian owners, constitutes in legal effect, a wrong.” In 1872, Merrifield again went to court, claiming the City of Worcester regularly dumped sewage into Mill Brook, by which the waters became greatly corrupted and unfit to use.”

scholarly journals The pandemic of social media panic travels faster than the COVID-19 outbreak

2020 ◽  
Vol 27 (3) ◽  
Author(s):  
Anneliese Depoux ◽  
Sam Martin ◽  
Emilie Karafillakis ◽  
Raman Preet ◽  
Annelies Wilder-Smith ◽  
...  
Keyword(s):  
Public Health ◽  
Social Media ◽  
Real Time ◽  
Control Measures ◽  
Public Health Officials ◽  
The Creation ◽  
And Control

We need to rapidly detect and respond to public rumours, perceptions, attitudes and behaviours around COVID-19 and control measures. The creation of an interactive platform and dashboard to provide real-time alerts of rumours and concerns about coronavirus spreading globally would enable public health officials and relevant stakeholders to respond rapidly with a proactive and engaging narrative that can mitigate misinformation.

scholarly journals Web Search Engine Misinformation Notifier Extension (SEMiNExt): A Machine Learning Based Approach during COVID-19 Pandemic

Healthcare ◽  
2021 ◽  
Vol 9 (2) ◽  
pp. 156
Author(s):  
Abdullah Bin Shams ◽  
Ehsanul Hoque Apu ◽  
Ashiqur Rahman ◽  
Md. Mohsin Sarker Raihan ◽  
Nazeeba Siddika ◽  
...  
Keyword(s):  
Public Health ◽  
Machine Learning ◽  
Real Time ◽  
Search Engine ◽  
Language Processing ◽  
Web Search ◽  
Training Data ◽  
Small Data ◽  
Web Search Engine ◽  
User Query

Misinformation such as on coronavirus disease 2019 (COVID-19) drugs, vaccination or presentation of its treatment from untrusted sources have shown dramatic consequences on public health. Authorities have deployed several surveillance tools to detect and slow down the rapid misinformation spread online. Large quantities of unverified information are available online and at present there is no real-time tool available to alert a user about false information during online health inquiries over a web search engine. To bridge this gap, we propose a web search engine misinformation notifier extension (SEMiNExt). Natural language processing (NLP) and machine learning algorithm have been successfully integrated into the extension. This enables SEMiNExt to read the user query from the search bar, classify the veracity of the query and notify the authenticity of the query to the user, all in real-time to prevent the spread of misinformation. Our results show that SEMiNExt under artificial neural network (ANN) works best with an accuracy of 93%, F1-score of 92%, precision of 92% and a recall of 93% when 80% of the data is trained. Moreover, ANN is able to predict with a very high accuracy even for a small training data size. This is very important for an early detection of new misinformation from a small data sample available online that can significantly reduce the spread of misinformation and maximize public health safety. The SEMiNExt approach has introduced the possibility to improve online health management system by showing misinformation notifications in real-time, enabling safer web-based searching on health-related issues.

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