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Ning S, Hussain A, Wang Q. Incorporating connectivity among Internet search data for enhanced influenza-like illness tracking. PLoS One 2024; 19:e0305579. [PMID: 39186560 PMCID: PMC11346739 DOI: 10.1371/journal.pone.0305579] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2024] [Accepted: 06/02/2024] [Indexed: 08/28/2024] Open
Abstract
Big data collected from the Internet possess great potential to reveal the ever-changing trends in society. In particular, accurate infectious disease tracking with Internet data has grown in popularity, providing invaluable information for public health decision makers and the general public. However, much of the complex connectivity among the Internet search data is not effectively addressed among existing disease tracking frameworks. To this end, we propose ARGO-C (Augmented Regression with Clustered GOogle data), an integrative, statistically principled approach that incorporates the clustering structure of Internet search data to enhance the accuracy and interpretability of disease tracking. Focusing on multi-resolution %ILI (influenza-like illness) tracking, we demonstrate the improved performance and robustness of ARGO-C over benchmark methods at various geographical resolutions. We also highlight the adaptability of ARGO-C to track various diseases in addition to influenza, and to track other social or economic trends.
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Affiliation(s)
- Shaoyang Ning
- Department of Mathematics and Statistics, Williams College, Williamstown, MA, United States of America
| | - Ahmed Hussain
- Department of Mathematics and Statistics, Williams College, Williamstown, MA, United States of America
| | - Qing Wang
- Department of Mathematics, Wellesley College, Wellesley, MA, United States of America
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Osborne MT, Kenah E, Lancaster K, Tien J. Catch the tweet to fight the flu: Using Twitter to promote flu shots on a college campus. JOURNAL OF AMERICAN COLLEGE HEALTH : J OF ACH 2023; 71:2470-2484. [PMID: 34519614 DOI: 10.1080/07448481.2021.1973480] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/02/2020] [Revised: 06/18/2021] [Accepted: 08/16/2021] [Indexed: 06/13/2023]
Abstract
Objective: Over the 2018-2019 flu season we conducted a randomized controlled trial examining the efficacy of a Twitter campaign on vaccination rates. Concurrently we investigated potential interactions between digital social network structure and vaccination status. Participants: Undergratuates at a large midwestern public university were randomly assigned to an intervention (n = 353) or control (n = 349) group. Methods: Vaccination data were collected via monthly surveys. Participant Twitter data were collected through the public-facing Twitter API. Intervention impact was assessed with logistic regression. Standard network science tools examined vaccination coverage over online social networks. Results: The campaign had no effect on vaccination outcome. Receiving a flu shot the prior year had a positive impact on participant vaccination. Evidence of an interaction between digital social network structure and vaccination status was detected. Conclusions: Social media campaigns may not be sufficient for increasing vaccination rates. There may be potential for social media campaigns that leverage network structure.
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Affiliation(s)
- Matthew T Osborne
- Department of Mathematics, The Ohio State University, Columbus, Ohio, USA
| | - Eben Kenah
- College of Public Health Department of Biostatistics, The Ohio State University, Columbus, Ohio, USA
| | - Kathryn Lancaster
- College of Public Health, Department of Epidemiology, The Ohio State University, Columbus, Ohio, USA
| | - Joseph Tien
- Department of Mathematics, The Ohio State University, Columbus, Ohio, USA
- College of Public Health, Department of Epidemiology, The Ohio State University, Columbus, Ohio, USA
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3
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Yang F, Servadio JL, Thanh NTL, Lam HM, Choisy M, Thai PQ, Thao TTN, Vy NHT, Phuong HT, Nguyen TD, Tam DTH, Hanks EM, Vinh H, Bjornstad ON, Chau NVV, Boni MF. A combination of annual and nonannual forces drive respiratory disease in the tropics. BMJ Glob Health 2023; 8:e013054. [PMID: 37935520 PMCID: PMC10632872 DOI: 10.1136/bmjgh-2023-013054] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2023] [Accepted: 10/08/2023] [Indexed: 11/09/2023] Open
Abstract
INTRODUCTION It is well known that influenza and other respiratory viruses are wintertime-seasonal in temperate regions. However, respiratory disease seasonality in the tropics is less well understood. In this study, we aimed to characterise the seasonality of influenza-like illness (ILI) and influenza virus in Ho Chi Minh City, Vietnam. METHODS We monitored the daily number of ILI patients in 89 outpatient clinics from January 2010 to December 2019. We collected nasal swabs and tested for influenza from a subset of clinics from May 2012 to December 2019. We used spectral analysis to describe the periodic signals in the system. We evaluated the contribution of these periodic signals to predicting ILI and influenza patterns through lognormal and gamma hurdle models. RESULTS During 10 years of community surveillance, 66 799 ILI reports were collected covering 2.9 million patient visits; 2604 nasal swabs were collected, 559 of which were PCR-positive for influenza virus. Both annual and nonannual cycles were detected in the ILI time series, with the annual cycle showing 8.9% lower ILI activity (95% CI 8.8% to 9.0%) from February 24 to May 15. Nonannual cycles had substantial explanatory power for ILI trends (ΔAIC=183) compared with all annual covariates (ΔAIC=263) in lognormal regression. Near-annual signals were observed for PCR-confirmed influenza but were not consistent over time or across influenza (sub)types. The explanatory power of climate factors for ILI and influenza virus trends was weak. CONCLUSION Our study reveals a unique pattern of respiratory disease dynamics in a tropical setting influenced by both annual and nonannual drivers, with influenza dynamics showing near-annual periodicities. Timing of vaccination campaigns and hospital capacity planning may require a complex forecasting approach.
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Affiliation(s)
- Fuhan Yang
- Department of Biology and Center for Infectious Disease Dynamics, The Pennsylvania State University, University Park, Pennsylvania, USA
| | - Joseph L Servadio
- Department of Biology and Center for Infectious Disease Dynamics, The Pennsylvania State University, University Park, Pennsylvania, USA
| | - Nguyen Thi Le Thanh
- Wellcome Trust Major Overseas Programme, Oxford University Clinical Research Unit, Ho Chi Minh City, Vietnam
| | - Ha Minh Lam
- Wellcome Trust Major Overseas Programme, Oxford University Clinical Research Unit, Ho Chi Minh City, Vietnam
| | - Marc Choisy
- Wellcome Trust Major Overseas Programme, Oxford University Clinical Research Unit, Ho Chi Minh City, Vietnam
- Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Pham Quang Thai
- National Institute of Hygiene and Epidemiology, Hanoi, Vietnam
| | - Tran Thi Nhu Thao
- Wellcome Trust Major Overseas Programme, Oxford University Clinical Research Unit, Ho Chi Minh City, Vietnam
- Department of Microbiology, Blavatnik Institute, Harvard Medical School, Boston, Massachusetts, USA
| | - Nguyen Ha Thao Vy
- Wellcome Trust Major Overseas Programme, Oxford University Clinical Research Unit, Ho Chi Minh City, Vietnam
| | - Huynh Thi Phuong
- Wellcome Trust Major Overseas Programme, Oxford University Clinical Research Unit, Ho Chi Minh City, Vietnam
| | - Tran Dang Nguyen
- Department of Biology and Center for Infectious Disease Dynamics, The Pennsylvania State University, University Park, Pennsylvania, USA
- Wellcome Trust Major Overseas Programme, Oxford University Clinical Research Unit, Ho Chi Minh City, Vietnam
| | - Dong Thi Hoai Tam
- Wellcome Trust Major Overseas Programme, Oxford University Clinical Research Unit, Ho Chi Minh City, Vietnam
| | - Ephraim M Hanks
- Department of Statistics and Center for Infectious Disease Dynamics, The Pennsylvania State University, University Park, Pennsylvania, USA
| | - Ha Vinh
- Wellcome Trust Major Overseas Programme, Oxford University Clinical Research Unit, Ho Chi Minh City, Vietnam
- Hospital for Tropical Diseases, Ho Chi Minh City, Vietnam
| | - Ottar N Bjornstad
- Department of Biology and Center for Infectious Disease Dynamics, The Pennsylvania State University, University Park, Pennsylvania, USA
| | - Nguyen Van Vinh Chau
- Wellcome Trust Major Overseas Programme, Oxford University Clinical Research Unit, Ho Chi Minh City, Vietnam
- Hospital for Tropical Diseases, Ho Chi Minh City, Vietnam
| | - Maciej F Boni
- Department of Biology and Center for Infectious Disease Dynamics, The Pennsylvania State University, University Park, Pennsylvania, USA
- Wellcome Trust Major Overseas Programme, Oxford University Clinical Research Unit, Ho Chi Minh City, Vietnam
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Yang L, Zhang T, Han X, Yang J, Sun Y, Ma L, Chen J, Li Y, Lai S, Li W, Feng L, Yang W. Influenza Epidemic Trend Surveillance and Prediction Based on Search Engine Data: Deep Learning Model Study. J Med Internet Res 2023; 25:e45085. [PMID: 37847532 PMCID: PMC10618884 DOI: 10.2196/45085] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2022] [Revised: 07/24/2023] [Accepted: 08/04/2023] [Indexed: 10/18/2023] Open
Abstract
BACKGROUND Influenza outbreaks pose a significant threat to global public health. Traditional surveillance systems and simple algorithms often struggle to predict influenza outbreaks in an accurate and timely manner. Big data and modern technology have offered new modalities for disease surveillance and prediction. Influenza-like illness can serve as a valuable surveillance tool for emerging respiratory infectious diseases like influenza and COVID-19, especially when reported case data may not fully reflect the actual epidemic curve. OBJECTIVE This study aimed to develop a predictive model for influenza outbreaks by combining Baidu search query data with traditional virological surveillance data. The goal was to improve early detection and preparedness for influenza outbreaks in both northern and southern China, providing evidence for supplementing modern intelligence epidemic surveillance methods. METHODS We collected virological data from the National Influenza Surveillance Network and Baidu search query data from January 2011 to July 2018, totaling 3,691,865 and 1,563,361 respective samples. Relevant search terms related to influenza were identified and analyzed for their correlation with influenza-positive rates using Pearson correlation analysis. A distributed lag nonlinear model was used to assess the lag correlation of the search terms with influenza activity. Subsequently, a predictive model based on the gated recurrent unit and multiple attention mechanisms was developed to forecast the influenza-positive trend. RESULTS This study revealed a high correlation between specific Baidu search terms and influenza-positive rates in both northern and southern China, except for 1 term. The search terms were categorized into 4 groups: essential facts on influenza, influenza symptoms, influenza treatment and medicine, and influenza prevention, all of which showed correlation with the influenza-positive rate. The influenza prevention and influenza symptom groups had a lag correlation of 1.4-3.2 and 5.0-8.0 days, respectively. The Baidu search terms could help predict the influenza-positive rate 14-22 days in advance in southern China but interfered with influenza surveillance in northern China. CONCLUSIONS Complementing traditional disease surveillance systems with information from web-based data sources can aid in detecting warning signs of influenza outbreaks earlier. However, supplementation of modern surveillance with search engine information should be approached cautiously. This approach provides valuable insights for digital epidemiology and has the potential for broader application in respiratory infectious disease surveillance. Further research should explore the optimization and customization of search terms for different regions and languages to improve the accuracy of influenza prediction models.
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Affiliation(s)
- Liuyang Yang
- Department of Management Science and Information System, Faculty of Management and Economics, Kunming University of Science and Technology, Kunming, China
- School of Population Medicine and Public Health, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
| | - Ting Zhang
- School of Population Medicine and Public Health, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
| | - Xuan Han
- School of Population Medicine and Public Health, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
| | - Jiao Yang
- School of Population Medicine and Public Health, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
| | - Yanxia Sun
- School of Population Medicine and Public Health, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
| | - Libing Ma
- School of Population Medicine and Public Health, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
- Department of Respiratory and Critical Care Medicine, Affiliated Hospital of Guilin Medical University, Guilin, China
| | - Jialong Chen
- Department of Respiratory and Critical Care Medicine, Bejing Hospital, Beijing, China
| | - Yanming Li
- Department of Respiratory and Critical Care Medicine, Bejing Hospital, Beijing, China
| | - Shengjie Lai
- WorldPop, School of Geography and Environmental Science, University of Southampton, Southampton, United Kingdom
| | - Wei Li
- The First People's Hospital of Yunnan Province, Affiliated Hospital of Kunming University of Science and Technology, Kunming, China
| | - Luzhao Feng
- School of Population Medicine and Public Health, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
| | - Weizhong Yang
- School of Population Medicine and Public Health, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
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Ziakas PD, Mylonakis E. Public interest trends for Covid-19 and alignment with the disease trajectory: A time-series analysis of national-level data. PLOS DIGITAL HEALTH 2023; 2:e0000271. [PMID: 37294742 DOI: 10.1371/journal.pdig.0000271] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Accepted: 05/09/2023] [Indexed: 06/11/2023]
Abstract
Data from web search engines have become a valuable adjunct in epidemiology and public health, specifically during epidemics. We aimed to explore the concordance of web search popularity for Covid-19 across 6 Western nations (United Kingdom, United States, France, Italy, Spain and Germany) and how timeline changes align with the pandemic waves, Covid-19 mortality, and incident case trajectories. We used the Google Trends tool for web-search popularity, and "Our World in Data" on Covid-19 reported cases, deaths, and administrative responses (measured by stringency index) to analyze country-level data. The Google Trends tool provides spatiotemporal data, scaled to a range of <1 (lowest relative popularity) to 100 (highest relative popularity), for the selected search terms, timeframe, and region. We used "coronavirus" and "covid" as search terms and set the timeframe up to November 12, 2022. We obtained multiple consecutive samples using the same terms to validate against sampling bias. We consolidated national-level incident cases and deaths weekly and transformed them to a range between 0 to 100 through the min-max normalization algorithm. We calculated the concordance of relative popularity rankings between regions, using the non-parametric Kendall's W, which maps concordance between 0 (lack of agreement) to 1 (perfect match). We used a dynamic time-warping algorithm to explore the similarity between Covid-19 relative popularity, mortality, and incident case trajectories. This methodology can recognize the similarity of shapes between time-series through a distance optimization process. The peak popularity was recorded on March 2020, to be followed by a decline below 20% in the subsequent three months and a long-standing period of variation around that level. At the end of 2021, public interest spiked shortly to fade away to a low level of around 10%. This pattern was highly concordant across the six regions (Kendal's W 0.88, p< .001). In dynamic time warping analysis, national-level public interest yielded a high similarity with the Covid-19 mortality trajectory (Similarity indices range 0.60-0.79). Instead, public interest was less similar with incident cases (0.50-0.76) and stringency index trajectories (0.33-0.64). We demonstrated that public interest is better intertwined with population mortality, rather than incident case trajectory and administrative responses. As the public interest in Covid-19 gradually subsides, these observations could help predict future public interest in pandemic events.
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Affiliation(s)
- Panayiotis D Ziakas
- Department of Medicine, Houston Methodist Hospital, Houston, Texas, United States of America
| | - Eleftherios Mylonakis
- Department of Medicine, Houston Methodist Hospital, Houston, Texas, United States of America
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Leitgöb H, Prandner D, Wolbring T. Editorial: Big data and machine learning in sociology. FRONTIERS IN SOCIOLOGY 2023; 8:1173155. [PMID: 37229284 PMCID: PMC10203698 DOI: 10.3389/fsoc.2023.1173155] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/24/2023] [Accepted: 04/13/2023] [Indexed: 05/27/2023]
Affiliation(s)
- Heinz Leitgöb
- Institute of Sociology, Leipzig University, Leipzig, Germany
- Institute of Sociology, University of Frankfurt, Frankfurt, Germany
| | | | - Tobias Wolbring
- Institute of Labour Market and Socioeconomics, University of Erlangen-Nuremberg, Nuremberg, Germany
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Surveillance of communicable diseases using social media: A systematic review. PLoS One 2023; 18:e0282101. [PMID: 36827297 PMCID: PMC9956027 DOI: 10.1371/journal.pone.0282101] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2022] [Accepted: 02/07/2023] [Indexed: 02/25/2023] Open
Abstract
BACKGROUND Communicable diseases pose a severe threat to public health and economic growth. The traditional methods that are used for public health surveillance, however, involve many drawbacks, such as being labor intensive to operate and resulting in a lag between data collection and reporting. To effectively address the limitations of these traditional methods and to mitigate the adverse effects of these diseases, a proactive and real-time public health surveillance system is needed. Previous studies have indicated the usefulness of performing text mining on social media. OBJECTIVE To conduct a systematic review of the literature that used textual content published to social media for the purpose of the surveillance and prediction of communicable diseases. METHODOLOGY Broad search queries were formulated and performed in four databases. Both journal articles and conference materials were included. The quality of the studies, operationalized as reliability and validity, was assessed. This qualitative systematic review was guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. RESULTS Twenty-three publications were included in this systematic review. All studies reported positive results for using textual social media content to surveille communicable diseases. Most studies used Twitter as a source for these data. Influenza was studied most frequently, while other communicable diseases received far less attention. Journal articles had a higher quality (reliability and validity) than conference papers. However, studies often failed to provide important information about procedures and implementation. CONCLUSION Text mining of health-related content published on social media can serve as a novel and powerful tool for the automated, real-time, and remote monitoring of public health and for the surveillance and prediction of communicable diseases in particular. This tool can address limitations related to traditional surveillance methods, and it has the potential to supplement traditional methods for public health surveillance.
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Dai S, Han L. Influenza surveillance with Baidu index and attention-based long short-term memory model. PLoS One 2023; 18:e0280834. [PMID: 36689543 PMCID: PMC9870163 DOI: 10.1371/journal.pone.0280834] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2022] [Accepted: 01/10/2023] [Indexed: 01/24/2023] Open
Abstract
BACKGROUND The prediction and prevention of influenza is a public health issue of great concern, and the study of timely acquisition of influenza transmission trend has become an important research topic. For achieving more quicker and accurate detection and prediction, the data recorded on the Internet, especially on the search engine from Google or Baidu are widely introduced into this field. Moreover, with the development of intelligent technology and machine learning algorithm, many updated and advanced trend tracking and forecasting methods are also being used in this research problem. METHODS In this paper, a new recurrent neural network architecture, attention-based long short-term memory model is proposed for influenza surveillance. This is a kind of deep learning model which is trained by processing from Baidu Index series so as to fit the real influenza survey time series. Previous studies on influenza surveillance by Baidu Index mostly used traditional autoregressive moving average model or classical machine learning models such as logarithmic linear regression, support vector regression or multi-layer perception model to fit influenza like illness data, which less considered the deep learning structure. Meanwhile, some new model that considered the deep learning structure did not take into account the application of Baidu index data. This study considers introducing the recurrent neural network with long short-term memory combined with attention mechanism into the influenza surveillance research model, which not only fits the research problems well in model structure, but also provides research methods based on Baidu index. RESULTS The actual survey data and Baidu Index data are used to train and test the proposed attention-based long short-term memory model and the other comparison models, so as to iterate the value of the model parameters, and to describe and predict the influenza epidemic situation. The experimental results show that our proposed model has better performance in the mean absolute error, mean absolute percentage error, index of agreement and other indicators than the other comparison models. CONCLUSION Our proposed attention-based long short-term memory model vividly verifies the ability of this attention-based long short-term memory structure for better surveillance and prediction the trend of influenza. In comparison with some of the latest models and methods in this research field, the model we proposed is also excellent in effect, even more lightweight and robust. Future research direction can consider fusing multimodal data based on this model and developing more application scenarios.
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Affiliation(s)
- Shangfang Dai
- School of Economics and Management, Tsinghua University, Beijing, China
| | - Litao Han
- School of Mathematics, Renmin University of China, Beijing, China
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Wang D, Guerra A, Wittke F, Lang JC, Bakker K, Lee AW, Finelli L, Chen YH. Real-Time Monitoring of Infectious Disease Outbreaks with a Combination of Google Trends Search Results and the Moving Epidemic Method: A Respiratory Syncytial Virus Case Study. Trop Med Infect Dis 2023; 8:tropicalmed8020075. [PMID: 36828491 PMCID: PMC9962753 DOI: 10.3390/tropicalmed8020075] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2022] [Revised: 01/07/2023] [Accepted: 01/16/2023] [Indexed: 01/22/2023] Open
Abstract
The COVID-19 pandemic has disrupted the seasonal patterns of several infectious diseases. Understanding when and where an outbreak may occur is vital for public health planning and response. We usually rely on well-functioning surveillance systems to monitor epidemic outbreaks. However, not all countries have a well-functioning surveillance system in place, or at least not for the pathogen in question. We utilized Google Trends search results for RSV-related keywords to identify outbreaks. We evaluated the strength of the Pearson correlation coefficient between clinical surveillance data and online search data and applied the Moving Epidemic Method (MEM) to identify country-specific epidemic thresholds. Additionally, we established pseudo-RSV surveillance systems, enabling internal stakeholders to obtain insights on the speed and risk of any emerging RSV outbreaks in countries with imprecise disease surveillance systems but with Google Trends data. Strong correlations between RSV clinical surveillance data and Google Trends search results from several countries were observed. In monitoring an upcoming RSV outbreak with MEM, data collected from both systems yielded similar estimates of country-specific epidemic thresholds, starting time, and duration. We demonstrate in this study the potential of monitoring disease outbreaks in real time and complement classical disease surveillance systems by leveraging online search data.
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Affiliation(s)
- Dawei Wang
- Health Economic and Decision Sciences, Merck & Co., Inc., Kenilworth, NJ 07065, USA
- Correspondence:
| | - Andrea Guerra
- Clinical Development, MSD, Kings Cross, London EC2M 6UR, UK
| | | | - John Cameron Lang
- Health Economic and Decision Sciences, Merck & Co., Inc., Kenilworth, NJ 07065, USA
| | - Kevin Bakker
- Health Economic and Decision Sciences, Merck & Co., Inc., Kenilworth, NJ 07065, USA
| | - Andrew W. Lee
- Clinical Development, Merck & Co., Inc., Kenilworth, NJ 07065, USA
| | - Lyn Finelli
- Clinical Development, Merck & Co., Inc., Kenilworth, NJ 07065, USA
| | - Yao-Hsuan Chen
- Health Economic and Decision Sciences, MSD, Kings Cross, London EC2M 6UR, UK
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Yan W, Du M, Qin C, Liu Q, Wang Y, Liang W, Liu M, Liu J. Association between public attention and monkeypox epidemic: A global lag-correlation analysis. J Med Virol 2023; 95:e28382. [PMID: 36478381 PMCID: PMC10108296 DOI: 10.1002/jmv.28382] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2022] [Revised: 11/28/2022] [Accepted: 12/01/2022] [Indexed: 12/13/2022]
Abstract
The human monkeypox has become a public health problem globally. Google Trends Index (GTI) is an indicator of public attention, being potential for infectious disease outbreak surveillance. In this study, we used lag-correlation analysis to evaluate the spearman correlation coefficients between public attention and monkeypox epidemic by -36 to +36 days-lag in top 20 countries with most cumulated cases until September 30, 2022, the meta-analyses were performed to pool the coefficients of countries among all lags. We also constructed vector autoregression model and Granger-causality test to probe the significance of GTI in monkeypox forecasting. The strongest spearman correlation was found at lag +13 day (r = 0.53, 95% confidence interval: 0.371-0.703, p < 0.05). Meta-analysis showed significantly positive correlation when the lag was from -12 to +36 day, which was most notable on the third posterior day (lag +3 day). The pooled spearman correlation coefficients were all above 0.200 when the lag ranged from +1 to +20 day, and the causality of GTI for daily case was significant in worldwide and multiple countries. The findings suggested a robust association between 13-days-priority GTI and daily cases worldwide. This work introduced a potential monitor indicator on the early warning and surveillance of monkeypox outbreak.
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Affiliation(s)
- Wenxin Yan
- Department of Epidemiology and Biostatistics, School of Public HealthPeking UniversityBeijingChina
| | - Min Du
- Department of Epidemiology and Biostatistics, School of Public HealthPeking UniversityBeijingChina
| | - Chenyuan Qin
- Department of Epidemiology and Biostatistics, School of Public HealthPeking UniversityBeijingChina
| | - Qiao Liu
- Department of Epidemiology and Biostatistics, School of Public HealthPeking UniversityBeijingChina
| | - Yaping Wang
- Department of Epidemiology and Biostatistics, School of Public HealthPeking UniversityBeijingChina
| | - Wannian Liang
- Vanke School of Public HealthTsinghua UniversityBeijingChina
| | - Min Liu
- Department of Epidemiology and Biostatistics, School of Public HealthPeking UniversityBeijingChina
| | - Jue Liu
- Department of Epidemiology and Biostatistics, School of Public HealthPeking UniversityBeijingChina
- Institute for Global Health and DevelopmentPeking UniversityBeijingChina
- Global Center for Infectious Disease and Policy Research & Global Health and Infectious Diseases GroupPeking UniversityBeijingChina
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11
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Xiao J, Gao M, Huang M, Zhang W, Du Z, Liu T, Meng X, Ma W, Lin S. How do El Niño Southern Oscillation (ENSO) and local meteorological factors affect the incidence of seasonal influenza in New York state. HYGIENE AND ENVIRONMENTAL HEALTH ADVANCES 2022; 4:100040. [PMID: 36777308 PMCID: PMC9914518 DOI: 10.1016/j.heha.2022.100040] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Background Research is lacking in examining how multiple climate factors affect the incidence of seasonal influenza. We investigated the associations between El Niño Southern Oscillation (ENSO), meteorological factors, and influenza incidence in New York State, United States. Method We collected emergency department visit data for influenza from the New York State Department of Health. ENSO index was obtained from the National Oceanic and Atmospheric Administration. Meteorological factors, Google Flu Search Index (GFI), and Influenza-like illness (ILI) data in New York State were also collected. Wavelet analysis was used to quantitatively estimate the coherence and phase difference of ENSO, temperature, precipitation, relative humidity, and absolute humidity with emergency department visits of influenza in New York State. Generalized additive models (GAM) were employed to examine the exposure-response relationships between ENSO, weather, and influenza. GFI and ILI data were used to simulate synchronous influenza visits. Results The influenza epidemic in New York State had multiple periodic and was primarily on the 1-year scale. The incidence of influenza closely followed the low ENSO index by an average of two months, and the lag period of ENSO on influenza was shorter during 2015-2018. Low temperature in the previous 2 weeks and low absolute humidity in the prior week were positively associated with influenza incidence in New York State. We found an l-shaped association between ENSO index and influenza, a parabolic relationship between temperature in the previous two weeks and influenza, and a linear negative association between absolute humidity in the previous week and influenza. The simulation models including GFI and ILI had higher accuracy for influenza visit estimation. Conclusions Low ENSO index, low temperature, and low absolute humidity may drive the influenza epidemics in New York State. The findings can help us deepen the understanding of the climate-influenza association, and help to develop an influenza forecasting model.
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Affiliation(s)
- Jianpeng Xiao
- Guangdong Provincial Institute of Public Health, Guangdong Provincial Center for Disease Control and Prevention, Guangzhou 511430, China,Department of Occupational Health and Occupational Medicine, Guangdong Provincial Key Laboratory of Tropical Disease Research, School of Public Health, Southern Medical University, Guangzhou 510515, China,Department of Environmental Health Sciences, School of Public Health, University at Albany, State University of New York, Rensselaer, NY 12144, United States
| | - Michael Gao
- Department of Environmental Health Sciences, School of Public Health, University at Albany, State University of New York, Rensselaer, NY 12144, United States
| | - Miaoling Huang
- Department of Obstetrics and Gynecology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou 510120, China
| | - Wangjian Zhang
- Department of Medical Statistics and Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China
| | - Zhicheng Du
- Department of Medical Statistics and Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China
| | - Tao Liu
- Guangdong Provincial Institute of Public Health, Guangdong Provincial Center for Disease Control and Prevention, Guangzhou 511430, China,Department of Public Health and Preventive Medicine, School of Medicine, Jinan University, Guangzhou 510632, Guangdong, China
| | - Xiaojing Meng
- Department of Occupational Health and Occupational Medicine, Guangdong Provincial Key Laboratory of Tropical Disease Research, School of Public Health, Southern Medical University, Guangzhou 510515, China
| | - Wenjun Ma
- Guangdong Provincial Institute of Public Health, Guangdong Provincial Center for Disease Control and Prevention, Guangzhou 511430, China,Department of Public Health and Preventive Medicine, School of Medicine, Jinan University, Guangzhou 510632, Guangdong, China
| | - Shao Lin
- Department of Environmental Health Sciences, School of Public Health, University at Albany, State University of New York, Rensselaer, NY 12144, United States,Corresponding author at: One University Place, Rensselaer, NY 12144, (S. Lin)
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12
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Mukka M, Pesälä S, Juutinen A, Virtanen MJ, Mustonen P, Kaila M, Helve O. Online searches of children’s oseltamivir in public primary and specialized care: Detecting influenza outbreaks in Finland using dedicated databases for health care professionals. PLoS One 2022; 17:e0272040. [PMID: 35930527 PMCID: PMC9355218 DOI: 10.1371/journal.pone.0272040] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2021] [Accepted: 07/12/2022] [Indexed: 11/18/2022] Open
Abstract
Introduction
Health care professionals working in primary and specialized care typically search for medical information from Internet sources. In Finland, Physician’s Databases are online portals aimed at professionals seeking medical information. As dosage errors may occur when prescribing medication to children, professionals’ need for reliable medical information has increased in public health care centers and hospitals. Influenza continues to be a public health threat, with young children at risk of developing severe illness and easily transmitting the virus. Oseltamivir is used to treat children with influenza. The objective of this study was to compare searches for children’s oseltamivir and influenza diagnoses in primary and specialized care, and to determine if the searches could aid detection of influenza outbreaks.
Methods
We compared searches in Physician’s Databases for children’s oral suspension of oseltamivir (6 mg/mL) for influenza diagnoses of children under 7 years and laboratory findings of influenza A and B from the National Infectious Disease Register. Searches and diagnoses were assessed in primary and specialized care across Finland by season from 2012–2016. The Moving Epidemic Method (MEM) calculated seasonal starts and ends, and paired differences in the mean compared two indicators. Correlation was tested to compare seasons.
Results
We found that searches and diagnoses in primary and specialized care showed visually similar patterns annually. The MEM-calculated starting weeks in searches appeared mainly in the same week. Oseltamivir searches in primary care preceded diagnoses by −1.0 weeks (95% CI: −3.0, −0.3; p = 0.132) with very high correlation (τ = 0.913). Specialized care oseltamivir searches and diagnoses correlated moderately (τ = 0.667).
Conclusion
Health care professionals’ searches for children’s oseltamivir in online databases linked with the registers of children’s influenza diagnoses in primary and specialized care. Therefore, database searches should be considered as supplementary information in disease surveillance when detecting influenza epidemics.
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Affiliation(s)
- Milla Mukka
- University of Helsinki, Helsinki, Finland
- * E-mail:
| | - Samuli Pesälä
- University of Helsinki, Helsinki, Finland
- Epidemiological Operations Unit, City of Helsinki, Helsinki, Finland
| | - Aapo Juutinen
- Department of Health Security, Finnish Institute for Health and Welfare, Helsinki, Finland
| | - Mikko J. Virtanen
- Department of Health Security, Finnish Institute for Health and Welfare, Helsinki, Finland
| | | | - Minna Kaila
- Clinicum, University of Helsinki, Helsinki, Finland
| | - Otto Helve
- Department of Health Security, Finnish Institute for Health and Welfare, Helsinki, Finland
- Children’s Hospital, Pediatric Research Center, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
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13
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Mittelstadt B. Protecting Health Privacy through Reasonable Inferences. THE AMERICAN JOURNAL OF BIOETHICS : AJOB 2022; 22:65-68. [PMID: 35737503 DOI: 10.1080/15265161.2022.2075980] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
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14
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Wang L, Lin M, Wang J, Chen H, Yang M, Qiu S, Zheng T, Li Z, Song H. Quantitative analysis of the impact of various urban socioeconomic indicators on search-engine-based estimation of COVID-19 prevalence. Infect Dis Model 2022; 7:117-126. [PMID: 35475256 PMCID: PMC9020494 DOI: 10.1016/j.idm.2022.04.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2022] [Revised: 04/12/2022] [Accepted: 04/14/2022] [Indexed: 12/03/2022] Open
Abstract
Numerous studies have proposed search engine-based estimation of COVID-19 prevalence during the COVID-19 pandemic; however, their estimation models do not consider the impact of various urban socioeconomic indicators (USIs). This study quantitatively analysed the impact of various USIs on search engine-based estimation of COVID-19 prevalence using 15 USIs (including total population, gross regional product (GRP), and population density) from 369 cities in China. The results suggested that 13 USIs affected either the correlation (SC-corr) or time lag (SC-lag) between search engine query volume and new COVID-19 cases ( p <0.05). Total population and GRP impacted SC-corr considerably, with their correlation coefficients r for SC-corr being 0.65 and 0.59, respectively. Total population, GRP per capita, and proportion of the population with a high school diploma or higher had simultaneous positive impacts on SC-corr and SC-lag ( p <0.05); these three indicators explained 37-50% of the total variation in SC-corr and SC-lag. Estimations for different urban agglomerations revealed that the goodness of fit,R 2 , for search engine-based estimation was more than 0.6 only when total urban population, GRP per capita, and proportion of the population with a high school diploma or higher exceeded 11.08 million, 120,700, and 38.13%, respectively. A greater urban size indicated higher accuracy of search engine-based estimation of COVID-19 prevalence. Therefore, the accuracy and time lag for search engine-based estimation of infectious disease prevalence can be improved only when the total urban population, GRP per capita, and proportion of the population with a high school diploma or higher are greater than the aforementioned thresholds.
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Affiliation(s)
- Ligui Wang
- Department of Infectious Disease Prevention and Control, Center for Disease Control and Prevention of Chinese People's Liberation Army, Beijing, China
| | - Mengxuan Lin
- Academy of Military Medical Sciences, Academy of Military Science of Chinese PLA, Beijing, China
| | - Jiaojiao Wang
- The State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Hui Chen
- Department of Infectious Disease Prevention and Control, Center for Disease Control and Prevention of Chinese People's Liberation Army, Beijing, China
| | - Mingjuan Yang
- Department of Infectious Disease Prevention and Control, Center for Disease Control and Prevention of Chinese People's Liberation Army, Beijing, China
| | - Shaofu Qiu
- Department of Infectious Disease Prevention and Control, Center for Disease Control and Prevention of Chinese People's Liberation Army, Beijing, China
| | - Tao Zheng
- Academy of Military Medical Sciences, Academy of Military Science of Chinese PLA, Beijing, China
| | - Zhenjun Li
- State Key Laboratory for Infectious Disease Prevention and Control, National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Hongbin Song
- Department of Infectious Disease Prevention and Control, Center for Disease Control and Prevention of Chinese People's Liberation Army, Beijing, China
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15
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Serman E, Thrastarson HT, Franklin M, Teixeira J. Spatial Variation in Humidity and the Onset of Seasonal Influenza Across the Contiguous United States. GEOHEALTH 2022; 6:e2021GH000469. [PMID: 35136850 PMCID: PMC8808265 DOI: 10.1029/2021gh000469] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/02/2021] [Revised: 11/12/2021] [Accepted: 12/06/2021] [Indexed: 06/14/2023]
Abstract
In recent years, environmental factors, particularly humidity, have been used to inform influenza prediction models. This study aims to quantify the relationship between humidity and influenza incidence at the state-level in the contiguous United States. Piecewise segmented regressions were performed on specific humidity data from NASA's Atmospheric Infrared Sounder (AIRS) and incident influenza estimates from Google Flu Trends to identify threshold values of humidity that signal the onset of an influenza outbreak. Our results suggest that influenza incidence increases after reaching a humidity threshold that is state-specific. A linear regression showed that the state-specific thresholds were associated with annual average humidity conditions (R 2 = 0.9). Threshold values statistically significantly varied by region (F-statistic = 8.274, p < 0.001) and of their 36 pairwise combinations, 13 pairs had at least marginally statistically significant differences in their means. All of the significant comparisons included either the South or Southeast region, which had higher humidity threshold values. Results from this study improve our understanding of the significance of humidity in the transmission of influenza and reinforce the need for local and regional conditions to be considered in this relationship. Ultimately this could help researchers to produce more accurate forecasts of seasonal influenza onset and provide health officials with better information prior to outbreaks.
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Affiliation(s)
- E. Serman
- University of Southern CaliforniaLos AngelesCAUSA
| | - H. Th. Thrastarson
- Jet Propulsion LaboratoryCalifornia Institute of TechnologyPasadenaCAUSA
| | - M. Franklin
- University of Southern CaliforniaLos AngelesCAUSA
| | - J. Teixeira
- Jet Propulsion LaboratoryCalifornia Institute of TechnologyPasadenaCAUSA
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16
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Kuchler T, Russel D, Stroebel J. JUE Insight: The geographic spread of COVID-19 correlates with the structure of social networks as measured by Facebook. JOURNAL OF URBAN ECONOMICS 2022; 127:103314. [PMID: 35250112 PMCID: PMC8886493 DOI: 10.1016/j.jue.2020.103314] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/10/2020] [Revised: 12/11/2020] [Indexed: 05/05/2023]
Abstract
We use aggregated data from Facebook to show that COVID-19 is more likely to spread between regions with stronger social network connections. Areas with more social ties to two early COVID-19 "hotspots" (Westchester County, NY, in the U.S. and Lodi province in Italy) generally had more confirmed COVID-19 cases by the end of March. These relationships hold after controlling for geographic distance to the hotspots as well as the population density and demographics of the regions. As the pandemic progressed in the U.S., a county's social proximity to recent COVID-19 cases and deaths predicts future outbreaks over and above physical proximity and demographics. In part due to its broad coverage, social connectedness data provides additional predictive power to measures based on smartphone location or online search data. These results suggest that data from online social networks can be useful to epidemiologists and others hoping to forecast the spread of communicable diseases such as COVID-19.
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Affiliation(s)
- Theresa Kuchler
- New York University, Stern School of Business, NBER, and CEPR
| | - Dominic Russel
- New York University, Stern School of Business, NBER, and CEPR
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17
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Abstract
Influenza is a common respiratory infection that causes considerable morbidity and mortality worldwide each year. In recent years, along with the improvement in computational resources, there have been a number of important developments in the science of influenza surveillance and forecasting. Influenza surveillance systems have been improved by synthesizing multiple sources of information. Influenza forecasting has developed into an active field, with annual challenges in the United States that have stimulated improved methodologies. Work continues on the optimal approaches to assimilating surveillance data and information on relevant driving factors to improve estimates of the current situation (nowcasting) and to forecast future dynamics.
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Affiliation(s)
- Sheikh Taslim Ali
- World Health Organization Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China;
| | - Benjamin J Cowling
- World Health Organization Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China;
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18
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Cai O, Sousa-Pinto B. United States Influenza Search Patterns Since the Emergence of COVID-19: Infodemiology Study. JMIR Public Health Surveill 2021; 8:e32364. [PMID: 34878996 PMCID: PMC8896565 DOI: 10.2196/32364] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2021] [Revised: 10/30/2021] [Accepted: 11/30/2021] [Indexed: 12/11/2022] Open
Abstract
Background The emergence and media coverage of COVID-19 may have affected influenza search patterns, possibly affecting influenza surveillance results using Google Trends. Objective We aimed to investigate if the emergence of COVID-19 was associated with modifications in influenza search patterns in the United States. Methods We retrieved US Google Trends data (relative number of searches for specified terms) for the topics influenza, Coronavirus disease 2019, and symptoms shared between influenza and COVID-19. We calculated the correlations between influenza and COVID-19 search data for a 1-year period after the first COVID-19 diagnosis in the United States (January 21, 2020 to January 20, 2021). We constructed a seasonal autoregressive integrated moving average model and compared predicted search volumes, using the 4 previous years, with Google Trends relative search volume data. We built a similar model for shared symptoms data. We also assessed correlations for the past 5 years between Google Trends influenza data, US Centers for Diseases Control and Prevention influenza-like illness data, and influenza media coverage data. Results We observed a nonsignificant weak correlation (ρ= –0.171; P=0.23) between COVID-19 and influenza Google Trends data. Influenza search volumes for 2020-2021 distinctly deviated from values predicted by seasonal autoregressive integrated moving average models—for 6 weeks within the first 13 weeks after the first COVID-19 infection was confirmed in the United States, the observed volume of searches was higher than the upper bound of 95% confidence intervals for predicted values. Similar results were observed for shared symptoms with influenza and COVID-19 data. The correlation between Google Trends influenza data and CDC influenza-like-illness data decreased after the emergence of COVID-19 (2020-2021: ρ=0.643; 2019-2020: ρ=0.902), while the correlation between Google Trends influenza data and influenza media coverage volume remained stable (2020-2021: ρ=0.746; 2019-2020: ρ=0.707). Conclusions Relevant differences were observed between predicted and observed influenza Google Trends data the year after the onset of the COVID-19 pandemic in the United States. Such differences are possibly due to media coverage, suggesting limitations to the use of Google Trends as a flu surveillance tool.
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Affiliation(s)
- Owen Cai
- Shadow Creek High School, Pearland, US
| | - Bernardo Sousa-Pinto
- MEDCIDS - Department of Community Medicine, Information and Health Decision Sciences, Faculty of Medicine, University of Porto, Rua Plácido Costa s/n, Porto, PT.,CINTESIS - Center for Health Technologies and Services Research, University of Porto, Porto, PT
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19
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Jabour AM, Varghese J, Damad AH, Ghailan KY, Mehmood AM. Examining the Correlation of Google Influenza Trend with Hospital Data: Retrospective Study. J Multidiscip Healthc 2021; 14:3073-3081. [PMID: 34754195 PMCID: PMC8572114 DOI: 10.2147/jmdh.s322185] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2021] [Accepted: 10/15/2021] [Indexed: 11/23/2022] Open
Abstract
Introduction Many studies have explored social media and users search activities such as Google Trends to predict and detect influenza activities. Studies that examined Google Trends correlation with the actual hospital influenza cases were conducted in non-tropical regions that have clearly defined seasons. Tropical areas are known for having less-defined seasonality and the extent of Google Trends concordance with actual influenza cases is unknown for these areas. The goal of this study is to compare Google Trends with hospital cases in tropical regions. Methods We analyzed 48,263 influenza cases in the time period of 2010 to 2019. The cases were retrieved from central hospital medical records in tropical regions using the corresponding codes for influenza ICD-10 AM. Cases from the medical records were compared with Google Trends to determine trends, seasonality, and correlation. Results Graphically, there were some similar areas of the trend, but cross-correlation analysis did not show any significant correlation between hospital and Google Trends with a maximum correlation rate of 0.300. Seasonality analysis showed a clear pattern that peaked around November in Google Trends while hospital data showed less defined seasonality with a smaller peak occurring at the end of December and beginning of January. Conclusion Based on the results, there is a weak correlation between Google Trends and hospital data. More innovative methods are emerging to predict influenza activity using social media and user search data and further study is needed to examine the concurrent trends derived using these methods across regions that have different humidity levels and temperatures.
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Affiliation(s)
- Abdulrahman M Jabour
- Health Informatics Department, Faculty of Public Health and Tropical Medicine, Jazan University, Jazan, Saudi Arabia
| | - Joe Varghese
- Health Informatics Department, Faculty of Public Health and Tropical Medicine, Jazan University, Jazan, Saudi Arabia
| | - Ahmed H Damad
- Quality & Patient Safety Department, King Fahd Central Hospital - Jazan, Jazan, Saudi Arabia
| | - Khalid Y Ghailan
- Epidemiology Department, Faculty of Public Health and Tropical Medicine, Jazan University, Jazan, Saudi Arabia
| | - Asim M Mehmood
- Health Informatics Department, Faculty of Public Health and Tropical Medicine, Jazan University, Jazan, Saudi Arabia
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20
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Chamola V, Hassija V, Gupta S, Goyal A, Guizani M, Sikdar B. Disaster and Pandemic Management Using Machine Learning: A Survey. IEEE INTERNET OF THINGS JOURNAL 2021; 8:16047-16071. [PMID: 35782181 PMCID: PMC8768997 DOI: 10.1109/jiot.2020.3044966] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/16/2020] [Revised: 11/26/2020] [Accepted: 12/10/2020] [Indexed: 05/14/2023]
Abstract
This article provides a literature review of state-of-the-art machine learning (ML) algorithms for disaster and pandemic management. Most nations are concerned about disasters and pandemics, which, in general, are highly unlikely events. To date, various technologies, such as IoT, object sensing, UAV, 5G, and cellular networks, smartphone-based system, and satellite-based systems have been used for disaster and pandemic management. ML algorithms can handle multidimensional, large volumes of data that occur naturally in environments related to disaster and pandemic management and are particularly well suited for important related tasks, such as recognition and classification. ML algorithms are useful for predicting disasters and assisting in disaster management tasks, such as determining crowd evacuation routes, analyzing social media posts, and handling the post-disaster situation. ML algorithms also find great application in pandemic management scenarios, such as predicting pandemics, monitoring pandemic spread, disease diagnosis, etc. This article first presents a tutorial on ML algorithms. It then presents a detailed review of several ML algorithms and how we can combine these algorithms with other technologies to address disaster and pandemic management. It also discusses various challenges, open issues and, directions for future research.
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Affiliation(s)
- Vinay Chamola
- Department of Electrical and Electronics Engineering & APPCAIRBirla Institute of Technology and Science at PilaniPilani333031India
| | - Vikas Hassija
- Department of Computer Science and ITJaypee Institute of Information TechnologyNoida201304India
| | - Sakshi Gupta
- Department of Computer Science and ITJaypee Institute of Information TechnologyNoida201304India
| | - Adit Goyal
- Department of Computer Science and ITJaypee Institute of Information TechnologyNoida201304India
| | - Mohsen Guizani
- Department of Computer Science and EngineeringQatar UniversityDohaQatar
| | - Biplab Sikdar
- Department of Electrical and Computer EngineeringNational University of SingaporeSingapore119077
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21
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Pollett S, Johansson MA, Reich NG, Brett-Major D, Del Valle SY, Venkatramanan S, Lowe R, Porco T, Berry IM, Deshpande A, Kraemer MUG, Blazes DL, Pan-ngum W, Vespigiani A, Mate SE, Silal SP, Kandula S, Sippy R, Quandelacy TM, Morgan JJ, Ball J, Morton LC, Althouse BM, Pavlin J, van Panhuis W, Riley S, Biggerstaff M, Viboud C, Brady O, Rivers C. Recommended reporting items for epidemic forecasting and prediction research: The EPIFORGE 2020 guidelines. PLoS Med 2021; 18:e1003793. [PMID: 34665805 PMCID: PMC8525759 DOI: 10.1371/journal.pmed.1003793] [Citation(s) in RCA: 36] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
BACKGROUND The importance of infectious disease epidemic forecasting and prediction research is underscored by decades of communicable disease outbreaks, including COVID-19. Unlike other fields of medical research, such as clinical trials and systematic reviews, no reporting guidelines exist for reporting epidemic forecasting and prediction research despite their utility. We therefore developed the EPIFORGE checklist, a guideline for standardized reporting of epidemic forecasting research. METHODS AND FINDINGS We developed this checklist using a best-practice process for development of reporting guidelines, involving a Delphi process and broad consultation with an international panel of infectious disease modelers and model end users. The objectives of these guidelines are to improve the consistency, reproducibility, comparability, and quality of epidemic forecasting reporting. The guidelines are not designed to advise scientists on how to perform epidemic forecasting and prediction research, but rather to serve as a standard for reporting critical methodological details of such studies. CONCLUSIONS These guidelines have been submitted to the EQUATOR network, in addition to hosting by other dedicated webpages to facilitate feedback and journal endorsement.
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Affiliation(s)
- Simon Pollett
- Walter Reed Army Institute of Research, Silver Spring, Maryland, United States of America
| | - Michael A. Johansson
- Division of Vector-Borne Diseases, Centers for Disease Control & Prevention, San Juan, Puerto Rico, United States of America
| | - Nicholas G. Reich
- University of Massachusetts–Amherst, School of Public Health and Health Sciences, Amherst, Massachusetts, United States of America
| | - David Brett-Major
- University of Nebraska Medical Center, Omaha, Nebraska, United States of America
| | - Sara Y. Del Valle
- Los Alamos National Laboratory, Los Alamos, New Mexico, United States of America
| | - Srinivasan Venkatramanan
- Biocomplexity Institute and Initiative, University of Virginia, Charlottesville, Virginia, United States of America
| | - Rachel Lowe
- Centre for Mathematical Modelling of Infectious Diseases and Centre on Climate Change and Planetary Health, London School of Hygiene & Tropical Medicine, London, United Kingdom
- Barcelona Institute for Global Health, Barcelona, Spain
| | - Travis Porco
- University of California at San Francisco, San Francisco, California, United States of America
| | - Irina Maljkovic Berry
- Walter Reed Army Institute of Research, Silver Spring, Maryland, United States of America
| | - Alina Deshpande
- Los Alamos National Laboratory, Los Alamos, New Mexico, United States of America
| | | | - David L. Blazes
- Bill and Melinda Gates Foundation, Seattle, Washington, United States of America
| | - Wirichada Pan-ngum
- Mahidol-Oxford Tropical Medicine Research Unit and Department of Tropical Hygiene, Mahidol University, Thailand
| | - Alessandro Vespigiani
- Network Science Institute, Northeastern University, Boston, Massachusetts, United States of America
| | - Suzanne E. Mate
- Walter Reed Army Institute of Research, Silver Spring, Maryland, United States of America
| | - Sheetal P. Silal
- Modelling and Simulation Hub, Africa, Department of Statistical Sciences, University of Cape Town, Cape Town, South Africa
- Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
| | - Sasikiran Kandula
- Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York City, New York, United States of America
| | - Rachel Sippy
- Institute for Global Health and Translational Science, State University of New York Upstate Medical University, Syracuse, New York, United States of America
| | - Talia M. Quandelacy
- Division of Vector-Borne Diseases, Centers for Disease Control & Prevention, San Juan, Puerto Rico, United States of America
| | - Jeffrey J. Morgan
- Catholic University of America, Washington, DC, United States of America
| | - Jacob Ball
- U.S. Army Public Health Center, Edgewood, Maryland, United States of America
| | - Lindsay C. Morton
- Armed Forces Health Surveillance Division, Global Emerging Infections Surveillance, Silver Spring, Maryland, United States of America
- George Washington University, Milken Institute School of Public Health, Washington, DC, United States of America
| | - Benjamin M. Althouse
- University of Washington, Seattle, Washington, United States of America
- Institute for Disease Modeling, Bellevue, Washington, United States of America
- New Mexico State University, Las Cruces, New Mexico, United States of America
| | - Julie Pavlin
- National Academies of Sciences, Engineering, and Medicine, Washington, DC, United States of America
| | - Wilbert van Panhuis
- University of Pittsburgh Graduate School of Public Health, Pittsburgh, Pennsylvania, United States of America
| | - Steven Riley
- MRC Centre for Global Infectious Disease Analysis, School of Public Health, Imperial College, London, United Kingdom
| | - Matthew Biggerstaff
- Influenza Division, Centers for Disease Control & Prevention, Atlanta, Georgia, United States of America
| | - Cecile Viboud
- Fogarty International Center, National Institutes for Health, Bethesda, Maryland, United States of America
| | - Oliver Brady
- Centre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, United Kingdom
| | - Caitlin Rivers
- Johns Hopkins Center for Health Security, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, United States of America
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22
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Quandelacy TM, Zimmer S, Lessler J, Vukotich C, Bieltz R, Grantz KH, Galloway D, Read JM, Zheteyeva Y, Gao H, Uzicanin A, Cummings DAT. Predicting virologically confirmed influenza using school absences in Allegheny County, Pennsylvania, USA during the 2007-2015 influenza seasons. Influenza Other Respir Viruses 2021; 15:757-766. [PMID: 34477304 PMCID: PMC8542956 DOI: 10.1111/irv.12865] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2021] [Revised: 03/30/2021] [Accepted: 03/31/2021] [Indexed: 11/30/2022] Open
Abstract
Background Children are important in community‐level influenza transmission. School‐based monitoring may inform influenza surveillance. Methods We used reported weekly confirmed influenza in Allegheny County during the 2007 and 2010‐2015 influenza seasons using Pennsylvania's Allegheny County Health Department all‐age influenza cases from health facilities, and all‐cause and influenza‐like illness (ILI)‐specific absences from nine county school districts. Negative binomial regression predicted influenza cases using all‐cause and illness‐specific absence rates, calendar week, average weekly temperature, and relative humidity, using four cross‐validations. Results School districts reported 2 184 220 all‐cause absences (2010‐2015). Three one‐season studies reported 19 577 all‐cause and 3012 ILI‐related absences (2007, 2012, 2015). Over seven seasons, 11 946 confirmed influenza cases were reported. Absences improved seasonal model fits and predictions. Multivariate models using elementary school absences outperformed middle and high school models (relative mean absolute error (relMAE) = 0.94, 0.98, 0.99). K‐5 grade‐specific absence models had lowest mean absolute errors (MAE) in cross‐validations. ILI‐specific absences performed marginally better than all‐cause absences in two years, adjusting for other covariates, but markedly worse one year. Conclusions Our findings suggest seasonal models including K‐5th grade absences predict all‐age‐confirmed influenza and may serve as a useful surveillance tool.
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Affiliation(s)
- Talia M Quandelacy
- Johns Hopkins University, Baltimore, MD, USA.,University of Colorado, Anschutz Medical Campus, Aurora, CO, USA
| | - Shanta Zimmer
- University of Pittsburgh, Pittsburgh, PA, USA.,University of Colorado, Denver, CO, USA
| | | | | | | | | | | | | | | | - Hongjiang Gao
- Centers for Disease Control and Prevention, Atlanta, GA, USA
| | - Amra Uzicanin
- Centers for Disease Control and Prevention, Atlanta, GA, USA
| | - Derek A T Cummings
- Johns Hopkins University, Baltimore, MD, USA.,University of Florida, Gainesville, FL, USA
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23
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Turtle J, Riley P, Ben-Nun M, Riley S. Accurate influenza forecasts using type-specific incidence data for small geographic units. PLoS Comput Biol 2021; 17:e1009230. [PMID: 34324487 PMCID: PMC8354478 DOI: 10.1371/journal.pcbi.1009230] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2020] [Revised: 08/10/2021] [Accepted: 06/30/2021] [Indexed: 11/24/2022] Open
Abstract
Influenza incidence forecasting is used to facilitate better health system planning and could potentially be used to allow at-risk individuals to modify their behavior during a severe seasonal influenza epidemic or a novel respiratory pandemic. For example, the US Centers for Disease Control and Prevention (CDC) runs an annual competition to forecast influenza-like illness (ILI) at the regional and national levels in the US, based on a standard discretized incidence scale. Here, we use a suite of forecasting models to analyze type-specific incidence at the smaller spatial scale of clusters of nearby counties. We used data from point-of-care (POC) diagnostic machines over three seasons, in 10 clusters, capturing: 57 counties; 1,061,891 total specimens; and 173,909 specimens positive for Influenza A. Total specimens were closely correlated with comparable CDC ILI data. Mechanistic models were substantially more accurate when forecasting influenza A positive POC data than total specimen POC data, especially at longer lead times. Also, models that fit subpopulations of the cluster (individual counties) separately were better able to forecast clusters than were models that directly fit to aggregated cluster data. Public health authorities may wish to consider developing forecasting pipelines for type-specific POC data in addition to ILI data. Simple mechanistic models will likely improve forecast accuracy when applied at small spatial scales to pathogen-specific data before being scaled to larger geographical units and broader syndromic data. Highly local forecasts may enable new public health messaging to encourage at-risk individuals to temporarily reduce their social mixing during seasonal peaks and guide public health intervention policy during potentially severe novel influenza pandemics.
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Affiliation(s)
- James Turtle
- Infectious Disease Group, Predictive Science Inc., San Diego, California, United States
- * E-mail:
| | - Pete Riley
- Infectious Disease Group, Predictive Science Inc., San Diego, California, United States
| | - Michal Ben-Nun
- Infectious Disease Group, Predictive Science Inc., San Diego, California, United States
| | - Steven Riley
- Infectious Disease Group, Predictive Science Inc., San Diego, California, United States
- MRC Centre for Outbreak Analysis and Modelling, Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, United Kingdom
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Miliou I, Xiong X, Rinzivillo S, Zhang Q, Rossetti G, Giannotti F, Pedreschi D, Vespignani A. Predicting seasonal influenza using supermarket retail records. PLoS Comput Biol 2021; 17:e1009087. [PMID: 34252075 PMCID: PMC8297944 DOI: 10.1371/journal.pcbi.1009087] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2020] [Revised: 07/22/2021] [Accepted: 05/15/2021] [Indexed: 11/19/2022] Open
Abstract
Increased availability of epidemiological data, novel digital data streams, and the rise of powerful machine learning approaches have generated a surge of research activity on real-time epidemic forecast systems. In this paper, we propose the use of a novel data source, namely retail market data to improve seasonal influenza forecasting. Specifically, we consider supermarket retail data as a proxy signal for influenza, through the identification of sentinel baskets, i.e., products bought together by a population of selected customers. We develop a nowcasting and forecasting framework that provides estimates for influenza incidence in Italy up to 4 weeks ahead. We make use of the Support Vector Regression (SVR) model to produce the predictions of seasonal flu incidence. Our predictions outperform both a baseline autoregressive model and a second baseline based on product purchases. The results show quantitatively the value of incorporating retail market data in forecasting models, acting as a proxy that can be used for the real-time analysis of epidemics.
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Affiliation(s)
- Ioanna Miliou
- University of Pisa, Pisa, Italy
- ISTI-CNR, Pisa, Italy
| | - Xinyue Xiong
- Northeastern University, Boston, Massachusetts, United States of America
| | | | - Qian Zhang
- Northeastern University, Boston, Massachusetts, United States of America
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A novel data-driven methodology for influenza outbreak detection and prediction. Sci Rep 2021; 11:13275. [PMID: 34168200 PMCID: PMC8225876 DOI: 10.1038/s41598-021-92484-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2020] [Accepted: 06/08/2021] [Indexed: 12/01/2022] Open
Abstract
Influenza is an infectious disease that leads to an estimated 5 million cases of severe illness and 650,000 respiratory deaths worldwide each year. The early detection and prediction of influenza outbreaks are crucial for efficient resource planning to save patient’s lives and healthcare costs. We propose a new data-driven methodology for influenza outbreak detection and prediction at very local levels. A doctor’s diagnostic dataset of influenza-like illness from more than 3000 clinics in Malaysia is used in this study because these diagnostic data are reliable and can be captured promptly. A new region index (RI) of the influenza outbreak is proposed based on the diagnostic dataset. By analysing the anomalies in the weekly RI value, potential outbreaks are identified using statistical methods. An ensemble learning method is developed to predict potential influenza outbreaks. Cross-validation is conducted to optimize the hyperparameters of the ensemble model. A testing data set is used to provide an unbiased evaluation of the model. The proposed methodology is shown to be sensitive and accurate at influenza outbreak prediction, with average of 75% recall, 74% precision, and 83% accuracy scores across five regions in Malaysia. The results are also validated by Google Flu Trends data, news reports, and surveillance data released by World Health Organization.
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Choi H, Choi WS, Han E. Suggestion of a simpler and faster influenza-like illness surveillance system using 2014-2018 claims data in Korea. Sci Rep 2021; 11:11243. [PMID: 34045533 PMCID: PMC8159991 DOI: 10.1038/s41598-021-90511-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2020] [Accepted: 05/06/2021] [Indexed: 11/10/2022] Open
Abstract
Influenza is an important public health concern. We propose a new real-time influenza-like illness (ILI) surveillance system that utilizes a nationwide prospective drug utilization monitoring in Korea. We defined ILI-related claims as outpatient claims that contain both antipyretic and antitussive agents and calculated the weekly rate of ILI-related claims, which was compared to weekly ILI rates from clinical sentinel surveillance data during 2014-2018. We performed a cross-correlation analysis using Pearson's correlation, time-series analysis to explore actual correlations after removing any dubious correlations due to underlying non-stationarity in both data sets. We used the moving epidemic method (MEM) to estimate an absolute threshold to designate potential influenza epidemics for the weeks with incidence rates above the threshold. We observed a strong correlation between the two surveillance systems each season. The absolute thresholds for the 4-years were 84.64 and 86.19 cases per 1000claims for claims data and 12.27 and 16.82 per 1000 patients for sentinel data. The epidemic patterns were more similar in the 2016-2017 and 2017-2018 seasons than the 2014-2015 and 2015-2016 seasons. ILI claims data can be loaded to a drug utilization review system in Korea to make an influenza surveillance system.
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Affiliation(s)
- HeeKyoung Choi
- College of Pharmacy, Yonsei Institute of Pharmaceutical Research, Yonsei University, 162-1 Songdo-dong, Yeonsu-gu, Incheon, Seoul, Republic of Korea
- Division of Infectious Diseases, Department of Internal Medicine, National Health Insurance Service Ilsan Hospital, Ilsan, Republic of Korea
| | - Won Suk Choi
- Division of Infectious Diseases, Department of Internal Medicine, Ansan Hospital, Korea University College of Medicine, Ansan, Republic of Korea
| | - Euna Han
- College of Pharmacy, Yonsei Institute of Pharmaceutical Research, Yonsei University, 162-1 Songdo-dong, Yeonsu-gu, Incheon, Seoul, Republic of Korea.
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Jun SP, Yoo HS, Lee JS. The impact of the pandemic declaration on public awareness and behavior: Focusing on COVID-19 google searches. TECHNOLOGICAL FORECASTING AND SOCIAL CHANGE 2021; 166:120592. [PMID: 33776154 PMCID: PMC7978359 DOI: 10.1016/j.techfore.2021.120592] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/20/2020] [Revised: 01/06/2021] [Accepted: 01/07/2021] [Indexed: 05/28/2023]
Abstract
The unprecedented outbreaks of epidemics such as the coronavirus has caused major socio-economic changes. To analyze public risk awareness and behavior in response to the outbreak of epidemic diseases, this study focuses on RSV (Relative Search Volume) provided by Google Trends. This study uses the social big data provided by Google RSV to investigate how the WHO's pandemic declaration affected public awareness and behavior. 37 OECD countries were analyzed and clustered according to the degree of reaction to the declaration, and the United States, France and Germany were selected for comparative study. The results of this study statistically confirmed that the pandemic declaration increased public awareness and had the effect of increasing searches for information on COVID-19 by more than 20%. In addition, this rapid rise in RSV also reflected interest in the COVID-19 test and had the effect of inducing individuals to be tested, which helped identify new cases. The significance of this study is that it provided the theoretical foundation for using RSV and its implications to understand and strategically utilize public awareness and behavior in situations where the WHO and governments must launch policies in response to the outbreak of new infectious diseases such as COVID-19.
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Affiliation(s)
- Seung-Pyo Jun
- Data Analysis Platform Center, Korea Institute of Science and Technology Information and Science & Technology Management Policy, University of Science & Technology (UST), 66, Hoegi-ro, Dongdaemun-gu, Seoul 130-741, Korea
| | - Hyoung Sun Yoo
- Korea Institute of Science and Technology Information and Science & Technology Management Policy, University of Science & Technology (UST), 66, Hoegi-ro, Dongdaemun-gu, Seoul 130-741, Korea
| | - Jae-Seong Lee
- Data Analysis Platform Center, Korea Institute of Science and Technology Information and Science & Technology Management Policy, University of Science & Technology (UST), 66, Hoegi-ro, Dongdaemun-gu, Seoul 130-741, Korea
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28
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Kardeş S, Kuzu AS, Pakhchanian H, Raiker R, Karagülle M. Population-level interest in anti-rheumatic drugs in the COVID-19 era: insights from Google Trends. Clin Rheumatol 2021; 40:2047-2055. [PMID: 33130946 PMCID: PMC7603411 DOI: 10.1007/s10067-020-05490-w] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2020] [Revised: 10/21/2020] [Accepted: 10/28/2020] [Indexed: 12/23/2022]
Abstract
INTRODUCTION/OBJECTIVE The general public may utilize online information through search engines for implications and risks of some anti-rheumatic drugs. These drugs have been used in the management of coronavirus disease 2019 (COVID-19) and associated inflammatory sequelae or cytokine storm of infection. Therefore, the objective of this study was to investigate the population-level interest in anti-rheumatic drugs during the COVID-19 era, by analyzing changes in Google search frequency data. METHOD To obtain the relative search volume (RSV) of anti-rheumatic drugs, we queried Google Trends for 78 search terms representing non-steroidal anti-inflammatory drugs (NSAIDs), glucocorticoids, antigout agents, conventional disease-modifying anti-rheumatic drugs (DMARDs), immunosuppressants, biologics, and Janus kinase (JAK) inhibitors within the USA. Three 8-week periods in 2020 (March 15-May 9), (May 10-July 4), and (July 5-August 29) representing the initial- and short-term periods were compared to overlapping periods of the preceding 3 years (2017-2019). RESULTS We found statistically significant increases in RSV for colchicine, hydroxychloroquine, tocilizumab (and its brand name-Actemra), and anakinra, and statistically significant decreases among brand names of immunosuppressive agents (i.e., mycophenolate mofetil, azathioprine, cyclophosphamide, tacrolimus, cyclosporine) during both the initial- and short-term COVID-19 periods as compared to overlapping periods of the preceding 3 years. CONCLUSION There were significant increases in RSV of colchicine, hydroxychloroquine, tocilizumab, and anakinra during both initial- and short-term COVID-19 periods when compared to overlapping periods of the preceding 3 years reflecting a heightened level of information-seeking on these drugs during the pandemic. Rheumatologists should address this increase in informational demand. Further research assessing medium- and long-term interest in anti-rheumatic drugs is required to increase our knowledge on this new pandemic. Key Points •This study was aimed to investigate the population-level interest in anti-rheumatic drugs in the COVID-19 era, by analyzing changes in Google search frequency data. •Significant increases were seen in relative searches for colchicine, hydroxychloroquine, tocilizumab, and anakinra during both initial and short-term COVID-19 periods when compared to similar periods of 2017-2019 reflecting a heightened level of information-seeking on these drugs during the pandemic. •Rheumatologists should address this increase in informational demand for colchicine, hydroxychloroquine, tocilizumab, and anakinra.
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Affiliation(s)
- Sinan Kardeş
- Department of Medical Ecology and Hydroclimatology, Istanbul Faculty of Medicine, Istanbul University, Capa-Fatih, 34093 Istanbul, Turkey
| | - Ali Suat Kuzu
- Department of Medical Ecology and Hydroclimatology, Istanbul Faculty of Medicine, Istanbul University, Capa-Fatih, 34093 Istanbul, Turkey
| | - Haig Pakhchanian
- George Washington University School of Medicine & Health Science, Washington, DC USA
| | - Rahul Raiker
- West Virginia University School of Medicine, Morgantown, WV USA
| | - Mine Karagülle
- Department of Medical Ecology and Hydroclimatology, Istanbul Faculty of Medicine, Istanbul University, Capa-Fatih, 34093 Istanbul, Turkey
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29
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Lampos V, Majumder MS, Yom-Tov E, Edelstein M, Moura S, Hamada Y, Rangaka MX, McKendry RA, Cox IJ. Tracking COVID-19 using online search. NPJ Digit Med 2021; 4:17. [PMID: 33558607 PMCID: PMC7870878 DOI: 10.1038/s41746-021-00384-w] [Citation(s) in RCA: 49] [Impact Index Per Article: 16.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2020] [Accepted: 12/24/2020] [Indexed: 12/30/2022] Open
Abstract
Previous research has demonstrated that various properties of infectious diseases can be inferred from online search behaviour. In this work we use time series of online search query frequencies to gain insights about the prevalence of COVID-19 in multiple countries. We first develop unsupervised modelling techniques based on associated symptom categories identified by the United Kingdom’s National Health Service and Public Health England. We then attempt to minimise an expected bias in these signals caused by public interest—as opposed to infections—using the proportion of news media coverage devoted to COVID-19 as a proxy indicator. Our analysis indicates that models based on online searches precede the reported confirmed cases and deaths by 16.7 (10.2–23.2) and 22.1 (17.4–26.9) days, respectively. We also investigate transfer learning techniques for mapping supervised models from countries where the spread of the disease has progressed extensively to countries that are in earlier phases of their respective epidemic curves. Furthermore, we compare time series of online search activity against confirmed COVID-19 cases or deaths jointly across multiple countries, uncovering interesting querying patterns, including the finding that rarer symptoms are better predictors than common ones. Finally, we show that web searches improve the short-term forecasting accuracy of autoregressive models for COVID-19 deaths. Our work provides evidence that online search data can be used to develop complementary public health surveillance methods to help inform the COVID-19 response in conjunction with more established approaches.
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Affiliation(s)
- Vasileios Lampos
- Department of Computer Science, University College London, London, UK.
| | - Maimuna S Majumder
- Computational Health Informatics Program, Boston Children's Hospital, Boston, MA, USA.,Department of Pediatrics, Harvard Medical School, Boston, MA, USA
| | | | - Michael Edelstein
- National Infection Service, Public Health England, London, UK.,Department of Population Health, Faculty of Medicine, Bar-Ilan University, Safed, Israel
| | - Simon Moura
- Department of Computer Science, University College London, London, UK
| | - Yohhei Hamada
- Institute for Global Health, University College London, London, UK
| | - Molebogeng X Rangaka
- Institute for Global Health, University College London, London, UK.,Division of Epidemiology and Biostatistics, University of Cape Town, Cape Town, South Africa
| | - Rachel A McKendry
- London Centre for Nanotechnology, University College London, London, UK.,Division of Medicine, University College London, London, UK
| | - Ingemar J Cox
- Department of Computer Science, University College London, London, UK.,Department of Computer Science, University of Copenhagen, Copenhagen, Denmark
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30
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Zhu G, Li L, Zheng Y, Zhang X, Zou H. Forecasting Influenza Based on Autoregressive Moving Average and Holt-Winters Exponential Smoothing Models. JOURNAL OF ADVANCED COMPUTATIONAL INTELLIGENCE AND INTELLIGENT INFORMATICS 2021. [DOI: 10.20965/jaciii.2021.p0138] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Influenza outbreaks can be effectively prevented if further outbreaks are predicted as early as possible. This article proposes an autoregressive integrated moving average (ARIMA) model and a Holt-Winters exponential smoothing (HWES) model to analyze tweet data for predicting influenza outbreaks and to visualize the number of flu-infection-related tweets with heat maps. First, textual influenza data for Australia from June 2015 to June 2017 are collected through the Twitter Application Programming Interface (API). Next, the ARIMA and HWES models are applied to predict the difference between the flu tweets and confirmations from the Centers for Disease Control and Prevention. Finally, a visualized heat map based on influenza topics validates the modeling analysis in two different time zones. The results show that the average relative error of the ARIMA (HWES) model is 7.25% (11.29%) for the one-week flu forecast.
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31
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Tanniru MR. Transforming public health using value lens and extended partner networks. Learn Health Syst 2021; 5:e10234. [PMID: 33490383 PMCID: PMC7805004 DOI: 10.1002/lrh2.10234] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2020] [Revised: 06/09/2020] [Accepted: 06/11/2020] [Indexed: 11/06/2022] Open
Abstract
INTRODUCTION Organizational transformations have focused on creating and fulfilling value for customers, leveraging advanced technologies. Transforming public health (PH) faces an interesting challenge. The value created (preventive practices) to fulfill policy makers' desire to reduce healthcare costs is realized by several external partners with varying goals and is practiced by the public (value in use), which often places low priority on prevention. METHODS This paper uses value lens to argue that PH transformation strategy must align the goals of all stakeholders involved. This may include allowing partners and the public to contextualize the preventive practices to see the value in near term and as relevant. It also means extending the number of partners PH uses and helping them connect with the public to seek shared alignment in shared goals of value fulfillment and value-in-use. RESULTS Using lessons from Covid-19 and PH experience with partners in four different sectors: business, healthcare, public and community, the paper illustrates how PH transformation strategy can be implemented going forward. CONCLUSIONS We conclude the paper with five distinct directions for future research to create and sustain value using the framework of learning health systems.
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Choi D, Sumner SA, Holland KM, Draper J, Murphy S, Bowen DA, Zwald M, Wang J, Law R, Taylor J, Konjeti C, De Choudhury M. Development of a Machine Learning Model Using Multiple, Heterogeneous Data Sources to Estimate Weekly US Suicide Fatalities. JAMA Netw Open 2020; 3:e2030932. [PMID: 33355678 PMCID: PMC7758810 DOI: 10.1001/jamanetworkopen.2020.30932] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/12/2020] [Accepted: 11/02/2020] [Indexed: 11/16/2022] Open
Abstract
Importance Suicide is a leading cause of death in the US. However, official national statistics on suicide rates are delayed by 1 to 2 years, hampering evidence-based public health planning and decision-making. Objective To estimate weekly suicide fatalities in the US in near real time. Design, Setting, and Participants This cross-sectional national study used a machine learning pipeline to combine signals from several streams of real-time information to estimate weekly suicide fatalities in the US in near real time. This 2-phase approach first fits optimal machine learning models to each individual data stream and subsequently combines predictions made from each data stream via an artificial neural network. National-level US administrative data on suicide deaths, health services, and economic, meteorological, and online data were variously obtained from 2014 to 2017. Data were analyzed from January 1, 2014, to December 31, 2017. Exposures Longitudinal data on suicide-related exposures were obtained from multiple, heterogeneous streams: emergency department visits for suicide ideation and attempts collected via the National Syndromic Surveillance Program (2015-2017); calls to the National Suicide Prevention Lifeline (2014-2017); calls to US poison control centers for intentional self-harm (2014-2017); consumer price index and seasonality-adjusted unemployment rate, hourly earnings, home price index, and 3-month and 10-year yield curves from the Federal Reserve Economic Data (2014-2017); weekly daylight hours (2014-2017); Google and YouTube search trends related to suicide (2014-2017); and public posts on suicide on Reddit (2 314 533 posts), Twitter (9 327 472 tweets; 2015-2017), and Tumblr (1 670 378 posts; 2014-2017). Main Outcomes and Measures Weekly estimates of suicide fatalities in the US were obtained through a machine learning pipeline that integrated the above data sources. Estimates were compared statistically with actual fatalities recorded by the National Vital Statistics System. Results Combining information from multiple data streams, the machine learning method yielded estimates of weekly suicide deaths with high correlation to actual counts and trends (Pearson correlation, 0.811; P < .001), while estimating annual suicide rates with low error (0.55%). Conclusions and Relevance The proposed ensemble machine learning framework reduces the error for annual suicide rate estimation to less than one-tenth of that of current forecasting approaches that use only historical information on suicide deaths. These findings establish a novel approach for tracking suicide fatalities in near real time and provide the potential for an effective public health response such as supporting budgetary decisions or deploying interventions.
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Affiliation(s)
- Daejin Choi
- Department of Computer Science and Engineering, Incheon National University, Incheon, South Korea
| | - Steven A. Sumner
- Office of Strategy and Innovation, National Center for Injury Prevention and Control, Centers for Disease Control and Prevention, Atlanta, Georgia
| | - Kristin M. Holland
- Division of Violence Prevention, National Center for Injury Prevention and Control, Centers for Disease Control and Prevention, Atlanta, Georgia
| | - John Draper
- National Suicide Prevention Lifeline, New York, New York
| | - Sean Murphy
- National Suicide Prevention Lifeline, New York, New York
| | - Daniel A. Bowen
- Division of Violence Prevention, National Center for Injury Prevention and Control, Centers for Disease Control and Prevention, Atlanta, Georgia
| | - Marissa Zwald
- Division of Violence Prevention, National Center for Injury Prevention and Control, Centers for Disease Control and Prevention, Atlanta, Georgia
| | - Jing Wang
- Division of Violence Prevention, National Center for Injury Prevention and Control, Centers for Disease Control and Prevention, Atlanta, Georgia
| | - Royal Law
- National Center for Environmental Health, Centers for Disease Control and Prevention, Atlanta, Georgia
| | - Jordan Taylor
- School of Interactive Computing, Georgia Institute of Technology, Atlanta
| | - Chaitanya Konjeti
- School of Interactive Computing, Georgia Institute of Technology, Atlanta
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Leuba SI, Yaesoubi R, Antillon M, Cohen T, Zimmer C. Tracking and predicting U.S. influenza activity with a real-time surveillance network. PLoS Comput Biol 2020; 16:e1008180. [PMID: 33137088 PMCID: PMC7707518 DOI: 10.1371/journal.pcbi.1008180] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2019] [Revised: 12/01/2020] [Accepted: 07/22/2020] [Indexed: 12/29/2022] Open
Abstract
Each year in the United States, influenza causes illness in 9.2 to 35.6 million individuals and is responsible for 12,000 to 56,000 deaths. The U.S. Centers for Disease Control and Prevention (CDC) tracks influenza activity through a national surveillance network. These data are only available after a delay of 1 to 2 weeks, and thus influenza epidemiologists and transmission modelers have explored the use of other data sources to produce more timely estimates and predictions of influenza activity. We evaluated whether data collected from a national commercial network of influenza diagnostic machines could produce valid estimates of the current burden and help to predict influenza trends in the United States. Quidel Corporation provided us with de-identified influenza test results transmitted in real-time from a national network of influenza test machines called the Influenza Test System (ITS). We used this ITS dataset to estimate and predict influenza-like illness (ILI) activity in the United States over the 2015-2016 and 2016-2017 influenza seasons. First, we developed linear logistic models on national and regional geographic scales that accurately estimated two CDC influenza metrics: the proportion of influenza test results that are positive and the proportion of physician visits that are ILI-related. We then used our estimated ILI-related proportion of physician visits in transmission models to produce improved predictions of influenza trends in the United States at both the regional and national scale. These findings suggest that ITS can be leveraged to improve "nowcasts" and short-term forecasts of U.S. influenza activity.
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Affiliation(s)
- Sequoia I. Leuba
- Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, CT, USA
| | - Reza Yaesoubi
- Health Policy and Management, Yale School of Public Health, New Haven, CT, USA
| | - Marina Antillon
- Household Economics and Health Systems Research Unit, Swiss Tropical and Public Health Institute, Basel, Switzerland
- University of Basel, Basel, Switzerland
| | - Ted Cohen
- Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, CT, USA
| | - Christoph Zimmer
- Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, CT, USA
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Cousins HC, Cousins CC, Harris A, Pasquale LR. Regional Infoveillance of COVID-19 Case Rates: Analysis of Search-Engine Query Patterns. J Med Internet Res 2020; 22:e19483. [PMID: 32692691 PMCID: PMC7394521 DOI: 10.2196/19483] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2020] [Revised: 07/13/2020] [Accepted: 07/19/2020] [Indexed: 01/25/2023] Open
Abstract
BACKGROUND Timely allocation of medical resources for coronavirus disease (COVID-19) requires early detection of regional outbreaks. Internet browsing data may predict case outbreaks in local populations that are yet to be confirmed. OBJECTIVE We investigated whether search-engine query patterns can help to predict COVID-19 case rates at the state and metropolitan area levels in the United States. METHODS We used regional confirmed case data from the New York Times and Google Trends results from 50 states and 166 county-based designated market areas (DMA). We identified search terms whose activity precedes and correlates with confirmed case rates at the national level. We used univariate regression to construct a composite explanatory variable based on best-fitting search queries offset by temporal lags. We measured the raw and z-transformed Pearson correlation and root-mean-square error (RMSE) of the explanatory variable with out-of-sample case rate data at the state and DMA levels. RESULTS Predictions were highly correlated with confirmed case rates at the state (mean r=0.69, 95% CI 0.51-0.81; median RMSE 1.27, IQR 1.48) and DMA levels (mean r=0.51, 95% CI 0.39-0.61; median RMSE 4.38, IQR 1.80), using search data available up to 10 days prior to confirmed case rates. They fit case-rate activity in 49 of 50 states and in 103 of 166 DMA at a significance level of .05. CONCLUSIONS Identifiable patterns in search query activity may help to predict emerging regional outbreaks of COVID-19, although they remain vulnerable to stochastic changes in search intensity.
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Affiliation(s)
- Henry C Cousins
- Department of Genetics, Stanford School of Medicine, Stanford, CA, United States
| | - Clara C Cousins
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, United States.,Department of Data Sciences, Dana-Farber Cancer Institute, Harvard TH Chan School of Public Health, Boston, MA, United States.,Center for Functional Cancer Epigenetics, Dana-Farber Cancer Institute, Boston, MA, United States
| | - Alon Harris
- Department of Ophthalmology, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Louis R Pasquale
- Department of Ophthalmology, Icahn School of Medicine at Mount Sinai, New York, NY, United States
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Vuorinen AL, Erkkola M, Fogelholm M, Kinnunen S, Saarijärvi H, Uusitalo L, Näppilä T, Nevalainen J. Characterization and Correction of Bias Due to Nonparticipation and the Degree of Loyalty in Large-Scale Finnish Loyalty Card Data on Grocery Purchases: Cohort Study. J Med Internet Res 2020; 22:e18059. [PMID: 32459633 PMCID: PMC7392131 DOI: 10.2196/18059] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2020] [Revised: 04/18/2020] [Accepted: 05/14/2020] [Indexed: 01/01/2023] Open
Abstract
Background To date, the evaluation of diet has mostly been based on questionnaires and diaries that have their limitations in terms of being time and resource intensive, and a tendency toward social desirability. Loyalty card data obtained in retailing provides timely and objective information on diet-related behaviors. In Finland, the market is highly concentrated, which provides a unique opportunity to investigate diet through grocery purchases. Objective The aims of this study were as follows: (1) to investigate and quantify the selection bias in large-scale (n=47,066) loyalty card (LoCard) data and correct the bias by developing weighting schemes and (2) to investigate how the degree of loyalty relates to food purchases. Methods Members of a loyalty card program from a large retailer in Finland were contacted via email and invited to take part in the study, which involved consenting to the release of their grocery purchase data for research purposes. Participants’ sociodemographic background was obtained through a web-based questionnaire and was compared to that of the general Finnish adult population obtained via Statistics Finland. To match the distributions of sociodemographic variables, poststratification weights were constructed by using the raking method. The degree of loyalty was self-estimated on a 5-point rating scale. Results On comparing our study sample with the general Finnish adult population, in our sample, there were more women (65.25%, 30,696/47,045 vs 51.12%, 2,273,139/4,446,869), individuals with higher education (56.91%, 20,684/36,348 vs 32.21%, 1,432,276/4,446,869), and employed individuals (60.53%, 22,086/36,487 vs 52.35%, 2,327,730/4,446,869). Additionally, in our sample, there was underrepresentation of individuals aged under 30 years (14.44%, 6,791/47,045 vs 18.04%, 802,295/4,446,869) and over 70 years (7.94%, 3,735/47,045 vs 18.20%, 809,317/4,446,869), as well as retired individuals (23.51%, 8,578/36,487 vs 31.82%, 1,414,785/4,446,869). Food purchases differed by the degree of loyalty, with higher shares of vegetable, red meat & processed meat, and fat spread purchases in the higher loyalty groups. Conclusions Individuals who consented to the use of their loyalty card data for research purposes tended to diverge from the general Finnish adult population. However, the high volume of data enabled the inclusion of sociodemographically diverse subgroups and successful correction of the differences found in the distributions of sociodemographic variables. In addition, it seems that food purchases differ according to the degree of loyalty, which should be taken into account when researching loyalty card data. Despite the limitations, loyalty card data provide a cost-effective approach to reach large groups of people, including hard-to-reach population subgroups.
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Affiliation(s)
- Anna-Leena Vuorinen
- Faculty of Social Sciences (Health Sciences), Tampere University, Tampere, Finland.,VTT Technical Research Centre of Finland Ltd, Tampere, Finland
| | - Maijaliisa Erkkola
- Department of Food and Nutrition, University of Helsinki, Helsinki, Finland
| | - Mikael Fogelholm
- Department of Food and Nutrition, University of Helsinki, Helsinki, Finland
| | - Satu Kinnunen
- Department of Food and Nutrition, University of Helsinki, Helsinki, Finland
| | - Hannu Saarijärvi
- Faculty of Management and Business, Tampere University, Tampere, Finland
| | - Liisa Uusitalo
- Department of Food and Nutrition, University of Helsinki, Helsinki, Finland
| | - Turkka Näppilä
- Tampere University Library, Tampere University, Tampere, Finland
| | - Jaakko Nevalainen
- Faculty of Social Sciences (Health Sciences), Tampere University, Tampere, Finland
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Sofiev M, Palamarchuk Y, Bédard A, Basagana X, Anto JM, Kouznetsov R, Urzua RD, Bergmann KC, Fonseca JA, De Vries G, Van Erd M, Annesi-Maesano I, Laune D, Pépin JL, Jullian-Desayes I, Zeng S, Czarlewski W, Bousquet J. A demonstration project of Global Alliance against Chronic Respiratory Diseases: Prediction of interactions between air pollution and allergen exposure-the Mobile Airways Sentinel NetworK-Impact of air POLLution on Asthma and Rhinitis approach. Chin Med J (Engl) 2020; 133:1561-1567. [PMID: 32649522 PMCID: PMC7386352 DOI: 10.1097/cm9.0000000000000916] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2020] [Indexed: 02/07/2023] Open
Abstract
This review analyzes the state and recent progress in the field of information support for pollen allergy sufferers. For decades, information available for the patients and allergologists consisted of pollen counts, which are vital but insufficient. New technology paves the way to substantial increase in amount and diversity of the data. This paper reviews old and newly suggested methods to predict pollen and air pollutant concentrations in the air and proposes an allergy risk concept, which combines the pollen and pollution information and transforms it into a qualitative risk index. This new index is available in an app (Mobile Airways Sentinel NetworK-air) that was developed in the frame of the European Union grant Impact of Air POLLution on sleep, Asthma and Rhinitis (a project of European Institute of Innovation and Technology-Health). On-going transformation of the pollen allergy information support is based on new technological solutions for pollen and air quality monitoring and predictions. The new information-technology and artificial-intelligence-based solutions help to convert this information into easy-to-use services for both medical practitioners and allergy sufferers.
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Affiliation(s)
- Mikhail Sofiev
- Finnish Meteorological Institute (FMI), Helsinki 00560, Finland
| | | | - Annabelle Bédard
- Barcelona Institute for Global Health, Centre for Research in Environmental Epidemiology (CREAL), Barcelona 08003, Spain
- Centro de Investigación Biomédica en Red de Epidemiología y Salud Pública (CIBER) Epidemiología y Salud Pública (CIBERESP), Barcelona 08003, Spain
- Universitat Pompeu Fabra (UPF), Barcelona 08003, Spain
| | - Xavier Basagana
- Barcelona Institute for Global Health, Centre for Research in Environmental Epidemiology (CREAL), Barcelona 08003, Spain
- Centro de Investigación Biomédica en Red de Epidemiología y Salud Pública (CIBER) Epidemiología y Salud Pública (CIBERESP), Barcelona 08003, Spain
- Universitat Pompeu Fabra (UPF), Barcelona 08003, Spain
- Institut Hospital del Mar d’Investigacions Mediques (IMIM), Barcelona 08003, Spain
| | - Josep M. Anto
- Barcelona Institute for Global Health, Centre for Research in Environmental Epidemiology (CREAL), Barcelona 08003, Spain
- Centro de Investigación Biomédica en Red de Epidemiología y Salud Pública (CIBER) Epidemiología y Salud Pública (CIBERESP), Barcelona 08003, Spain
- Universitat Pompeu Fabra (UPF), Barcelona 08003, Spain
- Institut Hospital del Mar d’Investigacions Mediques (IMIM), Barcelona 08003, Spain
| | | | | | - Karl Christian Bergmann
- Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Uniersität zu Berlin and Berlin Institute of Health, Comprehensive Allergy-Centre, Department of Dermatology and Allergy, Berlin 10117, Germany
| | - Joao A. Fonseca
- Center for Health Technology and Services Research (CINTESIS), Center for Research in Health Technology and Information Systems, Faculdade de Medicina da Universidade do Porto; and Medida, Lda Porto s/n 4200-450, Portugal
| | | | | | - Isabella Annesi-Maesano
- Epidemiology of Allergic and Respiratory Diseases Department, Institute Pierre Louis of Epidemiology and Public Health, INSERM and Sorbonne Université, Medical School Saint Antoine, Paris 75571, France
| | | | - Jean Louis Pépin
- Université Grenoble Alpes, Laboratoire HP2, Grenoble, INSERM, U1042 and CHU de Grenoble, Grenoble 38000, France
| | - Ingrid Jullian-Desayes
- Université Grenoble Alpes, Laboratoire HP2, Grenoble, INSERM, U1042 and CHU de Grenoble, Grenoble 38000, France
| | | | | | - Jean Bousquet
- University Hospital Montpellier, Montpellier 34000, France
- Contre les Maladies Chroniques pour un Vieillissement Actif en Languedoc Roussillon-France, Montpellier, France
- Charité, Universitätsmedizin Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Comprehensive Allergy Center, Department of Dermatology and Allergy, Berlin 10117, Germany
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Scarpino SV, Scott JG, Eggo RM, Clements B, Dimitrov NB, Meyers LA. Socioeconomic bias in influenza surveillance. PLoS Comput Biol 2020; 16:e1007941. [PMID: 32644990 PMCID: PMC7347107 DOI: 10.1371/journal.pcbi.1007941] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2018] [Accepted: 05/11/2020] [Indexed: 11/18/2022] Open
Abstract
Individuals in low socioeconomic brackets are considered at-risk for developing influenza-related complications and often exhibit higher than average influenza-related hospitalization rates. This disparity has been attributed to various factors, including restricted access to preventative and therapeutic health care, limited sick leave, and household structure. Adequate influenza surveillance in these at-risk populations is a critical precursor to accurate risk assessments and effective intervention. However, the United States of America's primary national influenza surveillance system (ILINet) monitors outpatient healthcare providers, which may be largely inaccessible to lower socioeconomic populations. Recent initiatives to incorporate Internet-source and hospital electronic medical records data into surveillance systems seek to improve the timeliness, coverage, and accuracy of outbreak detection and situational awareness. Here, we use a flexible statistical framework for integrating multiple surveillance data sources to evaluate the adequacy of traditional (ILINet) and next generation (BioSense 2.0 and Google Flu Trends) data for situational awareness of influenza across poverty levels. We find that ZIP Codes in the highest poverty quartile are a critical vulnerability for ILINet that the integration of next generation data fails to ameliorate.
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Affiliation(s)
- Samuel V. Scarpino
- Network Science Institute, Northeastern University, Boston, Massachusetts, United States of America
- Marine & Environmental Sciences, Northeastern University, Boston, Massachusetts, United States of America
- Physics, Northeastern University, Boston, Massachusetts, United States of America
- Health Sciences, Northeastern University, Boston, Massachusetts, United States of America
- ISI Foundation, Turin, Italy
| | - James G. Scott
- Department of Statistics and Data Sciences, The University of Texas at Austin, Austin, Texas, United States of America
| | - Rosalind M. Eggo
- Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Bruce Clements
- Pediatric Healthcare Connection, Austin, Texas, United States of America
| | - Nedialko B. Dimitrov
- Department of Operations Research, The University of Texas at Austin, Austin, Texas, United States of America
| | - Lauren Ancel Meyers
- Department of Integrative Biology, The University of Texas at Austin, Austin, Texas, United States of America
- Santa Fe Institute, Santa Fe, New Mexico, United States of America
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38
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Obeidat R, Alsmadi I, Bani Bakr Q, Obeidat L. Can Users Search Trends Predict People Scares or Disease Breakout? An Examination of Infectious Skin Diseases in the United States. Infect Dis (Lond) 2020; 13:1178633720928356. [PMID: 32565678 PMCID: PMC7285938 DOI: 10.1177/1178633720928356] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/29/2018] [Accepted: 04/29/2020] [Indexed: 11/17/2022] Open
Abstract
Background In health and medicine, people heavily use the Internet to search for information about symptoms, diseases, and treatments. As such, the Internet information can simulate expert medical doctors, pharmacists, and other health care providers. Aim This article aims to evaluate a dataset of search terms to determine whether search queries and terms can be used to reliably predict skin disease breakouts. Furthermore, the authors propose and evaluate a model to decide when to declare a particular month as Epidemic at the US national level. Methods A Model was designed to distinguish a breakout in skin diseases based on the number of monthly discovered cases. To apply this model, the authors correlated Google Trends of popular search terms with monthly reported Rubella and Measles cases from Centers for Disease Control and Prevention (CDC). Regressions and decision trees were used to determine the impact of different terms to trigger the occurrence of epidemic classes. Results Results showed that the volume of search keywords for Rubella and Measles rises when the volume of those reported diseases rises. Results also implied that the overall process was successful and should be repeated with other diseases. Such process can trigger different actions or activities to be taken when a certain month is declared as "Epidemic." Furthermore, this research has shown great interest for vaccination against Measles and Rubella. Conclusions The findings suggest that the search queries and keyword trends can be truly reliable to be used for the prediction of disease outbreaks and some other related knowledge extraction applications. Also search-term surveillance can provide an additional tool for infectious disease surveillance. Future research needs to re-apply the model used in this article, and researchers need to question whether characterizing the epidemiology of Coronavirus Disease 2019 (COVID-19) pandemic waves in United States can be done through search queries and keyword trends.
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Affiliation(s)
- Rand Obeidat
- Department of Management Information Systems, Bowie State University, Bowie, MD, USA
| | - Izzat Alsmadi
- Department of Computing and Cyber Security, Texas A&M University-San Antonio, San Antonio, TX, USA
| | - Qanita Bani Bakr
- Computer Science, Jordan University of Science and Technology, Irbid, Jordan
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Murayama T, Shimizu N, Fujita S, Wakamiya S, Aramaki E. Robust two-stage influenza prediction model considering regular and irregular trends. PLoS One 2020; 15:e0233126. [PMID: 32437380 PMCID: PMC7241782 DOI: 10.1371/journal.pone.0233126] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2019] [Accepted: 04/28/2020] [Indexed: 11/18/2022] Open
Abstract
Influenza causes numerous deaths worldwide every year. Predicting the number of influenza patients is an important task for medical institutions. Two types of data regarding influenza-like illnesses (ILIs) are often used for flu prediction: (1) historical data and (2) user generated content (UGC) data on the web such as search queries and tweets. Historical data have an advantage against the normal state but show disadvantages against irregular phenomena. In contrast, UGC data are advantageous for irregular phenomena. So far, no effective model providing the benefits of both types of data has been devised. This study proposes a novel model, designated the two-stage model, which combines both historical and UGC data. The basic idea is, first, basic regular trends are estimated using the historical data-based model, and then, irregular trends are predicted by the UGC data-based model. Our approach is practically useful because we can train models separately. Thus, if a UGC provider changes the service, our model could produce better performance because the first part of the model is still stable. Experiments on the US and Japan datasets demonstrated the basic feasibility of the proposed approach. In the dropout (pseudo-noise) test that assumes a UGC service would change, the proposed method also showed robustness against outliers. The proposed model is suitable for prediction of seasonal flu.
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Affiliation(s)
- Taichi Murayama
- Nara Institute of Science and Technology (NAIST), Ikoma-city, Japan
| | | | | | - Shoko Wakamiya
- Nara Institute of Science and Technology (NAIST), Ikoma-city, Japan
| | - Eiji Aramaki
- Nara Institute of Science and Technology (NAIST), Ikoma-city, Japan
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40
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Ziakas PD, Mylonakis E. Web search popularity, publicity, and utilization of direct oral anticoagulants in the United States, 2008-2018: A STROBE-compliant study. Medicine (Baltimore) 2020; 99:e20005. [PMID: 32384456 PMCID: PMC7220638 DOI: 10.1097/md.0000000000020005] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/25/2022] Open
Abstract
We aimed to study the changing popularity of oral anticoagulants and the potential association between media coverage and real-world utilization practice, using time series analysis.In this STROBE-compliant study, we used Google Trends data to study public interest for direct oral anticoagulants (DOACs) (dabigatran, rivaroxaban, apixaban, and edoxaban) and warfarin in the United States (10-year coverage, beginning July 1st, 2008 ending June 30th, 2018). We validated our findings on a sample of 50 consecutive datasets (accumulated between July 6th, 2018 and October 19th, 2018), using the same search criteria. We used the LexisNexis Academic database to quantify monthly media coverage for DOACs and explored its association with interest by the public, using the cross-correlation coefficient function. Finally, we studied the association between public interest and real-world utilization data, including published US-wide data on ambulatory anticoagulation visits.The approval of dabigatran in 2010 marked an increasing public interest for DOACs. Dabigatran exhibited a steep rise early after Food and Drug Administration approval that peaks in 2011, to be surpassed sequentially by rivaroxaban (2012) and apixaban (2014). Apixaban has outperformed its competitors in popularity since mid-2017, and, by the end of the observation period, was close to warfarin that is on first place. Media coverage was low before approval of the first oral DOAC (dabigatran), increased thereafter (median 13 news articles per month vs 64, P < .001), with peaks on the approval dates (81 vs 48, P = .003). Media coverage had a weak immediate impact on DOACs public interest and public interest patterns preceded changes in ambulatory anticoagulation visits by up to 5 months.For a long-run observation period, a single Google Trends search will suffice to produce robust estimations of the relative popularity between treatment options, such as oral anticoagulants. Media coverage has limited immediate impact and relative public interest is a potential lead indicator of changes in actual utilization.
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41
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Kissler SM, Viboud C, Grenfell BT, Gog JR. Symbolic transfer entropy reveals the age structure of pandemic influenza transmission from high-volume influenza-like illness data. J R Soc Interface 2020; 17:20190628. [PMID: 32183640 PMCID: PMC7115222 DOI: 10.1098/rsif.2019.0628] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022] Open
Abstract
Existing methods to infer the relative roles of age groups in epidemic transmission can normally only accommodate a few age classes, and/or require data that are highly specific for the disease being studied. Here, symbolic transfer entropy (STE), a measure developed to identify asymmetric transfer of information between stochastic processes, is presented as a way to reveal asymmetric transmission patterns between age groups in an epidemic. STE provides a ranking of which age groups may dominate transmission, rather than a reconstruction of the explicit between-age-group transmission matrix. Using simulations, we establish that STE can identify which age groups dominate transmission even when there are differences in reporting rates between age groups and even if the data are noisy. Then, the pairwise STE is calculated between time series of influenza-like illness for 12 age groups in 884 US cities during the autumn of 2009. Elevated STE from 5 to 19 year-olds indicates that school-aged children were likely the most important transmitters of infection during the autumn wave of the 2009 pandemic in the USA. The results may be partially confounded by higher rates of physician-seeking behaviour in children compared to adults, but it is unlikely that differences in reporting rates can explain the observed differences in STE.
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Affiliation(s)
- Stephen M Kissler
- Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Wilberforce Road, Cambridge, UK.,Department of Immunology and Infectious Diseases, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Cécile Viboud
- Fogarty International Center, National Institutes of Health, Bethesda, MA, USA
| | - Bryan T Grenfell
- Department of Ecology and Evolutionary Biology, University of Princeton, Princeton, NJ, USA
| | - Julia R Gog
- Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Wilberforce Road, Cambridge, UK
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42
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Comparing Social media and Google to detect and predict severe epidemics. Sci Rep 2020; 10:4747. [PMID: 32179780 PMCID: PMC7076014 DOI: 10.1038/s41598-020-61686-9] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2019] [Accepted: 02/27/2020] [Indexed: 11/16/2022] Open
Abstract
Internet technologies have demonstrated their value for the early detection and prediction of epidemics. In diverse cases, electronic surveillance systems can be created by obtaining and analyzing on-line data, complementing other existing monitoring resources. This paper reports the feasibility of building such a system with search engine and social network data. Concretely, this study aims at gathering evidence on which kind of data source leads to better results. Data have been acquired from the Internet by means of a system which gathered real-time data for 23 weeks. Data on influenza in Greece have been collected from Google and Twitter and they have been compared to influenza data from the official authority of Europe. The data were analyzed by using two models: the ARIMA model computed estimations based on weekly sums and a customized approximate model which uses daily sums. Results indicate that influenza was successfully monitored during the test period. Google data show a high Pearson correlation and a relatively low Mean Absolute Percentage Error (R = 0.933, MAPE = 21.358). Twitter results are slightly better (R = 0.943, MAPE = 18.742). The alternative model is slightly worse than the ARIMA(X) (R = 0.863, MAPE = 22.614), but with a higher mean deviation (abs. mean dev: 5.99% vs 4.74%).
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43
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Barros JM, Duggan J, Rebholz-Schuhmann D. The Application of Internet-Based Sources for Public Health Surveillance (Infoveillance): Systematic Review. J Med Internet Res 2020; 22:e13680. [PMID: 32167477 PMCID: PMC7101503 DOI: 10.2196/13680] [Citation(s) in RCA: 50] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2019] [Revised: 09/18/2019] [Accepted: 11/26/2019] [Indexed: 12/30/2022] Open
Abstract
Background 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. Objective 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. 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. Results 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. Conclusions 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.
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Affiliation(s)
- Joana M Barros
- Insight Centre for Data Analytics, National University of Ireland Galway, Galway, Ireland.,School of Computer Science, National University of Ireland Galway, Galway, Ireland
| | - Jim Duggan
- School of Computer Science, National University of Ireland Galway, Galway, Ireland
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Big Data Analytics and Processing Platform in Czech Republic Healthcare. APPLIED SCIENCES-BASEL 2020. [DOI: 10.3390/app10051705] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Big data analytics (BDA) in healthcare has made a positive difference in the integration of Artificial Intelligence (AI) in advancements of analytical capabilities, while lowering the costs of medical care. The aim of this study is to improve the existing healthcare eSystem by implementing a Big Data Analytics (BDA) platform and to meet the requirements of the Czech Republic National Health Service (Tender-Id. VZ0036628, No. Z2017-035520). In addition to providing analytical capabilities on Linux platforms supporting current and near-future AI with machine-learning and data-mining algorithms, there is the need for ethical considerations mandating new ways to preserve privacy, all of which are preconditioned by the growing body of regulations and expectations. The presented BDA platform, has met all requirements (N > 100), including the healthcare industry-standard Transaction Processing Performance Council (TPC-H) decision support benchmark in compliance with the European Union (EU) and the Czech Republic legislations. Currently, the presented Proof of Concept (PoC) that has been upgraded to a production environment has unified isolated parts of Czech Republic healthcare over the past seven months. The reported PoC BDA platform, artefacts, and concepts are transferrable to healthcare systems in other countries interested in developing or upgrading their own national healthcare infrastructure in a cost-effective, secure, scalable and high-performance manner.
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45
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Crowson MG, Witsell D, Eskander A. Using Google Trends to Predict Pediatric Respiratory Syncytial Virus Encounters at a Major Health Care System. J Med Syst 2020; 44:57. [PMID: 31997013 DOI: 10.1007/s10916-020-1526-8] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2019] [Accepted: 01/22/2020] [Indexed: 10/25/2022]
Abstract
To assess whether Google search activity predicts lead-time for pediatric respiratory syncytial virus (RSV) encounters within a major health care system. Internet user search and health system encounter database analysis. Pediatric RSV encounter volumes across all clinics and hospitals in the Duke Health system were tabulated from 2005 to 2016. North Carolina Google user search activity for RSV were obtained over the same time period. Time series analysis was used to compare RSV encounters and search activity. Cross-correlation was used to determine the 'lag' time difference between Google user search interest for RSV and observed Pediatric RSV encounter volumes. Google search activity and Pediatric RSV encounter volumes demonstrated strong seasonality with predilection for winter months. Granger Causality testing revealed that North Carolina RSV Google search activity can predict pediatric RSV encounters at our health system (F = 5.72, p < 0.0001). Using cross-correlation, increases in Google search activity provided lead time of 0.21 weeks (1.47 days) prior to observed increases in Pediatric RSV encounter volumes at our health system. RSV is a common cause of upper airway obstruction in pediatric patients for which pediatric otolaryngologists are consulted. We demonstrate that Google search activity can predict RSV patient interactions with a major health system with a measurable lead-time. The ability to predict when illnesses in a population result in increased health care utilization would be an asset to health system providers, planners and administrators. Prediction of RSV would allow specific care pathways to be developed and resource needs to be anticipated before actual presentation.
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Affiliation(s)
- Matthew G Crowson
- Department of Otolaryngology-Head & Neck Surgery, Sunnybrook Health Sciences Centre, Toronto, Ontario, M4N 3N5, Canada.
| | - David Witsell
- Division of Otolaryngology-Head & Neck Surgery, Duke University Medical Center, Durham, NC, USA
| | - Antoine Eskander
- Department of Otolaryngology-Head & Neck Surgery, Sunnybrook Health Sciences Centre, Toronto, Ontario, M4N 3N5, Canada
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Ackley SF, Pilewski S, Petrovic VS, Worden L, Murray E, Porco TC. Assessing the utility of a smart thermometer and mobile application as a surveillance tool for influenza and influenza-like illness. Health Informatics J 2020; 26:2148-2158. [PMID: 31969046 DOI: 10.1177/1460458219897152] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Kinsa Inc. sells Food and Drug Administration-cleared smart thermometers, which synchronize with a mobile application, and may aid influenza forecasting efforts. We compare smart thermometer and mobile application data to regional influenza and influenza-like illness surveillance data from the California Department of Public Health. We evaluated the correlation between the regional California surveillance data and smart thermometer data, tested the hypothesis that smart thermometer readings and symptom reports provide regionally specific predictions, and determined whether smart thermometer and mobile application improved disease forecasts. Smart thermometer readings are highly correlated with regional surveillance data, are more predictive of surveillance data for their own region and season than for other times and places, and improve predictions of influenza, but not predictions of influenza-like illness. These results are consistent with the hypothesis that smart thermometer readings and symptom reports reflect underlying disease transmission in California. Data from such cloud-based devices could supplement syndromic influenza surveillance data.
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Affiliation(s)
| | | | | | - Lee Worden
- University of California, San Francisco, USA
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Harnessing wearable device data to improve state-level real-time surveillance of influenza-like illness in the USA: a population-based study. LANCET DIGITAL HEALTH 2020; 2:e85-e93. [PMID: 33334565 DOI: 10.1016/s2589-7500(19)30222-5] [Citation(s) in RCA: 149] [Impact Index Per Article: 37.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/23/2019] [Revised: 12/02/2019] [Accepted: 12/06/2019] [Indexed: 01/01/2023]
Abstract
BACKGROUND Acute infections can cause an individual to have an elevated resting heart rate (RHR) and change their routine daily activities due to the physiological response to the inflammatory insult. Consequently, we aimed to evaluate if population trends of seasonal respiratory infections, such as influenza, could be identified through wearable sensors that collect RHR and sleep data. METHODS We obtained de-identified sensor data from 200 000 individuals who used a Fitbit wearable device from March 1, 2016, to March 1, 2018, in the USA. We included users who wore a Fitbit for at least 60 days and used the same wearable throughout the entire period, and focused on the top five states with the most Fitbit users in the dataset: California, Texas, New York, Illinois, and Pennsylvania. Inclusion criteria included having a self-reported birth year between 1930 and 2004, height greater than 1 m, and weight greater than 20 kg. We excluded daily measurements with missing RHR, missing wear time, and wear time less than 1000 min per day. We compared sensor data with weekly estimates of influenza-like illness (ILI) rates at the state level, as reported by the US Centers for Disease Control and Prevention (CDC), by identifying weeks in which Fitbit users displayed elevated RHRs and increased sleep levels. For each state, we modelled ILI case counts with a negative binomial model that included 3-week lagged CDC ILI rate data (null model) and the proportion of weekly Fitbit users with elevated RHR and increased sleep duration above a specified threshold (full model). We also evaluated weekly change in ILI rate by linear regression using change in proportion of elevated Fitbit data. Pearson correlation was used to compare predicted versus CDC reported ILI rates. FINDINGS We identified 47 249 users in the top five states who wore a Fitbit consistently during the study period, including more than 13·3 million total RHR and sleep measures. We found the Fitbit data significantly improved ILI predictions in all five states, with an average increase in Pearson correlation of 0·12 (SD 0·07) over baseline models, corresponding to an improvement of 6·3-32·9%. Correlations of the final models with the CDC ILI rates ranged from 0·84 to 0·97. Week-to-week changes in the proportion of Fitbit users with abnormal data were associated with week-to-week changes in ILI rates in most cases. INTERPRETATION Activity and physiological trackers are increasingly used in the USA and globally to monitor individual health. By accessing these data, it could be possible to improve real-time and geographically refined influenza surveillance. This information could be vital to enact timely outbreak response measures to prevent further transmission of influenza cases during outbreaks. FUNDING Partly supported by the US National Institutes of Health National Center for Advancing Translational Sciences.
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Aiello AE, Renson A, Zivich PN. Social Media- and Internet-Based Disease Surveillance for Public Health. Annu Rev Public Health 2020; 41:101-118. [PMID: 31905322 DOI: 10.1146/annurev-publhealth-040119-094402] [Citation(s) in RCA: 106] [Impact Index Per Article: 26.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Abstract
Disease surveillance systems are a cornerstone of public health tracking and prevention. This review addresses the use, promise, perils, and ethics of social media- and Internet-based data collection for public health surveillance. Our review highlights untapped opportunities for integrating digital surveillance in public health and current applications that could be improved through better integration, validation, and clarity on rules surrounding ethical considerations. Promising developments include hybrid systems that couple traditional surveillance data with data from search queries, social media posts, and crowdsourcing. In the future, it will be important to identify opportunities for public and private partnerships, train public health experts in data science, reduce biases related to digital data (gathered from Internet use, wearable devices, etc.), and address privacy. We are on the precipice of an unprecedented opportunity to track, predict, and prevent global disease burdens in the population using digital data.
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Affiliation(s)
- Allison E Aiello
- Department of Epidemiology, Carolina Population Center, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599-7435, USA; , ,
| | - Audrey Renson
- Department of Epidemiology, Carolina Population Center, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599-7435, USA; , ,
| | - Paul N Zivich
- Department of Epidemiology, Carolina Population Center, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599-7435, USA; , ,
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Samaras L, García-Barriocanal E, Sicilia MA. Syndromic surveillance using web data: a systematic review. INNOVATION IN HEALTH INFORMATICS 2020. [PMCID: PMC7153324 DOI: 10.1016/b978-0-12-819043-2.00002-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
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.
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Rains SA. Big Data, Computational Social Science, and Health Communication: A Review and Agenda for Advancing Theory. HEALTH COMMUNICATION 2020; 35:26-34. [PMID: 30351198 DOI: 10.1080/10410236.2018.1536955] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
Contemporary research on health communication has been marked by the presence of big data and computational social science (CSS) techniques. The relative novelty of these approaches makes it worthwhile to consider their status and potential for advancing health communication scholarship. This essay offers an introduction focusing on how big data and CSS techniques are being employed to study health communication and their utility for theory development. Key trends in this body of research are summarized, including the use of big data and CSS for examining public perceptions of health conditions or events, investigating network-related dimensions of health phenomena, and illness monitoring. The implications of big data and CSS for health communication theory are also evaluated. Opportunities presented by big data and CSS to help extend existing theories and build new communication theories are discussed.
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