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 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 Impact of Public Health Interventions on Seasonal Influenza Activity During the COVID-19 Outbreak in Korea

2020 ◽  
Author(s):  
Hyunju Lee ◽  
Heeyoung Lee ◽  
Kyoung-Ho Song ◽  
Eu Suk Kim ◽  
Jeong Su Park ◽  
...  
Keyword(s):  
Public Health ◽  
Seasonal Influenza ◽  
Health Interventions ◽  
Influenza Surveillance ◽  
Influenza B ◽  
Public Health Interventions ◽  
National Response ◽  
Influenza Activity ◽  
High Level ◽  
The Impact

Abstract Background Coronavirus disease 2019 (COVID-19) was introduced in Korea early with a large outbreak in mid-February. We reviewed the public health interventions used during the COVID-19 outbreak and describe the impact on seasonal influenza activity in Korea. Methods National response strategies, public health interventions and daily COVID-19–confirmed cases in Korea were reviewed during the pandemic. National influenza surveillance data were compared between 7 sequential seasons. Characteristics of each season, including rate of influenza-like illness (ILI), duration of epidemic, date of termination of epidemic, distribution of influenza virus strain, and hospitalization, were analyzed. Results After various public health interventions including enforced public education on hand hygiene, cough etiquette, staying at home with respiratory symptoms, universal mask use in public places, refrain from nonessential social activities, and school closures the duration of the influenza epidemic in 2019/2020 decreased by 6–12 weeks and the influenza activity peak rated 49.8 ILIs/1000 visits compared to 71.9–86.2 ILIs/1000 visits in previous seasons. During the period of enforced social distancing from weeks 9–17 of 2020, influenza hospitalization cases were 11.9–26.9-fold lower compared with previous seasons. During the 2019/2020 season, influenza B accounted for only 4%, in contrast to previous seasons in which influenza B accounted for 26.6–54.9% of all cases. Conclusions Efforts to activate a high-level national response not only led to a decrease in COVID-19 but also a substantial decrease in seasonal influenza activity. Interventions applied to control COVID-19 may serve as useful strategies for prevention and control of influenza in upcoming seasons.

scholarly journals Real-time Laboratory-Based Influenza Surveillance with Xpert Flu

2015 ◽  
Vol 7 (1) ◽  
Author(s):  
Andrea Dugas ◽  
Howard Burkom ◽  
Richard Rothman
Keyword(s):  
Public Health ◽  
Real Time ◽  
Surveillance System ◽  
Influenza Surveillance ◽  
Nasopharyngeal Swab ◽  
National Surveillance ◽  
Test Results ◽  
Site Type ◽  
Rapid Pcr ◽  
Surveillance And Response

In order to provide real-time access to influenza test results, we created a laboratory-based surveillance system which automatically uploaded influenza test results from a rapid PCR-based influenza test, Xpert Flu, and the associated testing times and locations. On-site, type-specific results were available to physicians and uploaded for public health awareness within 100 minutes of patient nasopharyngeal swab. Expansion of this real-time capability to sentinel facilities could improve both local and national surveillance and response, reducing the need for syndromic influenza surveillance.

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.

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.

Enhancing COVID-19 Epidemics Forecasting Accuracy by Combining Real-time and Historical Data from Social Media, Online News Articles, and Search Queries (Preprint)

2021 ◽  
Author(s):  
Jingwei Li ◽  
Wei Huang ◽  
Choon Ling Sia ◽  
Zhuo Chen ◽  
Tailai Wu ◽  
...  
Keyword(s):  
Public Health ◽  
Real Time ◽  
Historical Data ◽  
Mainland China ◽  
Hubei Province ◽  
Online News ◽  
Data Sources ◽  
Resources Allocation ◽  
Forecasting Accuracy ◽  
Proposed Model

BACKGROUND The SARS-COV-2 virus and its variants are posing extraordinary challenges for public health worldwide. More timely and accurate forecasting of COVID-19 epidemics is the key to maintaining timely interventions and policies and efficient resources allocation. Internet-based data sources have shown great potential to supplement traditional infectious disease surveillance, and the combination of different Internet-based data sources has shown greater power to enhance epidemic forecasting accuracy than using a single Internet-based data source. However, existing methods incorporating multiple Internet-based data sources only used real-time data from these sources as exogenous inputs, but didn’t take all the historical data into account. Moreover, the predictive power of different Internet-based data sources in providing early warning for COVID-19 outbreaks has not been fully explored. OBJECTIVE The main aim of our study is to explore whether combining real-time and historical data from multiple Internet-based sources could improve the COVID-19 forecasting accuracy over the existing baseline models. A secondary aim is to explore the COVID-19 forecasting timeliness based on different Internet-based data sources. METHODS We first used core terms and symptoms related keywords-based methods to extract COVID-19 related Internet-based data from December 21, 2019, to February 29, 2020. The Internet-based data we explored included 90,493,912 online news articles, 37,401,900 microblogs, and all the Baidu search query data during that period. We then proposed an autoregressive model with exogenous inputs, incorporating the real-time and historical data from multiple Internet-based sources. Our proposed model was compared with baseline models, and all the models were tested during the first wave of COVID-19 epidemics in Hubei province and the rest of mainland China separately. We also used the lagged Pearson correlations for the COVID-19 forecasting timeliness analysis. RESULTS Our proposed model achieved the highest accuracy in all the five accuracy measures, compared with all the baseline models in both Hubei province and the rest of mainland China. In mainland China except Hubei, the COVID-19 epidemics forecasting accuracy differences between our proposed model (model i) and all the other baseline models were statistically significant (model 1, t=–8.722, P<.001; model 2, t=–5.000, P<.001, model 3, t=–1.882, P =0.063, model 4, t=–4.644, P<.001; model 5, t=–4.488, P<.001). In Hubei province, our proposed model's forecasting accuracy improved significantly compared with the baseline model using historical COVID-19 new confirmed case counts only (model 1, t=–1.732, P=0.086). Our results also showed that Internet-based sources could provide a 2-6 days earlier warning for COVID-19 outbreaks. CONCLUSIONS Our approach incorporating real-time and historical data from multiple Internet-based sources could improve forecasting accuracy for COVID-19 epidemics and its variants, which may help improve public health agencies' interventions and resources allocation in mitigating and controlling new waves of COVID-19 or other epidemics.

scholarly journals Use Internet search data to accurately track state level influenza epidemics

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Shihao Yang ◽  
Shaoyang Ning ◽  
S. C. Kou
Keyword(s):  
Real Time ◽  
State Level ◽  
The United States ◽  
Search Pattern ◽  
Influenza Surveillance ◽  
Internet Search ◽  
Spatial Correlation Structure ◽  
Control And Prevention ◽  
Influenza Activity ◽  
Search Data

AbstractFor 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.

scholarly journals A regional approach for the influenza surveillance in France

2017 ◽  
Vol 9 (1) ◽  
Author(s):  
Marc Ruello ◽  
Camille Pelat ◽  
Céline Caserio-Schönemann ◽  
Anne Fouillet ◽  
Isabelle Bonmarin ◽  
...  
Keyword(s):  
Public Health ◽  
Health Care ◽  
National Level ◽  
Health Agency ◽  
Data Sources ◽  
Outbreak Detection ◽  
Detection Methods ◽  
Influenza Surveillance ◽  
Time Step ◽  
Statistical Results

ObjectiveTo describe the results of the new organization of influenzasurveillance in France, based on a regional approach.IntroductionIn France, until winter 2014-2015, management and preventiveactions for the control of the flu epidemic were implemented whenthe national incidence of influenza-like illness (ILI) consultationsin general practice was over an epidemic threshold. The 2014-2015influenza epidemic had a major public health impact, particularly inthe elderly, and caused a severe overloading of the health care system,in particular emergency departments (ED) [1]. The epidemic alertemitted by the French National Public Health Agency at the nationallevel was too late for the hospitals to prepare themselves in manyregions.After a national feedback organized in April 2015 with allpartners involved in influenza surveillance and management, it wasrecommended to improve influenza surveillance in France following3 axes: 1) regionalize surveillance so that healthcare structures canadapt to the particular situation of their region; 2) use a pre-epidemicalert level for better anticipating the outbreak; 3) use multiple datasources and multiple outbreak detection methods to strengthen thedetermination of influenza alert level.MethodsA user-friendly web application was developed to provide commondata visualizations and statistical results of outbreak detectionmethods to all the epidemiologists involved in influenza surveillanceat the national level or in the 15 regional units of our agency [2].It relies on 3 data sources, aggregated on a weekly time step: 1) theproportion of ILI among all coded attendances in the ED participatingto the OSCOUR Network [3] ; 2) the proportion of ILI among allcoded visits made by emergency general practitioners (GPs) workingin the SOS Médecins associations [3]; 3) the incidence rate of ILIestimated from a sample of sentinel GPs [4].For each region each week, 3 statistical outbreak detection methodswere applied to the 3 data sources, generating 9 results that werecombined to obtain a weekly regional influenza alarm level. Basedon this alarm level and on other information (e.g.virological data),the epidemiologists then determined the epidemiological status ofeach region as either 1) epidemic-free, 2) in pre/post epidemic or 3)epidemic.The R software was used for programming algorithms and buildingthe web interface (package shiny).ResultsThe epidemiological status of influenza at the regional level wascommunicated through maps published in the weekly influenzareports of the Agency throughout the surveillance season [5].In week 2016-W03, Brittany was the first French region to declarethe influenza epidemic, with nine other regions in pre-epidemic alert.The epidemic then spread over the whole mainland territory. The peakof the epidemic was declared in week 11, the end in week 16.ConclusionsThis regional multi-source approach has been made possible bythe sharing of data visualizations and statistical results through a webapplication. This application helped detecting early the epidemicstart and allowed a reactive communication with the regionalhealth authorities in charge of the organization of health care, themanagement and the setting up of the appropriate preventivemeasures.

scholarly journals A machine learning aided global diagnostic and comparative tool to assess effect of quarantine control in Covid-19 spread

2020 ◽  
Author(s):  
Raj Dandekar ◽  
Chris Rackauckas ◽  
George Barbastathis
Keyword(s):  
Public Health ◽  
Neural Network ◽  
Machine Learning ◽  
Decision Making ◽  
Machine Learning Algorithms ◽  
Informed Decision Making ◽  
Network Module ◽  
Public Health Officials ◽  
America South

We have developed a globally applicable diagnostic Covid-19 model by augmenting the classical SIR epidemiological model with a neural network module. Our model does not rely upon previous epidemics like SARS/MERS and all parameters are optimized via machine learning algorithms employed on publicly available Covid-19 data. The model decomposes the contributions to the infection timeseries to analyze and compare the role of quarantine control policies employed in highly affected regions of Europe, North America, South America and Asia in controlling the spread of the virus. For all continents considered, our results show a generally strong correlation between strengthening of the quarantine controls as learnt by the model and actions taken by the regions' respective governments. Finally, we have hosted our quarantine diagnosis results for the top $70$ affected countries worldwide, on a public platform, which can be used for informed decision making by public health officials and researchers alike.

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