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Wynn M. Online spaces and the control of communicable diseases: implications for nursing practice. Nurs Stand 2024; 39:39-44. [PMID: 38369909 DOI: 10.7748/ns.2024.e12174] [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] [Accepted: 07/04/2023] [Indexed: 02/20/2024]
Abstract
The digital revolution has significantly altered healthcare, including communicable disease control, with online spaces emerging as vital tools in preventing, identifying and controlling the spread of diseases. However, healthcare professionals, including nurses, need to find a balance between harnessing the benefits of mass communication and mitigating the potentially harmful effects of online misinformation. This article explores the benefits and challenges of using online spaces such as social media platforms in the control of communicable diseases and discusses the potential use of telehealth in reducing the risk of healthcare-associated infection and antimicrobial resistance. The author also describes a framework that nurses can use to explore potential roles and practice in the context of communicable disease control in online spaces.
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Geng X, Ma Y, Cai W, Zha Y, Zhang T, Zhang H, Yang C, Yin F, Shui T. Evaluation of models for multi-step forecasting of hand, foot and mouth disease using multi-input multi-output: A case study of Chengdu, China. PLoS Negl Trop Dis 2023; 17:e0011587. [PMID: 37683009 PMCID: PMC10511093 DOI: 10.1371/journal.pntd.0011587] [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: 01/18/2023] [Revised: 09/20/2023] [Accepted: 08/11/2023] [Indexed: 09/10/2023] Open
Abstract
BACKGROUND Hand, foot and mouth disease (HFMD) is a public health concern that threatens the health of children. Accurately forecasting of HFMD cases multiple days ahead and early detection of peaks in the number of cases followed by timely response are essential for HFMD prevention and control. However, many studies mainly predict future one-day incidence, which reduces the flexibility of prevention and control. METHODS We collected the daily number of HFMD cases among children aged 0-14 years in Chengdu from 2011 to 2017, as well as meteorological and air pollutant data for the same period. The LSTM, Seq2Seq, Seq2Seq-Luong and Seq2Seq-Shih models were used to perform multi-step prediction of HFMD through multi-input multi-output. We evaluated the models in terms of overall prediction performance, the time delay and intensity of detection peaks. RESULTS From 2011 to 2017, HFMD in Chengdu showed seasonal trends that were consistent with temperature, air pressure, rainfall, relative humidity, and PM10. The Seq2Seq-Shih model achieved the best performance, with RMSE, sMAPE and PCC values of 13.943~22.192, 17.880~27.937, and 0.887~0.705 for the 2-day to 15-day predictions, respectively. Meanwhile, the Seq2Seq-Shih model is able to detect peaks in the next 15 days with a smaller time delay. CONCLUSIONS The deep learning Seq2Seq-Shih model achieves the best performance in overall and peak prediction, and is applicable to HFMD multi-step prediction based on environmental factors.
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Affiliation(s)
- Xiaoran Geng
- West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
| | - Yue Ma
- West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
| | - Wennian Cai
- West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
| | - Yuanyi Zha
- Kunming Medical University, Kunming, China
| | - Tao Zhang
- West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
| | - Huadong Zhang
- Chongqing Center for Disease Control and Prevention, Chongqing, China
| | - Changhong Yang
- Sichuan Center for Disease Control and Prevention, Chengdu, China
| | - Fei Yin
- West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
| | - Tiejun Shui
- Yunnan Center for Disease Control and Prevention, Kunming, China
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3
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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: 0] [Impact Index Per Article: 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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Liu J, Suzuki S. Real-Time Detection of Flu Season Onset: A Novel Approach to Flu Surveillance. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:ijerph19063681. [PMID: 35329368 PMCID: PMC8950522 DOI: 10.3390/ijerph19063681] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/30/2022] [Revised: 03/12/2022] [Accepted: 03/17/2022] [Indexed: 11/16/2022]
Abstract
The current gold standard for detection of flu season onset in the USA is done retrospectively, where flu season is detected after it has already started. We aimed to create a new surveillance strategy capable of detecting flu season onset prior to its starting. We used an established data generation method that combines Google search volume and historical flu activity data to simulate real-time estimates of flu activity. We then applied a method known as change-point detection to the generated data to determine the point in time that identifies the initial uptick in flu activity which indicates the imminent onset of flu season. Our strategy exhibits a high level of accuracy in predicting the onset of flu season at 86%. Additionally, on average, we detected the onset three weeks prior to the official start of flu season. The results provide evidence to support both the feasibility and efficacy of our strategy to improve the current standard of flu surveillance. The improvement may provide valuable support and lead time for public health officials to take appropriate actions to prevent and control the spread of the flu.
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Affiliation(s)
- Jialiang Liu
- Department of Epidemiology and Biostatistics, College of Public Health, Temple University, Philadelphia, PA 19122, USA
- Correspondence: or
| | - Sumihiro Suzuki
- Department of Preventive Medicine, Rush University Medical Center, Chicago, IL 60612, USA;
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5
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Yang S, Bao Y. Comprehensive learning particle swarm optimization enabled modeling framework for multi-step-ahead influenza prediction. Appl Soft Comput 2021. [DOI: 10.1016/j.asoc.2021.107994] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
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6
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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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7
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Pei S, Teng X, Lewis P, Shaman J. Optimizing respiratory virus surveillance networks using uncertainty propagation. Nat Commun 2021; 12:222. [PMID: 33431854 PMCID: PMC7801666 DOI: 10.1038/s41467-020-20399-3] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2019] [Accepted: 12/01/2020] [Indexed: 02/07/2023] Open
Abstract
Infectious disease prevention, control and forecasting rely on sentinel observations; however, many locations lack the capacity for routine surveillance. Here we show that, by using data from multiple sites collectively, accurate estimation and forecasting of respiratory diseases for locations without surveillance is feasible. We develop a framework to optimize surveillance sites that suppresses uncertainty propagation in a networked disease transmission model. Using influenza outbreaks from 35 US states, the optimized system generates better near-term predictions than alternate systems designed using population and human mobility. We also find that monitoring regional population centers serves as a reasonable proxy for the optimized network and could direct surveillance for diseases with limited records. The proxy method is validated using model simulations for 3,108 US counties and historical data for two other respiratory pathogens - human metapneumovirus and seasonal coronavirus - from 35 US states and can be used to guide systemic allocation of surveillance efforts.
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Affiliation(s)
- Sen Pei
- Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, NY, 10032, USA.
| | - Xian Teng
- School of Computing and Information, University of Pittsburgh, Pittsburgh, PA, 15260, USA
| | - Paul Lewis
- Integrated Biosurveillance Section, Armed Forces Health Surveillance Branch, Silver Spring, MD, 20904, USA
| | - Jeffrey Shaman
- Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, NY, 10032, USA.
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8
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Cheng HY, Wu YC, Lin MH, Liu YL, Tsai YY, Wu JH, Pan KH, Ke CJ, Chen CM, Liu DP, Lin IF, Chuang JH. Applying Machine Learning Models with An Ensemble Approach for Accurate Real-Time Influenza Forecasting in Taiwan: Development and Validation Study. J Med Internet Res 2020; 22:e15394. [PMID: 32755888 PMCID: PMC7439145 DOI: 10.2196/15394] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2019] [Revised: 12/21/2019] [Accepted: 06/13/2020] [Indexed: 12/14/2022] Open
Abstract
Background Changeful seasonal influenza activity in subtropical areas such as Taiwan causes problems in epidemic preparedness. The Taiwan Centers for Disease Control has maintained real-time national influenza surveillance systems since 2004. Except for timely monitoring, epidemic forecasting using the national influenza surveillance data can provide pivotal information for public health response. Objective We aimed to develop predictive models using machine learning to provide real-time influenza-like illness forecasts. Methods Using surveillance data of influenza-like illness visits from emergency departments (from the Real-Time Outbreak and Disease Surveillance System), outpatient departments (from the National Health Insurance database), and the records of patients with severe influenza with complications (from the National Notifiable Disease Surveillance System), we developed 4 machine learning models (autoregressive integrated moving average, random forest, support vector regression, and extreme gradient boosting) to produce weekly influenza-like illness predictions for a given week and 3 subsequent weeks. We established a framework of the machine learning models and used an ensemble approach called stacking to integrate these predictions. We trained the models using historical data from 2008-2014. We evaluated their predictive ability during 2015-2017 for each of the 4-week time periods using Pearson correlation, mean absolute percentage error (MAPE), and hit rate of trend prediction. A dashboard website was built to visualize the forecasts, and the results of real-world implementation of this forecasting framework in 2018 were evaluated using the same metrics. Results All models could accurately predict the timing and magnitudes of the seasonal peaks in the then-current week (nowcast) (ρ=0.802-0.965; MAPE: 5.2%-9.2%; hit rate: 0.577-0.756), 1-week (ρ=0.803-0.918; MAPE: 8.3%-11.8%; hit rate: 0.643-0.747), 2-week (ρ=0.783-0.867; MAPE: 10.1%-15.3%; hit rate: 0.669-0.734), and 3-week forecasts (ρ=0.676-0.801; MAPE: 12.0%-18.9%; hit rate: 0.643-0.786), especially the ensemble model. In real-world implementation in 2018, the forecasting performance was still accurate in nowcasts (ρ=0.875-0.969; MAPE: 5.3%-8.0%; hit rate: 0.582-0.782) and remained satisfactory in 3-week forecasts (ρ=0.721-0.908; MAPE: 7.6%-13.5%; hit rate: 0.596-0.904). Conclusions This machine learning and ensemble approach can make accurate, real-time influenza-like illness forecasts for a 4-week period, and thus, facilitate decision making.
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Affiliation(s)
| | | | - Min-Hau Lin
- Taiwan Centers for Disease Control, Taipei, Taiwan
| | - Yu-Lun Liu
- Taiwan Centers for Disease Control, Taipei, Taiwan
| | | | - Jo-Hua Wu
- Value Lab, Acer Inc., Taipei, Taiwan
| | | | - Chih-Jung Ke
- Taiwan Centers for Disease Control, Taipei, Taiwan
| | | | - Ding-Ping Liu
- Taiwan Centers for Disease Control, Taipei, Taiwan.,National Taipei University of Nursing and Health Sciences, Taipei, Taiwan
| | - I-Feng Lin
- Institute of Public Health, National Yang-Ming University, Taipei, Taiwan
| | - Jen-Hsiang Chuang
- Taiwan Centers for Disease Control, Taipei, Taiwan.,Institute of Public Health, National Yang-Ming University, Taipei, Taiwan
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9
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Gupta A, Katarya R. Social media based surveillance systems for healthcare using machine learning: A systematic review. J Biomed Inform 2020; 108:103500. [PMID: 32622833 PMCID: PMC7331523 DOI: 10.1016/j.jbi.2020.103500] [Citation(s) in RCA: 54] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2019] [Revised: 06/21/2020] [Accepted: 06/26/2020] [Indexed: 11/30/2022]
Abstract
BACKGROUND Real-time surveillance in the field of health informatics has emerged as a growing domain of interest among worldwide researchers. Evolution in this field has helped in the introduction of various initiatives related to public health informatics. Surveillance systems in the area of health informatics utilizing social media information have been developed for early prediction of disease outbreaks and to monitor diseases. In the past few years, the availability of social media data, particularly Twitter data, enabled real-time syndromic surveillance that provides immediate analysis and instant feedback to those who are charged with follow-ups and investigation of potential outbreaks. In this paper, we review the recent work, trends, and machine learning(ML) text classification approaches used by surveillance systems seeking social media data in the healthcare domain. We also highlight the limitations and challenges followed by possible future directions that can be taken further in this domain. METHODS To study the landscape of research in health informatics performing surveillance of the various health-related data posted on social media or web-based platforms, we present a bibliometric analysis of the 1240 publications indexed in multiple scientific databases (IEEE, ACM Digital Library, ScienceDirect, PubMed) from the year 2010-2018. The papers were further reviewed based on the various machine learning algorithms used for analyzing health-related text posted on social media platforms. FINDINGS Based on the corpus of 148 selected articles, the study finds the types of social media or web-based platforms used for surveillance in the healthcare domain, along with the health topic(s) studied by them. In the corpus of selected articles, we found 26 articles were using machine learning technique. These articles were studied to find commonly used ML techniques. The majority of studies (24%) focused on the surveillance of flu or influenza-like illness (ILI). Twitter (64%) is the most popular data source to perform surveillance research using social media text data, and Support Vector Machine (SVM) (33%) being the most used ML algorithm for text classification. CONCLUSIONS The inclusion of online data in surveillance systems has improved the disease prediction ability over traditional syndromic surveillance systems. However, social media based surveillance systems have many limitations and challenges, including noise, demographic bias, privacy issues, etc. Our paper mentions future directions, which can be useful for researchers working in the area. Researchers can use this paper as a library for social media based surveillance systems in the healthcare domain and can expand such systems by incorporating the future works discussed in our paper.
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10
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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. Public health agencies maintain increasingly sophisticated surveillance systems, which integrate diverse data streams within limited budgets. Here we develop a method to design robust and efficient forecasting systems for influenza hospitalizations. With these forecasting models, we find support for a key data gap namely that the USA’s public health surveillance data sets are much more representative of higher socioeconomic sub-populations and perform poorly for the most at-risk communities. Thus, our study highlights another related socioeconomic inequity—a reduced capability to monitor outbreaks in at-risk populations—which impedes effective public health interventions.
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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
- * E-mail:
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11
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Darwish A, Rahhal Y, Jafar A. A comparative study on predicting influenza outbreaks using different feature spaces: application of influenza-like illness data from Early Warning Alert and Response System in Syria. BMC Res Notes 2020; 13:33. [PMID: 31948473 PMCID: PMC6964210 DOI: 10.1186/s13104-020-4889-5] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2019] [Accepted: 01/03/2020] [Indexed: 11/10/2022] Open
Abstract
Objective An accurate forecasting of outbreaks of influenza-like illness (ILI) could support public health officials to suggest public health actions earlier. We investigated the performance of three different feature spaces in different models to forecast the weekly ILI rate in Syria using EWARS data from World Health Organization (WHO). Time series feature space was first used and we applied the seven models which are Naïve, Average, Seasonal naïve, drift, dynamic harmonic regression (Dhr), seasonal and trend decomposition using loess (STL) and TBATS. The Second feature space is like some state-of-the-art, which we named \documentclass[12pt]{minimal}
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\begin{document}$$53-weeks-before\_52-first-order-difference$$\end{document}53-weeks-before_52-first-order-difference feature space. The third one, we proposed and named \documentclass[12pt]{minimal}
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\begin{document}$$n-years-before\_m-weeks-around$$\end{document}n-years-before_m-weeks-around (YnWm) feature space. Machine learning (ML) and deep learning (DL) model were applied to the second and third feature spaces (generalized linear model (GLM), support vector regression (SVR), gradient boosting (GB), random forest (RF) and long short term memory (LSTM)). Results It was indicated that the LSTM model of four layers with \documentclass[12pt]{minimal}
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\begin{document}$$1-year-before\_4-weeks-around$$\end{document}1-year-before_4-weeks-around feature space gave more accurate results than other models and reached the lowest MAPE of \documentclass[12pt]{minimal}
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\begin{document}$$3.52\%$$\end{document}3.52% and the lowest RMSE of 0.01662. I hope that this modelling methodology can be applied in other countries and therefore help prevent and control influenza worldwide.
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Affiliation(s)
- Ali Darwish
- Department of Informatics, Higher Institute for Applied Sciences and Technology, Damascus, Syria.
| | - Yasser Rahhal
- Department of Informatics, Higher Institute for Applied Sciences and Technology, Damascus, Syria
| | - Assef Jafar
- Department of Informatics, Higher Institute for Applied Sciences and Technology, Damascus, Syria
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12
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Rangarajan P, Mody SK, Marathe M. Forecasting dengue and influenza incidences using a sparse representation of Google trends, electronic health records, and time series data. PLoS Comput Biol 2019; 15:e1007518. [PMID: 31751346 PMCID: PMC6894887 DOI: 10.1371/journal.pcbi.1007518] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2019] [Revised: 12/05/2019] [Accepted: 10/29/2019] [Indexed: 12/20/2022] Open
Abstract
Dengue and influenza-like illness (ILI) are two of the leading causes of viral infection in the world and it is estimated that more than half the world’s population is at risk for developing these infections. It is therefore important to develop accurate methods for forecasting dengue and ILI incidences. Since data from multiple sources (such as dengue and ILI case counts, electronic health records and frequency of multiple internet search terms from Google Trends) can improve forecasts, standard time series analysis methods are inadequate to estimate all the parameter values from the limited amount of data available if we use multiple sources. In this paper, we use a computationally efficient implementation of the known variable selection method that we call the Autoregressive Likelihood Ratio (ARLR) method. This method combines sparse representation of time series data, electronic health records data (for ILI) and Google Trends data to forecast dengue and ILI incidences. This sparse representation method uses an algorithm that maximizes an appropriate likelihood ratio at every step. Using numerical experiments, we demonstrate that our method recovers the underlying sparse model much more accurately than the lasso method. We apply our method to dengue case count data from five countries/states: Brazil, Mexico, Singapore, Taiwan, and Thailand and to ILI case count data from the United States. Numerical experiments show that our method outperforms existing time series forecasting methods in forecasting the dengue and ILI case counts. In particular, our method gives a 18 percent forecast error reduction over a leading method that also uses data from multiple sources. It also performs better than other methods in predicting the peak value of the case count and the peak time. Dengue and influenza-like illness (ILI) are leading causes of viral infection in the world and hence it is important to develop accurate methods for forecasting their incidence. We use Autoregressive Likelihood Ratio method, which is a computationally efficient implementation of the variable selection method, in order to obtain a sparse (non-lasso) representation of time series, Google Trends and electronic health records (for ILI) data. This method is used to forecast dengue incidence in five countries/states and ILI incidence in USA. We show that this method outperforms existing time series methods in forecasting these diseases. The method is general and can also be used to forecast other diseases.
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Affiliation(s)
- Prashant Rangarajan
- Departments of Computer Science and Mathematics, Birla Institute of Technology and Science, Pilani, India
| | - Sandeep K. Mody
- Department of Mathematics, Indian Institute of Science, Bangalore, India
| | - Madhav Marathe
- Department of Computer Science, Network, Simulation Science and Advanced Computing Division, Biocomplexity Institute, University of Virginia, Charlottesville, Virginia, United States of America
- * E-mail:
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13
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Abstract
OBJECTIVES To introduce and summarize current research in the field of Public Health and Epidemiology Informatics. METHODS The 2018 literature concerning public health and epidemiology informatics was searched in PubMed and Web of Science, and the returned references were reviewed by the two section editors to select 15 candidate best papers. These papers were then peer-reviewed by external reviewers to give the editorial team an enlightened selection of the best papers. RESULTS Among the 805 references retrieved from PubMed and Web of Science, three were finally selected as best papers. All three papers are about surveillance using digital tools. One study is about the surveillance of flu, another about emerging animal infectious diseases and the last one is about foodborne illness. The sources of information are Google news, Twitter, and Yelp restaurant reviews. Machine learning approaches are most often used to detect signals. CONCLUSIONS Surveillance is a central topic in public health informatics with the growing use of machine learning approaches in regards of the size and complexity of data. The evaluation of the approaches developed remains a serious challenge.
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Affiliation(s)
- Rodolphe Thiébaut
- Univ. Bordeaux, Inserm, Bordeaux Population Health Research Center, UMR 1219, Bordeaux, France.,Centre Hospitalier Universitaire de Bordeaux, Service d'Information Médicale, Bordeaux, France.,Inria, SISTM, Talence, France
| | - Sébastien Cossin
- Univ. Bordeaux, Inserm, Bordeaux Population Health Research Center, UMR 1219, Bordeaux, France.,Centre Hospitalier Universitaire de Bordeaux, Service d'Information Médicale, Bordeaux, France
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Drake JM, Brett TS, Chen S, Epureanu BI, Ferrari MJ, Marty É, Miller PB, O’Dea EB, O’Regan SM, Park AW, Rohani P. The statistics of epidemic transitions. PLoS Comput Biol 2019; 15:e1006917. [PMID: 31067217 PMCID: PMC6505855 DOI: 10.1371/journal.pcbi.1006917] [Citation(s) in RCA: 32] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022] Open
Abstract
Emerging and re-emerging pathogens exhibit very complex dynamics, are hard to model and difficult to predict. Their dynamics might appear intractable. However, new statistical approaches-rooted in dynamical systems and the theory of stochastic processes-have yielded insight into the dynamics of emerging and re-emerging pathogens. We argue that these approaches may lead to new methods for predicting epidemics. This perspective views pathogen emergence and re-emergence as a "critical transition," and uses the concept of noisy dynamic bifurcation to understand the relationship between the system observables and the distance to this transition. Because the system dynamics exhibit characteristic fluctuations in response to perturbations for a system in the vicinity of a critical point, we propose this information may be harnessed to develop early warning signals. Specifically, the motion of perturbations slows as the system approaches the transition.
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Affiliation(s)
- John M. Drake
- Odum School of Ecology, University of Georgia, Athens, Georgia, United States of America
- Center for the Ecology of Infectious Diseases, University of Georgia, Athens, Georgia, United States of America
| | - Tobias S. Brett
- Odum School of Ecology, University of Georgia, Athens, Georgia, United States of America
- Center for the Ecology of Infectious Diseases, University of Georgia, Athens, Georgia, United States of America
| | - Shiyang Chen
- Department of Mechanical Engineering, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Bogdan I. Epureanu
- Department of Mechanical Engineering, University of Michigan, Ann Arbor, Michigan, United States of America
- Automotive Research Center, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Matthew J. Ferrari
- Center for Infectious Disease Dynamics, Pennsylvania State University, State College, Pennsylvania, United States of America
| | - Éric Marty
- Odum School of Ecology, University of Georgia, Athens, Georgia, United States of America
- Center for the Ecology of Infectious Diseases, University of Georgia, Athens, Georgia, United States of America
| | - Paige B. Miller
- Odum School of Ecology, University of Georgia, Athens, Georgia, United States of America
- Center for the Ecology of Infectious Diseases, University of Georgia, Athens, Georgia, United States of America
| | - Eamon B. O’Dea
- Odum School of Ecology, University of Georgia, Athens, Georgia, United States of America
- Center for the Ecology of Infectious Diseases, University of Georgia, Athens, Georgia, United States of America
| | - Suzanne M. O’Regan
- Department of Mathematics, North Carolina A&T State University, Greensboro, North Carolina, United States of America
| | - Andrew W. Park
- Odum School of Ecology, University of Georgia, Athens, Georgia, United States of America
- Center for the Ecology of Infectious Diseases, University of Georgia, Athens, Georgia, United States of America
| | - Pejman Rohani
- Odum School of Ecology, University of Georgia, Athens, Georgia, United States of America
- Center for the Ecology of Infectious Diseases, University of Georgia, Athens, Georgia, United States of America
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