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Shih DH, Wu YH, Wu TW, Chang SC, Shih MH. Infodemiology of Influenza-like Illness: Utilizing Google Trends' Big Data for Epidemic Surveillance. J Clin Med 2024; 13:1946. [PMID: 38610711 PMCID: PMC11012909 DOI: 10.3390/jcm13071946] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2024] [Revised: 03/18/2024] [Accepted: 03/25/2024] [Indexed: 04/14/2024] Open
Abstract
Background: Influenza-like illness (ILI) encompasses symptoms similar to influenza, affecting population health. Surveillance, including Google Trends (GT), offers insights into epidemic patterns. Methods: This study used multiple regression models to analyze the correlation between ILI incidents, GT keyword searches, and climate variables during influenza outbreaks. It compared the predictive capabilities of time-series and deep learning models against ILI emergency incidents. Results: The GT searches for "fever" and "cough" were significantly associated with ILI cases (p < 0.05). Temperature had a more substantial impact on ILI incidence than humidity. Among the tested models, ARIMA provided the best predictive power. Conclusions: GT and climate data can forecast ILI trends, aiding governmental decision making. Temperature is a crucial predictor, and ARIMA models excel in forecasting ILI incidences.
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Affiliation(s)
- Dong-Her Shih
- Department of Information Management, National Yunlin University of Science and Technology, Douliu 64002, Taiwan; (D.-H.S.); (Y.-H.W.); (S.-C.C.)
| | - Yi-Huei Wu
- Department of Information Management, National Yunlin University of Science and Technology, Douliu 64002, Taiwan; (D.-H.S.); (Y.-H.W.); (S.-C.C.)
| | - Ting-Wei Wu
- Department of Information Management, National Yunlin University of Science and Technology, Douliu 64002, Taiwan; (D.-H.S.); (Y.-H.W.); (S.-C.C.)
| | - Shu-Chi Chang
- Department of Information Management, National Yunlin University of Science and Technology, Douliu 64002, Taiwan; (D.-H.S.); (Y.-H.W.); (S.-C.C.)
| | - Ming-Hung Shih
- Department of Electrical and Computer Engineering, Iowa State University, 2520 Osborn Drive, Ames, IA 50011, USA;
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Fang L, Hu W, Pan G. Meteorological factors cannot be ignored in machine learning-based methods for predicting dengue, a systematic review. INTERNATIONAL JOURNAL OF BIOMETEOROLOGY 2024; 68:401-410. [PMID: 38150020 DOI: 10.1007/s00484-023-02605-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/30/2022] [Revised: 09/18/2023] [Accepted: 12/13/2023] [Indexed: 12/28/2023]
Abstract
In recent years, there has been a rapid increase in the application of machine learning methods about predicting the incidence of dengue fever. However, the predictive factors and models employed in different studies vary greatly. Hence, we conducted a systematic review to summarize machine learning methods and predictors in previous studies. We searched PubMed, ScienceDirect, and Web of Science databases for articles published up to July 2023. The selected papers included not only the forecast of dengue incidence but also machine learning methods. A total of 23 papers were included in this study. Predictive factors included meteorological factors (22, 95.7%), historical dengue data (14, 60.9%), environmental factors (4, 17.4%), socioeconomic factors (4, 17.4%), vector surveillance data (2, 8.7%), and internet search data (3, 13.0%). Among meteorological factors, temperature (20, 87.0%), rainfall (20, 87.0%), and relative humidity (14, 60.9%) were the most commonly used. We found that Support Vector Machine (SVM) (6, 26.1%), Long Short-Term Memory (LSTM) (5, 21.7%), Random Forest (RF) (4, 17.4%), Least Absolute Shrinkage and Selection Operator (LASSO) (2, 8.7%), ensemble model (2, 8.7%), and other models (4, 17.4%) were identified as the best models based on evaluation metrics used in each article. These results indicate that meteorological factors are important predictors that cannot be ignored and SVM and LSTM algorithms are the most commonly used models in dengue fever prediction with good predictive performance. This review will contribute to the development of more robust early dengue warning systems and promote the application of machine learning methods in predicting climate-related infectious diseases.
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Affiliation(s)
- Lanlan Fang
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, 81 Meishan Road, Hefei, 230032, Anhui, China
| | - Wan Hu
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, 81 Meishan Road, Hefei, 230032, Anhui, China
| | - Guixia Pan
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, 81 Meishan Road, Hefei, 230032, Anhui, China.
- The Inflammation and Immune Mediated Diseases Laboratory of Anhui Province, Anhui Medical University, Hefei, China.
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Yin S, Ren C, Shi Y, Hua J, Yuan HY, Tian LW. A Systematic Review on Modeling Methods and Influential Factors for Mapping Dengue-Related Risk in Urban Settings. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:ijerph192215265. [PMID: 36429980 PMCID: PMC9690886 DOI: 10.3390/ijerph192215265] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/18/2022] [Revised: 11/11/2022] [Accepted: 11/17/2022] [Indexed: 05/12/2023]
Abstract
Dengue fever is an acute mosquito-borne disease that mostly spreads within urban or semi-urban areas in warm climate zones. The dengue-related risk map is one of the most practical tools for executing effective control policies, breaking the transmission chain, and preventing disease outbreaks. Mapping risk at a small scale, such as at an urban level, can demonstrate the spatial heterogeneities in complicated built environments. This review aims to summarize state-of-the-art modeling methods and influential factors in mapping dengue fever risk in urban settings. Data were manually extracted from five major academic search databases following a set of querying and selection criteria, and a total of 28 studies were analyzed. Twenty of the selected papers investigated the spatial pattern of dengue risk by epidemic data, whereas the remaining eight papers developed an entomological risk map as a proxy for potential dengue burden in cities or agglomerated urban regions. The key findings included: (1) Big data sources and emerging data-mining techniques are innovatively employed for detecting hot spots of dengue-related burden in the urban context; (2) Bayesian approaches and machine learning algorithms have become more popular as spatial modeling tools for predicting the distribution of dengue incidence and mosquito presence; (3) Climatic and built environmental variables are the most common factors in making predictions, though the effects of these factors vary with the mosquito species; (4) Socio-economic data may be a better representation of the huge heterogeneity of risk or vulnerability spatial distribution on an urban scale. In conclusion, for spatially assessing dengue-related risk in an urban context, data availability and the purpose for mapping determine the analytical approaches and modeling methods used. To enhance the reliabilities of predictive models, sufficient data about dengue serotyping, socio-economic status, and spatial connectivity may be more important for mapping dengue-related risk in urban settings for future studies.
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Affiliation(s)
- Shi Yin
- Faculty of Architecture, The University of Hong Kong, Pokfulam, Hong Kong SAR, China
- School of Architecture, South China University of Technology, Guangzhou 510641, China
| | - Chao Ren
- Faculty of Architecture, The University of Hong Kong, Pokfulam, Hong Kong SAR, China
- Correspondence:
| | - Yuan Shi
- Department of Geography and Planning, University of Liverpool, Liverpool L69 3BX, UK
| | - Junyi Hua
- School of International Affairs and Public Administration, Ocean University of China, Qingdao 266100, China
| | - Hsiang-Yu Yuan
- Department of Biomedical Sciences, City University of Hong Kong, Kowloon, Hong Kong SAR, China
| | - Lin-Wei Tian
- School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong SAR, China
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4
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Hoyos W, Aguilar J, Toro M. Dengue models based on machine learning techniques: A systematic literature review. Artif Intell Med 2021; 119:102157. [PMID: 34531010 DOI: 10.1016/j.artmed.2021.102157] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2020] [Revised: 05/08/2021] [Accepted: 08/17/2021] [Indexed: 12/16/2022]
Abstract
BACKGROUND Dengue modeling is a research topic that has increased in recent years. Early prediction and decision-making are key factors to control dengue. This Systematic Literature Review (SLR) analyzes three modeling approaches of dengue: diagnostic, epidemic, intervention. These approaches require models of prediction, prescription and optimization. This SLR establishes the state-of-the-art in dengue modeling, using machine learning, in the last years. METHODS Several databases were selected to search the articles. The selection was made based on Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology. Sixty-four articles were obtained and analyzed to describe their strengths and limitations. Finally, challenges and opportunities for research on machine-learning for dengue modeling were identified. RESULTS Logistic regression was the most used modeling approach for the diagnosis of dengue (59.1%). The analysis of the epidemic approach showed that linear regression (17.4%) is the most used technique within the spatial analysis. Finally, the most used intervention modeling is General Linear Model with 70%. CONCLUSIONS We conclude that cause-effect models may improve diagnosis and understanding of dengue. Models that manage uncertainty can also be helpful, because of low data-quality in healthcare. Finally, decentralization of data, using federated learning, may decrease computational costs and allow model building without compromising data security.
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Affiliation(s)
- William Hoyos
- Grupo de Investigaciones Microbiológicas y Biomédicas de Córdoba, Universidad de Córdoba, Montería, Colombia; Grupo de Investigación en I+D+i en TIC, Universidad EAFIT, Medellín, Colombia.
| | - Jose Aguilar
- Grupo de Investigación en I+D+i en TIC, Universidad EAFIT, Medellín, Colombia; Centro de Estudios en Microelectrónica y Sistemas Distribuidos, Universidad de Los Andes, Mérida, Venezuela; Universidad de Alcalá, Depto. de Automática, Alcalá de Henares, Spain
| | - Mauricio Toro
- Grupo de Investigación en I+D+i en TIC, Universidad EAFIT, Medellín, Colombia
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Lucas-Dominguez R, Alonso-Arroyo A, Vidal-Infer A, Aleixandre-Benavent R. The sharing of research data facing the COVID-19 pandemic. Scientometrics 2021; 126:4975-4990. [PMID: 33935332 PMCID: PMC8072296 DOI: 10.1007/s11192-021-03971-6] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2020] [Accepted: 03/24/2021] [Indexed: 11/25/2022]
Abstract
During the previous Ebola and Zika outbreaks, researchers shared their data, allowing many published epidemiological studies to be produced only from open research data, to speed up investigations and control of these infections. This study aims to evaluate the dissemination of the COVID-19 research data underlying scientific publications. Analysis of COVID-19 publications from December 1, 2019, to April 30, 2020, was conducted through the PubMed Central repository to evaluate the research data available through its publication as supplementary material or deposited in repositories. The PubMed Central search generated 5,905 records, of which 804 papers included complementary research data, especially as supplementary material (77.4%). The most productive journals were The New England Journal of Medicine, The Lancet and The Lancet Infectious Diseases, the most frequent keyword was pneumonia, and the most used repositories were GitHub and GenBank. An expected growth in the number of published articles following the course of the pandemics is confirmed in this work, while the underlying research data are only 13.6%. It can be deduced that data sharing is not a common practice, even in health emergencies, such as the present one. High-impact generalist journals have accounted for a large share of global publishing. The topics most often covered are related to epidemiological and public health concepts, genetics, virology and respiratory diseases, such as pneumonia. However, it is essential to interpret these data with caution following the evolution of publications and their funding in the coming months.
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Affiliation(s)
- Rut Lucas-Dominguez
- Department of the History of Science and Information Science, School of Medicine and Dentistry, University of Valencia, Avda. Blasco Ibañez 15, 46010 Valencia, Spain
- UISYS, Joint Research Unit CSIC–University of Valencia, Pza. Cisneros 4, 46003 Valencia, Spain
- CIBERONC, Valencia, Spain
| | - Adolfo Alonso-Arroyo
- Department of the History of Science and Information Science, School of Medicine and Dentistry, University of Valencia, Avda. Blasco Ibañez 15, 46010 Valencia, Spain
- UISYS, Joint Research Unit CSIC–University of Valencia, Pza. Cisneros 4, 46003 Valencia, Spain
| | - Antonio Vidal-Infer
- Department of the History of Science and Information Science, School of Medicine and Dentistry, University of Valencia, Avda. Blasco Ibañez 15, 46010 Valencia, Spain
- UISYS, Joint Research Unit CSIC–University of Valencia, Pza. Cisneros 4, 46003 Valencia, Spain
| | - Rafael Aleixandre-Benavent
- UISYS, Joint Research Unit CSIC–University of Valencia, Pza. Cisneros 4, 46003 Valencia, Spain
- Ingenio (CSIC-Politechnic University of Valencia), Ciudad Politécnica de La Innovación, Edif 8E 4º, Camino de Vera s/n, 46022 Valencia, Spain
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Software Defect Prediction for Healthcare Big Data: An Empirical Evaluation of Machine Learning Techniques. JOURNAL OF HEALTHCARE ENGINEERING 2021; 2021:8899263. [PMID: 33815733 PMCID: PMC7987450 DOI: 10.1155/2021/8899263] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/09/2020] [Revised: 09/29/2020] [Accepted: 02/24/2021] [Indexed: 01/02/2023]
Abstract
Software defect prediction (SDP) in the initial period of the software development life cycle (SDLC) remains a critical and important assignment. SDP is essentially studied during few last decades as it leads to assure the quality of software systems. The quick forecast of defective or imperfect artifacts in software development may serve the development team to use the existing assets competently and more effectively to provide extraordinary software products in the given or narrow time. Previously, several canvassers have industrialized models for defect prediction utilizing machine learning (ML) and statistical techniques. ML methods are considered as an operative and operational approach to pinpoint the defective modules, in which moving parts through mining concealed patterns amid software metrics (attributes). ML techniques are also utilized by several researchers on healthcare datasets. This study utilizes different ML techniques software defect prediction using seven broadly used datasets. The ML techniques include the multilayer perceptron (MLP), support vector machine (SVM), decision tree (J48), radial basis function (RBF), random forest (RF), hidden Markov model (HMM), credal decision tree (CDT), K-nearest neighbor (KNN), average one dependency estimator (A1DE), and Naïve Bayes (NB). The performance of each technique is evaluated using different measures, for instance, relative absolute error (RAE), mean absolute error (MAE), root mean squared error (RMSE), root relative squared error (RRSE), recall, and accuracy. The inclusive outcome shows the best performance of RF with 88.32% average accuracy and 2.96 rank value, second-best performance is achieved by SVM with 87.99% average accuracy and 3.83 rank values. Moreover, CDT also shows 87.88% average accuracy and 3.62 rank values, placed on the third position. The comprehensive outcomes of research can be utilized as a reference point for new research in the SDP domain, and therefore, any assertion concerning the enhancement in prediction over any new technique or model can be benchmarked and proved.
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Performance Assessment of Classification Algorithms on Early Detection of Liver Syndrome. JOURNAL OF HEALTHCARE ENGINEERING 2021; 2020:6680002. [PMID: 33489060 PMCID: PMC7787853 DOI: 10.1155/2020/6680002] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/03/2020] [Revised: 11/18/2020] [Accepted: 11/25/2020] [Indexed: 11/17/2022]
Abstract
In the recent era, a liver syndrome that causes any damage in life capacity is exceptionally normal everywhere throughout the world. It has been found that liver disease is exposed more in young people as a comparison with other aged people. At the point when liver capacity ends up, life endures just up to 1 or 2 days scarcely, and it is very hard to predict such illness in the early stage. Researchers are trying to project a model for early prediction of liver disease utilizing various machine learning approaches. However, this study compares ten classifiers including A1DE, NB, MLP, SVM, KNN, CHIRP, CDT, Forest-PA, J48, and RF to find the optimal solution for early and accurate prediction of liver disease. The datasets utilized in this study are taken from the UCI ML repository and the GitHub repository. The outcomes are assessed via RMSE, RRSE, recall, specificity, precision, G-measure, F-measure, MCC, and accuracy. The exploratory outcomes show a better consequence of RF utilizing the UCI dataset. Assessing RF using RMSE and RRSE, the outcomes are 0.4328 and 87.6766, while the accuracy of RF is 72.1739% that is also better than other employed classifiers. However, utilizing the GitHub dataset, SVM beats other employed techniques in terms of increasing accuracy up to 71.3551%. Moreover, the comprehensive outcomes of this exploration can be utilized as a reference point for further research studies that slight assertion concerning the enhancement in extrapolation through any new technique, model, or framework can be benchmarked and confirmed.
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Chen YP, Ho TS, Lee PC, Chang HH, Shieh GS, Lee CI, Hu WL, Hung YC. Effects of Chinese and Western Medicine on Patients with Dengue Fever. THE AMERICAN JOURNAL OF CHINESE MEDICINE 2020; 48:329-340. [PMID: 32138530 DOI: 10.1142/s0192415x20500160] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Dengue fever is an important epidemic disease with a high prevalence in tropical and subtropical countries. We aimed to investigate the effects of a treatment integrating traditional Chinese (TCM) and Western medicines on dengue inpatients with warning signs (i.e., group B) according to the World Health Organization dengue classification in this retrospective cohort study of medical records. Inpatients who were treated with conventional Western therapies in the absence or presence of TCM were assigned to the control and treatment groups, respectively. Data were compared using an analysis of variance, general linear analysis, and chi-square test. The most common clinical symptoms and signs of dengue fever were fever and muscle ache. The treatment group patients were significantly more likely to present general weakness and poor appetite than the control group patients. Patients in the treatment group were more likely to experience stomachache than those in the control group. Moreover, comparisons of the changes in hemoglobin and alanine aminotransferase levels over time revealed significant differences between the patient groups. Zhu Ye Shi Gao Tang, Gui Pi Tang, Paeonia suffruticosa, and Clerodendrum cyrtophyllum were the most commonly administered TCM formula and single herbs in this study. Patients in the treatment group experienced a resolution of symptoms, signs, and laboratory data and were discharged smoothly, without deterioration to death or critical care. Our findings suggest that the integration of TCM and Western medicine may yield an appropriate treatment for dengue fever.
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Affiliation(s)
- Yu-Pei Chen
- Department of Chinese Medicine, Tainan Hospital, Ministry of Health and Welfare, Tainan, Taiwan.,School of Medicine, National Cheng Kung University Tainan, Taiwan.,Department of Pharmacy, Chia Nan University of Pharmacy & Science, Tainan, Taiwan.,School of Chinese Medicine, China Medical University Taichung, Taiwan
| | - Tzong-Shiann Ho
- College of Medicine, National Cheng Kung University Tainan, Taiwan.,Department of Pediatrics, National Cheng Kung University Hospital, Tainan, Taiwan
| | - Po-Chang Lee
- School of Medicine, National Cheng Kung University Tainan, Taiwan.,National Health Insurance Administration, Ministry of Health and Welfare, Taipei, Taiwan
| | - Hen-Hong Chang
- Graduate Institute of Integrated Medicine, College of Chinese Medicine, and Chinese Medicine Research Center, China Medical University, Taichung, Taiwan.,Department of Chinese Medicine, China Medical University Hospital, Taichung, Taiwan
| | - Gia-Shing Shieh
- Department of Urology, Tainan Hospital, Ministry of Health and Welfare, Tainan, Taiwan.,Department of Biochemistry and Molecular Biology, College of Medicine, National Cheng Kung University, Tainan, Taiwan
| | - Chih-I Lee
- Department of Infection, Tainan Hospital, Ministry of Health and Welfare, Tainan, Taiwan
| | - Wen-Long Hu
- Department of Chinese Medicine, Kaohsiung Chang Gung Memorial Hospital and School of Traditional Chinese Medicine, Chang Gung University College of Medicine, Kaohsiung, Taiwan.,Kaohsiung Medical University College of Medicine, Kaohsiung, Taiwan.,Fooyin University College of Nursing, Kaohsiung, Taiwan
| | - Yu-Chiang Hung
- Department of Chinese Medicine, Kaohsiung Chang Gung Memorial Hospital and School of Traditional Chinese Medicine, Chang Gung University College of Medicine, Kaohsiung, Taiwan
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Bragazzi NL, Mahroum N. Google Trends Predicts Present and Future Plague Cases During the Plague Outbreak in Madagascar: Infodemiological Study. JMIR Public Health Surveill 2019; 5:e13142. [PMID: 30763255 PMCID: PMC6429048 DOI: 10.2196/13142] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2018] [Revised: 01/17/2019] [Accepted: 01/18/2019] [Indexed: 01/08/2023] Open
Abstract
Background Plague is a highly infectious zoonotic disease caused by the bacillus Yersinia pestis. Three major forms of the disease are known: bubonic, septicemic, and pneumonic plague. Though highly related to the past, plague still represents a global public health concern. Cases of plague continue to be reported worldwide. In recent months, pneumonic plague cases have been reported in Madagascar. However, despite such a long-standing and rich history, it is rather difficult to get a comprehensive overview of the general situation. Within the framework of electronic health (eHealth), in which people increasingly search the internet looking for health-related material, new information and communication technologies could enable researchers to get a wealth of data, which could complement traditional surveillance of infectious diseases. Objective In this study, we aimed to assess public reaction regarding the recent plague outbreak in Madagascar by quantitatively characterizing the public’s interest. Methods We captured public interest using Google Trends (GT) and correlated it to epidemiological real-world data in terms of incidence rate and spread pattern. Results Statistically significant positive correlations were found between GT search data and confirmed (R2=0.549), suspected (R2=0.265), and probable (R2=0.518) cases. From a geospatial standpoint, plague-related GT queries were concentrated in Toamasina (100%), Toliara (68%), and Antananarivo (65%). Concerning the forecasting models, the 1-day lag model was selected as the best regression model. Conclusions An earlier digital Web search reaction could potentially contribute to better management of outbreaks, for example, by designing ad hoc interventions that could contain the infection both locally and at the international level, reducing its spread.
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Affiliation(s)
- Nicola Luigi Bragazzi
- Department of Health Sciences, Postgraduate School of Public Health, University of Genoa, Genoa, Italy
| | - Naim Mahroum
- Sackler Faculty of Medicine, Tel-Aviv University, Tel Aviv, Israel
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