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Servadio JL, Convertino M, Fiecas M, Muñoz‐Zanzi C. Weekly Forecasting of Yellow Fever Occurrence and Incidence via Eco-Meteorological Dynamics. GEOHEALTH 2023; 7:e2023GH000870. [PMID: 37885914 PMCID: PMC10599710 DOI: 10.1029/2023gh000870] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/27/2023] [Revised: 08/31/2023] [Accepted: 10/11/2023] [Indexed: 10/28/2023]
Abstract
Yellow Fever (YF), a mosquito-borne disease, requires ongoing surveillance and prevention due to its persistence and ability to cause major epidemics, including one that began in Brazil in 2016. Forecasting based on factors influencing YF risk can improve efficiency in prevention. This study aimed to produce weekly forecasts of YF occurrence and incidence in Brazil using weekly meteorological and ecohydrological conditions. Occurrence was forecast as the probability of observing any cases, and incidence was forecast to represent morbidity if YF occurs. We fit gamma hurdle models, selecting predictors from several meteorological and ecohydrological factors, based on forecast accuracy defined by receiver operator characteristic curves and mean absolute error. We fit separate models for data before and after the start of the 2016 outbreak, forecasting occurrence and incidence for all municipalities of Brazil weekly. Different predictor sets were found to produce most accurate forecasts in each time period, and forecast accuracy was high for both time periods. Temperature, precipitation, and previous YF burden were most influential predictors among models. Minimum, maximum, mean, and range of weekly temperature, precipitation, and humidity contributed to forecasts, with optimal lag times of 2, 6, and 7 weeks depending on time period. Results from this study show the use of environmental predictors in providing regular forecasts of YF burden and producing nationwide forecasts. Weekly forecasts, which can be produced using the forecast model developed in this study, are beneficial for informing immediate preparedness measures.
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Affiliation(s)
- Joseph L. Servadio
- Department of BiologyCenter for Infectious Disease DynamicsPennsylvania State UniversityUniversity ParkPAUSA
- Division of Environmental Health SciencesSchool of Public HealthUniversity of MinnesotaMinneapolisMNUSA
| | | | - Mark Fiecas
- Division of BiostatisticsSchool of Public HealthUniversity of MinnesotaMinneapolisMNUSA
| | - Claudia Muñoz‐Zanzi
- Division of Environmental Health SciencesSchool of Public HealthUniversity of MinnesotaMinneapolisMNUSA
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2
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Dong L, Zhu X, Zhao H, Zhao Q, Liu S, Liu J, Gong L. Development and validation of a LASSO-based prediction model for immunosuppressive medication nonadherence in kidney transplant recipients. Ren Fail 2023; 45:2238832. [PMID: 38532721 PMCID: PMC10512851 DOI: 10.1080/0886022x.2023.2238832] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2023] [Accepted: 07/15/2023] [Indexed: 03/28/2024] Open
Abstract
INTRODUCTION To establish a prediction model to predict immunosuppressive medication (IM) nonadherence in kidney transplant recipients (KTRs) based on a combined theory framework. METHODS This polycentric, cross-sectional study included 1191 KTRs from October 2020 to February 2021 in China, with 1011 KTRs enrolled in the derivation set and 180 in the external validation set. Variables selected based on the combined theory of planned behavior (TPB)/health belief model (HBM) theory were analyzed by the least absolute shrinkage and selection operator (LASSO). Internal 10 cross-validation was conducted to determine the optimal lambda value. The receiver operating characteristic (ROC) curve, specificity, and sensitivity were used to evaluate the prediction model, and further assessment was run by external validation. RESULTS IM nonadherence rate was 38.48% in the derivation set and 37.22% in the validation set. The LASSO model was developed with eight predictors for IM nonadherence: age, preoperative drinking history, education, marital status, perceived barriers, social support, perceived behavioral control, and perceived susceptibility. The model demonstrated acceptable discrimination with the area under the ROC curve of 0.797 (95% CI: 0.745-0.850) in the internal validation set and 0.757 (95% CI: 0.684-0.829) in the external validation set. The specificity and sensitivity in the internal validation and external validation set were 0.741, 0.748, 0.673, and 0.716, respectively. CONCLUSIONS The LASSO model was developed to guide identifying high-risk nonadherent patients and timely and effective interventions to improve their prognosis and survival.
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Affiliation(s)
- Lei Dong
- Nursing School, Central South University, Changsha, China
| | - Xiao Zhu
- Nursing Department, The Third Xiangya Hospital of Central South University, Changsha, China
- Research Center of Chinese Health Ministry on Transplantation Medicine Engineering and Technology, The Third Xiangya Hospital of Central South University, Changsha, China
| | - Hongyu Zhao
- Nursing School, Central South University, Changsha, China
| | - Qin Zhao
- Nursing School, Central South University, Changsha, China
| | - Shan Liu
- College of Nursing and Public Health, Adelphi University, New York, NY, USA
| | - Jia Liu
- Nursing School, Central South University, Changsha, China
- Nursing Department, The Third Xiangya Hospital of Central South University, Changsha, China
- Research Center of Chinese Health Ministry on Transplantation Medicine Engineering and Technology, The Third Xiangya Hospital of Central South University, Changsha, China
| | - Lina Gong
- Nursing Department, The Third Xiangya Hospital of Central South University, Changsha, China
- Department of Neurology, The Third Xiangya Hospital of Central South University, Changsha, China
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Tao Q, Liu S, Zhang J, Jiang J, Jin Z, Huang Y, Liu X, Lin S, Zeng X, Li X, Tao G, Chen H. Clinical applications of smart wearable sensors. iScience 2023; 26:107485. [PMID: 37636055 PMCID: PMC10448028 DOI: 10.1016/j.isci.2023.107485] [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] [Indexed: 08/29/2023] Open
Abstract
Smart wearable sensors are electronic devices worn on the body that collect, process, and transmit various physiological data. Compared to traditional devices, their advantages in terms of portability and comfort have made them increasingly important in the medical field. This review takes a unique clinical physician's standpoint, diverging from conventional sensor-type-based classifications, and provides a comprehensive overview of the diverse clinical applications of wearable sensors in recent years. In this review, we categorize these applications according to different diseases, encompassing skin diseases and injuries, cardiovascular diseases, abnormal human motion, as well as endocrine and metabolic disorders. Additionally, we discuss the challenges and perspectives hindering the development of sensors for clinical use, emphasizing the critical need for interdisciplinary collaboration between medical and engineering professionals. Overall, this review would serve as an important reference for the future direction of sensor devices in clinical use.
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Affiliation(s)
- Qingxiao Tao
- Department of Dermatology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China
| | - Suwen Liu
- Department of Dermatology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China
| | - Jingyu Zhang
- Department of Dermatology, Huazhong University of Science and Technology Union Shenzhen Hospital, Shenzhen 518052, China
- Shenzhen University Medical School, Shenzhen 518060, China
| | - Jian Jiang
- Department of Dermatology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China
| | - Zilin Jin
- Department of Dermatology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China
| | - Yuqiong Huang
- Department of Dermatology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China
| | - Xin Liu
- Department of Dermatology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China
| | - Shiying Lin
- Department of Dermatology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China
| | - Xin Zeng
- Department of Dermatology, Huazhong University of Science and Technology Union Shenzhen Hospital, Shenzhen 518052, China
| | - Xuemei Li
- Department of Dermatology, Huazhong University of Science and Technology Union Shenzhen Hospital, Shenzhen 518052, China
| | - Guangming Tao
- Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan 430074, China
- State Key Laboratory of Material Processing and Die & Mould Technology, School of Materials Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Hongxiang Chen
- Department of Dermatology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China
- Department of Dermatology, Huazhong University of Science and Technology Union Shenzhen Hospital, Shenzhen 518052, China
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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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Li D, Ren X, Su Y. Predicting COVID-19 using lioness optimization algorithm and graph convolution network. Soft comput 2023; 27:5437-5501. [PMID: 36686544 PMCID: PMC9838306 DOI: 10.1007/s00500-022-07778-2] [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] [Accepted: 12/21/2022] [Indexed: 01/11/2023]
Abstract
In this paper, a graph convolution network prediction model based on the lioness optimization algorithm (LsOA-GCN) is proposed to predict the cumulative number of confirmed COVID-19 cases in 17 regions of Hubei Province from March 23 to March 29, 2020, according to the transmission characteristics of COVID-19. On the one hand, Spearman correlation analysis with delay days and LsOA are used to capture the dynamic changes of feature information to obtain the temporal features. On the other hand, the graph convolutional network is used to capture the topological structure of the city network, so as to obtain spatial information and finally realize the prediction task. Then, we evaluate this model through performance evaluation indicators and statistical test methods and compare the results of LsOA-GCN with 10 representative prediction methods in the current epidemic prediction study. The experimental results show that the LsOA-GCN prediction model is significantly better than other prediction methods in all indicators and can successfully capture spatio-temporal information from feature data, thereby achieving accurate prediction of epidemic trends in different regions of Hubei Province.
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Affiliation(s)
- Dong Li
- College of Economics and Management, Xi’an University of Posts and Telecommunications, Xi’an, 710061 Shaanxi People’s Republic of China
| | - Xiaofei Ren
- College of Economics and Management, Xi’an University of Posts and Telecommunications, Xi’an, 710061 Shaanxi People’s Republic of China
| | - Yunze Su
- College of Economics and Management, Xi’an University of Posts and Telecommunications, Xi’an, 710061 Shaanxi People’s Republic of China
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Ahmed F, Khan AA, Ansari HR, Haque A. A Systems Biology and LASSO-Based Approach to Decipher the Transcriptome-Interactome Signature for Predicting Non-Small Cell Lung Cancer. BIOLOGY 2022; 11:biology11121752. [PMID: 36552262 PMCID: PMC9774707 DOI: 10.3390/biology11121752] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Revised: 11/26/2022] [Accepted: 11/28/2022] [Indexed: 12/05/2022]
Abstract
The lack of precise molecular signatures limits the early diagnosis of non-small cell lung cancer (NSCLC). The present study used gene expression data and interaction networks to develop a highly accurate model with the least absolute shrinkage and selection operator (LASSO) for predicting NSCLC. The differentially expressed genes (DEGs) were identified in NSCLC compared with normal tissues using TCGA and GTEx data. A biological network was constructed using DEGs, and the top 20 upregulated and 20 downregulated hub genes were identified. These hub genes were used to identify signature genes with penalized logistic regression using the LASSO to predict NSCLC. Our model’s development involved the following steps: (i) the dataset was divided into 80% for training (TR) and 20% for testing (TD1); (ii) a LASSO logistic regression analysis was performed on the TR with 10-fold cross-validation and identified a combination of 17 genes as NSCLC predictors, which were used further for development of the LASSO model. The model’s performance was assessed on the TD1 dataset and achieved an accuracy and an area under the curve of the receiver operating characteristics (AUC-ROC) of 0.986 and 0.998, respectively. Furthermore, the performance of the LASSO model was evaluated using three independent NSCLC test datasets (GSE18842, GSE27262, GSE19804) and achieved high accuracy, with an AUC-ROC of >0.99, >0.99, and 0.95, respectively. Based on this study, a web application called NSCLCpred was developed to predict NSCLC.
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Affiliation(s)
- Firoz Ahmed
- Department of Biochemistry, College of Science, University of Jeddah, P.O. Box 80327, Jeddah 21589, Saudi Arabia
- Correspondence:
| | - Abdul Arif Khan
- Department of Pharmaceutics, College of Pharmacy, King Saud University, P.O. Box 2457, Riyadh 11451, Saudi Arabia
| | - Hifzur Rahman Ansari
- King Abdullah International Medical Research Center (KAIMRC), King Saud Bin Abdulaziz University for Health Sciences, King Abdulaziz Medical City, Ministry of National Guard Health Affairs, P.O. Box 9515, Jeddah 21423, Saudi Arabia
| | - Absarul Haque
- King Fahd Medical Research Center, King Abdulaziz University, P.O. Box 80216, Jeddah 21589, Saudi Arabia
- Department of Medical Laboratory Sciences, Faculty of Applied Medical Sciences, King Abdulaziz University, P.O. Box 80216, Jeddah 21589, Saudi Arabia
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7
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Yoshida K, Fujimoto T, Muramatsu M, Shimizu H. Prediction of hand, foot, and mouth disease epidemics in Japan using a long short-term memory approach. PLoS One 2022; 17:e0271820. [PMID: 35900968 PMCID: PMC9333334 DOI: 10.1371/journal.pone.0271820] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2022] [Accepted: 07/08/2022] [Indexed: 11/19/2022] Open
Abstract
Hand, foot, and mouth disease (HFMD) is a common febrile illness caused by enteroviruses in the Picornaviridae family. The major symptoms of HFMD are fever and a vesicular rash on the hand, foot, or oral mucosa. Acute meningitis and encephalitis are observed in rare cases. HFMD epidemics occur annually in Japan, usually in the summer season. Relatively large-scale outbreaks have occurred every two years since 2011. In this study, the epidemic patterns of HFMD in Japan are predicted four weeks in advance using a deep learning method. The time-series data were analyzed by a long short-term memory (LSTM) approach called a Recurrent Neural Network. The LSTM model was trained on the numbers of weekly HFMD cases in each prefecture. These data are reported in the Infectious Diseases Weekly Report, which compiles the national surveillance data from web sites at the National Institute of Infectious Diseases, Japan, under the Infectious Diseases Control Law. Consequently, our trained LSTM model distinguishes between relatively large-scale and small-scale epidemics. The trained model predicted the HFMD epidemics in 2018 and 2019, indicating that the LSTM approach can estimate the future epidemic patterns of HFMD in Japan.
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Affiliation(s)
- Kazuhiro Yoshida
- Department of Virology II, National Institute of Infectious Diseases, Tokyo, Japan
- * E-mail:
| | - Tsuguto Fujimoto
- Department of Fungal Infection, National Institute of Infectious Diseases, Tokyo, Japan
| | - Masamichi Muramatsu
- Department of Virology II, National Institute of Infectious Diseases, Tokyo, Japan
| | - Hiroyuki Shimizu
- Department of Virology II, National Institute of Infectious Diseases, Tokyo, Japan
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Influencing factors and multi-scenario prediction of China's ecological footprint based on the STIRPAT model. ECOL INFORM 2022. [DOI: 10.1016/j.ecoinf.2022.101664] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
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9
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Data-driven methods for dengue prediction and surveillance using real-world and Big Data: A systematic review. PLoS Negl Trop Dis 2022; 16:e0010056. [PMID: 34995281 PMCID: PMC8740963 DOI: 10.1371/journal.pntd.0010056] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2021] [Accepted: 12/06/2021] [Indexed: 12/23/2022] Open
Abstract
Background Traditionally, dengue surveillance is based on case reporting to a central health agency. However, the delay between a case and its notification can limit the system responsiveness. Machine learning methods have been developed to reduce the reporting delays and to predict outbreaks, based on non-traditional and non-clinical data sources. The aim of this systematic review was to identify studies that used real-world data, Big Data and/or machine learning methods to monitor and predict dengue-related outcomes. Methodology/Principal findings We performed a search in PubMed, Scopus, Web of Science and grey literature between January 1, 2000 and August 31, 2020. The review (ID: CRD42020172472) focused on data-driven studies. Reviews, randomized control trials and descriptive studies were not included. Among the 119 studies included, 67% were published between 2016 and 2020, and 39% used at least one novel data stream. The aim of the included studies was to predict a dengue-related outcome (55%), assess the validity of data sources for dengue surveillance (23%), or both (22%). Most studies (60%) used a machine learning approach. Studies on dengue prediction compared different prediction models, or identified significant predictors among several covariates in a model. The most significant predictors were rainfall (43%), temperature (41%), and humidity (25%). The two models with the highest performances were Neural Networks and Decision Trees (52%), followed by Support Vector Machine (17%). We cannot rule out a selection bias in our study because of our two main limitations: we did not include preprints and could not obtain the opinion of other international experts. Conclusions/Significance Combining real-world data and Big Data with machine learning methods is a promising approach to improve dengue prediction and monitoring. Future studies should focus on how to better integrate all available data sources and methods to improve the response and dengue management by stakeholders. Dengue is one of the most important arbovirus infections in the world and its public health, societal and economic burden is increasing. Although the majority of dengue cases are asymptomatic or mild, severe disease forms can lead to death. For this reason, early diagnosis and monitoring of dengue are crucial to decrease mortality. However, most endemic regions still rely on traditional monitoring methods, despite the growing availability of novel data sources and data-driven methods based on real-world data, Big Data, and machine learning algorithms. In this systematic review, we identified and analyzed studies that used these novel approaches for dengue monitoring and/or prediction. We found that novel data streams, such as Internet search engines and social media platforms, and machine learning methods can be successfully used to improve dengue management, but are still vastly ignored in real life. These approaches should be combined with traditional methods to help stakeholders better prepare for each outbreak and improve early responsiveness.
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He Y, Zhao Y, Chen Y, Yuan H, Tsui K. Nowcasting influenza‐like illness (ILI) via a deep learning approach using google search data: An empirical study on Taiwan ILI. INT J INTELL SYST 2021. [DOI: 10.1002/int.22788] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Affiliation(s)
- Yuxin He
- College of Urban Transportation and Logistics Shenzhen Technology University Shenzhen China
| | - Yang Zhao
- School of Public Health (Shenzhen) Sun Yat‐Sen University Guangzhou China
| | - Yupeng Chen
- Trial Retail Engineering (T. R. E. China) Yantai China
| | - Hsiang‐Yu Yuan
- Department of Biomedical Sciences City University of Hong Kong Hong Kong China
| | - Kwok‐Leung Tsui
- Department of Industrial and Systems Engineering Virginia Polytechnic Institute and State University Blacksburg Virginia USA
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Qu J, Sumali B, Lee H, Terai H, Ishii M, Fukunaga K, Mitsukura Y, Nishimura T. Finding of the factors affecting the severity of COVID-19 based on mathematical models. Sci Rep 2021; 11:24224. [PMID: 34930966 PMCID: PMC8688457 DOI: 10.1038/s41598-021-03632-x] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2021] [Accepted: 12/07/2021] [Indexed: 01/08/2023] Open
Abstract
Since 2019, a large number of people worldwide have been infected with severe acute respiratory syndrome coronavirus 2. Among those infected, a limited number develop severe coronavirus disease 2019 (COVID-19), which generally has an acute onset. The treatment of patients with severe COVID-19 is challenging. To optimize disease prognosis and effectively utilize medical resources, proactive measures must be adopted for patients at risk of developing severe COVID-19. We analyzed the data of COVID-19 patients from seven medical institutions in Tokyo and used mathematical modeling of patient blood test results to quantify and compare the predictive ability of multiple prognostic indicators for the development of severe COVID-19. A machine learning logistic regression model was used to analyze the blood test results of 300 patients. Due to the limited data set, the size of the training group was constantly adjusted to ensure that the results of machine learning were effective (e.g., recognition rate of disease severity > 80%). Lymphocyte count, hemoglobin, and ferritin levels were the best prognostic indicators of severe COVID-19. The mathematical model developed in this study enables prediction and classification of COVID-19 severity.
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Affiliation(s)
- Jiahao Qu
- School of Integrated Design Engineering, Keio University, Yokohama, Kanagawa, Japan
| | - Brian Sumali
- School of Integrated Design Engineering, Keio University, Yokohama, Kanagawa, Japan
| | - Ho Lee
- Department of Neuropsychiatry, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-ku, Tokyo, Japan
| | - Hideki Terai
- Department of Neuropsychiatry, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-ku, Tokyo, Japan
| | - Makoto Ishii
- Department of Neuropsychiatry, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-ku, Tokyo, Japan
| | - Koichi Fukunaga
- Department of Neuropsychiatry, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-ku, Tokyo, Japan
| | - Yasue Mitsukura
- School of Integrated Design Engineering, Keio University, Yokohama, Kanagawa, Japan.
| | - Toshihiko Nishimura
- Department of Anesthesia, Stanford University School of Medicine, Stanford, CA, USA
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12
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Sundaravadivel T, Mahalakshmi V. Weighted butterfly optimization algorithm with intuitionistic fuzzy gaussian function based adaptive-neuro fuzzy inference system for covid-19 prediction. ACTA ACUST UNITED AC 2021; 56:3317-3324. [PMID: 34722166 PMCID: PMC8542441 DOI: 10.1016/j.matpr.2021.10.153] [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] [Indexed: 11/28/2022]
Abstract
Covid-19 cases are increasing each day, however none of the countries successfully came up with a proper approved vaccine. Studies suggest that the virus enters the body causing a respiratory infection post contact with a disease. Measures like screening and early diagnosis contribute towards the management of COVID- 19 thereby reducing the load of health care systems. Recent studies have provided promising methods that will be applicable for the current pandemic situation. The previous system designed a various Machine Learning (ML) algorithms such as Decision Tree (DT), Random Forest (RF), XGBoost, Gradient Boosting Machine (GBM) and Support Vector Machine (SVM) for predicting COVID-19 disease with symptoms. However, it does not produce satisfactory results in terms of true positive rate. And also, better optimization methods are required to enhance the precision rate with minimum execution time. To solve this problem the proposed system designed a Weighted Butterfly Optimization Algorithm (WBOA) with Intuitionistic fuzzy Gaussian function based Adaptive-Neuro Fuzzy Inference System (IFGF-ANFIS) classifier for predicting the magnitude of COVID- 19 disease. The principle aim of this method is to design an algorithm that could predict and assess the COVID-19 parameters. Initially, the dataset regarding COVID-19 is taken as an input and preprocessed. The parameters included are age, sex, history of fever, travel history, presence of cough and lung infection. Then the optimal features are selected by using Weighted Butterfly Optimization Algorithm (WBOA) to improve the classification accuracy. Based on the selected features, an Intuitionistic fuzzy Gaussian function based Adaptive-Neuro Fuzzy Inference System (IFGF-ANFIS) classifier is utilized for classifying the people having infection possibility. The studies conducted on this proposed system indicates that it is capable of producing better results than the other systems especially in terms of accuracy, precision, recall and f-measure.
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Affiliation(s)
| | - V Mahalakshmi
- Assistant professor, Dept. of computer science and Engineering
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13
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Sharif O, Islam MR, Hasan MZ, Kabir MA, Hasan ME, AlQahtani SA, Xu G. Analyzing the Impact of Demographic Variables on Spreading and Forecasting COVID-19. JOURNAL OF HEALTHCARE INFORMATICS RESEARCH 2021; 6:72-90. [PMID: 34549163 PMCID: PMC8444526 DOI: 10.1007/s41666-021-00105-8] [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: 11/10/2020] [Revised: 07/13/2021] [Accepted: 08/26/2021] [Indexed: 12/24/2022]
Abstract
The aim of this study is to analyse the coronavirus disease 2019 (COVID-19) outbreak in Bangladesh. This study investigates the impact of demographic variables on the spread of COVID-19 as well as tries to forecast the COVID-19 infected numbers. First of all, this study uses Fisher's Exact test to investigate the association between the infected groups of COVID-19 and demographical variables. Second, it exploits the ANOVA test to examine significant difference in the mean infected number of COVID-19 cases across the population density, literacy rate, and regions/divisions in Bangladesh. Third, this research predicts the number of infected cases in the epidemic peak region of Bangladesh for the year 2021. As a result, from the Fisher's Exact test, we find a very strong significant association between the population density groups and infected groups of COVID-19. And, from the ANOVA test, we observe a significant difference in the mean infected number of COVID-19 cases across the five different population density groups. Besides, the prediction model shows that the cumulative number of infected cases would be raised to around 500,000 in the most densely region of Bangladesh, Dhaka division.
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Affiliation(s)
- Omar Sharif
- Daffodil International University, Dhaka, Bangladesh
| | - Md Rafiqul Islam
- Advanced Analytics Institute (AAi), University of Technology Sydney (UTS), Ultimo, Australia
| | - Md Zobaer Hasan
- School of Science, Monash University Malaysia, Subang Jaya, Selangor D. E. Malaysia
| | - Muhammad Ashad Kabir
- School of Computing and Mathematics, Charles Sturt University, Bathurst, NSW Australia
| | | | - Salman A AlQahtani
- College of Computer and Information Sciences, King Saud University, Riyadh, Kingdom of Saudi Arabia
| | - Guandong Xu
- Advanced Analytics Institute (AAi), University of Technology Sydney (UTS), Ultimo, Australia
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14
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Hussain-Alkhateeb L, Rivera Ramírez T, Kroeger A, Gozzer E, Runge-Ranzinger S. Early warning systems (EWSs) for chikungunya, dengue, malaria, yellow fever, and Zika outbreaks: What is the evidence? A scoping review. PLoS Negl Trop Dis 2021; 15:e0009686. [PMID: 34529649 PMCID: PMC8445439 DOI: 10.1371/journal.pntd.0009686] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022] Open
Abstract
BACKGROUND Early warning systems (EWSs) are of increasing importance in the context of outbreak-prone diseases such as chikungunya, dengue, malaria, yellow fever, and Zika. A scoping review has been undertaken for all 5 diseases to summarize existing evidence of EWS tools in terms of their structural and statistical designs, feasibility of integration and implementation into national surveillance programs, and the users' perspective of their applications. METHODS Data were extracted from Cochrane Database of Systematic Reviews (CDSR), Google Scholar, Latin American and Caribbean Health Sciences Literature (LILACS), PubMed, Web of Science, and WHO Library Database (WHOLIS) databases until August 2019. Included were studies reporting on (a) experiences with existing EWS, including implemented tools; and (b) the development or implementation of EWS in a particular setting. No restrictions were applied regarding year of publication, language or geographical area. FINDINGS Through the first screening, 11,710 documents for dengue, 2,757 for Zika, 2,706 for chikungunya, 24,611 for malaria, and 4,963 for yellow fever were identified. After applying the selection criteria, a total of 37 studies were included in this review. Key findings were the following: (1) a large number of studies showed the quality performance of their prediction models but except for dengue outbreaks, only few presented statistical prediction validity of EWS; (2) while entomological, epidemiological, and social media alarm indicators are potentially useful for outbreak warning, almost all studies focus primarily or exclusively on meteorological indicators, which tends to limit the prediction capacity; (3) no assessment of the integration of the EWS into a routine surveillance system could be found, and only few studies addressed the users' perspective of the tool; (4) almost all EWS tools require highly skilled users with advanced statistics; and (5) spatial prediction remains a limitation with no tool currently able to map high transmission areas at small spatial level. CONCLUSIONS In view of the escalating infectious diseases as global threats, gaps and challenges are significantly present within the EWS applications. While some advanced EWS showed high prediction abilities, the scarcity of tool assessments in terms of integration into existing national surveillance systems as well as of the feasibility of transforming model outputs into local vector control or action plans tends to limit in most cases the support of countries in controlling disease outbreaks.
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Affiliation(s)
- Laith Hussain-Alkhateeb
- Global Health, School of Public Health and Community Medicine, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | | | - Axel Kroeger
- Centre for Medicine and Society, Albert-Ludwigs-Universität Freiburg, Freiburg, Germany
| | | | - Silvia Runge-Ranzinger
- Centre for Medicine and Society, Albert-Ludwigs-Universität Freiburg, Freiburg, Germany
- Heidelberg Institute of Global Health, University of Heidelberg, Heidelberg, Germany
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15
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Alfred R, Obit JH. The roles of machine learning methods in limiting the spread of deadly diseases: A systematic review. Heliyon 2021; 7:e07371. [PMID: 34179541 PMCID: PMC8219638 DOI: 10.1016/j.heliyon.2021.e07371] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2020] [Revised: 10/20/2020] [Accepted: 06/17/2021] [Indexed: 12/03/2022] Open
Abstract
Machine learning (ML) methods can be leveraged to prevent the spread of deadly infectious disease outbreak (e.g., COVID-19). This can be done by applying machine learning methods in predicting and detecting the deadly infectious disease. Most reviews did not discuss about the machine learning algorithms, datasets and performance measurements used for various applications in predicting and detecting the deadly infectious disease. In contrast, this paper outlines the literature review based on two major ways (e.g., prediction, detection) to limit the spread of deadly disease outbreaks. Hence, this study aims to investigate the state of the art, challenges and future works of leveraging ML methods to detect and predict deadly disease outbreaks according to two categories mentioned earlier. Specifically, this study provides a review on various approaches (e.g., individual and ensemble models), types of datasets, parameters or variables and performance measures used in the previous works. The literature review included all articles from journals and conference proceedings published from 2010 through 2020 in Scopus indexed databases using the search terms Predicting Disease Outbreaks and/or Detecting Disease using Machine Learning. The findings from this review focus on commonly used machine learning approaches, challenges and future works to limit the spread of deadly disease outbreaks through preventions and detections.
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Affiliation(s)
- Rayner Alfred
- Corresponding author. http://www.machineintelligencespace.com
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16
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Hao Y, Xu T, Hu H, Wang P, Bai Y. Prediction and analysis of Corona Virus Disease 2019. PLoS One 2020; 15:e0239960. [PMID: 33017421 PMCID: PMC7535054 DOI: 10.1371/journal.pone.0239960] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2020] [Accepted: 09/16/2020] [Indexed: 12/24/2022] Open
Abstract
The outbreak of Corona Virus Disease 2019 (COVID-19) in Wuhan has significantly impacted the economy and society globally. Countries are in a strict state of prevention and control of this pandemic. In this study, the development trend analysis of the cumulative confirmed cases, cumulative deaths, and cumulative cured cases was conducted based on data from Wuhan, Hubei Province, China from January 23, 2020 to April 6, 2020 using an Elman neural network, long short-term memory (LSTM), and support vector machine (SVM). A SVM with fuzzy granulation was used to predict the growth range of confirmed new cases, new deaths, and new cured cases. The experimental results showed that the Elman neural network and SVM used in this study can predict the development trend of cumulative confirmed cases, deaths, and cured cases, whereas LSTM is more suitable for the prediction of the cumulative confirmed cases. The SVM with fuzzy granulation can successfully predict the growth range of confirmed new cases and new cured cases, although the average predicted values are slightly large. Currently, the United States is the epicenter of the COVID-19 pandemic. We also used data modeling from the United States to further verify the validity of the proposed models.
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Affiliation(s)
- Yan Hao
- School of Information and Communication Engineering, North University of China, Taiyuan, China
| | - Ting Xu
- Department of Mathematics, School of Science, North University of China, Taiyuan, China
| | - Hongping Hu
- Department of Mathematics, School of Science, North University of China, Taiyuan, China
| | - Peng Wang
- Department of Mathematics, School of Science, North University of China, Taiyuan, China
| | - Yanping Bai
- Department of Mathematics, School of Science, North University of China, Taiyuan, China
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17
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Ribeiro MHDM, Mariani VC, Coelho LDS. Multi-step ahead meningitis case forecasting based on decomposition and multi-objective optimization methods. J Biomed Inform 2020; 111:103575. [PMID: 32976990 PMCID: PMC7507988 DOI: 10.1016/j.jbi.2020.103575] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2019] [Revised: 09/10/2020] [Accepted: 09/16/2020] [Indexed: 12/23/2022]
Abstract
Epidemiological time series forecasting plays an important role in health public systems, due to its ability to allow managers to develop strategic planning to avoid possible epidemics. In this paper, a hybrid learning framework is developed to forecast multi-step-ahead (one, two, and three-month-ahead) meningitis cases in four states of Brazil. First, the proposed approach applies an ensemble empirical mode decomposition (EEMD) to decompose the data into intrinsic mode functions and residual components. Then, each component is used as the input of five different forecasting models, and, from there, forecasted results are obtained. Finally, all combinations of models and components are developed, and for each case, the forecasted results are weighted integrated (WI) to formulate a heterogeneous ensemble forecaster for the monthly meningitis cases. In the final stage, a multi-objective optimization (MOO) using the Non-Dominated Sorting Genetic Algorithm – version II is employed to find a set of candidates’ weights, and then the Technique for Order of Preference by similarity to Ideal Solution (TOPSIS) is applied to choose the adequate set of weights. Next, the most adequate model is the one with the best generalization capacity out-of-sample in terms of performance criteria including mean absolute error (MAE), relative root mean squared error (RRMSE), and symmetric mean absolute percentage error (sMAPE). By using MOO, the intention is to enhance the performance of the forecasting models by improving simultaneously their accuracy and stability measures. To access the model’s performance, comparisons based on metrics are conducted with: (i) EEMD, heterogeneous ensemble integrated by direct strategy, or simple sum; (ii) EEMD, homogeneous ensemble of components WI; (iii) models without signal decomposition. At this stage, MAE, RRMSE, and sMAPE criteria as well as Diebold–Mariano statistical test are adopted. In all twelve scenarios, the proposed framework was able to perform more accurate and stable forecasts, which showed, on 89.17% of the cases, that the errors of the proposed approach are statistically lower than other approaches. These results showed that combining EEMD, heterogeneous ensemble and WI with weights obtained by optimization can develop precise and stable forecasts. The modeling developed in this paper is promising and can be used by managers to support decision making. Ensemble empirical mode decomposition is applied into the raw time series. Heterogeneous ensemble learning models are used to forecasting meningitis cases. The NSGA-II algorithm and TOPSIS criterion are employed in the multi-objective procedure. Proposed model has errors statistically lower than 89.17% of the compared models. Promising results are achieved by the weighted integrated ensemble learning model.
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Affiliation(s)
- Matheus Henrique Dal Molin Ribeiro
- Graduate Program in Industrial & Systems Engineering (PPGEPS), Pontifical Catholic University of Parana (PUCPR), 1155, Rua Imaculada Conceicao, Curitiba, Parana, 80215-901, Brazil; Department of Mathematics, Federal Technological University of Parana (UTFPR), Via do Conhecimento, KM 01 - Fraron, Pato Branco, Parana, 85503-390, Brazil.
| | - Viviana Cocco Mariani
- Department of Electrical Engineering, Federal University of Parana (UFPR), 100, Avenida Cel. Francisco dos Santos, Curitiba, Parana, 81530-000, Brazil; Mechanical Engineering Graduate Program (PPGEM), Pontifical Catholic University of Parana (PUCPR), 1155, Rua Imaculada Conceicao, Curitiba, Parana, 80215-901, Brazil
| | - Leandro Dos Santos Coelho
- Graduate Program in Industrial & Systems Engineering (PPGEPS), Pontifical Catholic University of Parana (PUCPR), 1155, Rua Imaculada Conceicao, Curitiba, Parana, 80215-901, Brazil; Department of Electrical Engineering, Federal University of Parana (UFPR), 100, Avenida Cel. Francisco dos Santos, Curitiba, Parana, 81530-000, Brazil
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18
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Special Issue on Novel Informatics Approaches to COVID-19 Research. J Biomed Inform 2020. [PMCID: PMC7833937 DOI: 10.1016/j.jbi.2020.103485] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
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19
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Fakhruddin M, Putra PS, Wijaya KP, Sopaheluwakan A, Satyaningsih R, Komalasari KE, Mamenun, Sumiati, Indratno SW, Nuraini N, Götz T, Soewono E. Assessing the interplay between dengue incidence and weather in Jakarta via a clustering integrated multiple regression model. ECOLOGICAL COMPLEXITY 2019. [DOI: 10.1016/j.ecocom.2019.100768] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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High-Dimensional LASSO-Based Computational Regression Models: Regularization, Shrinkage, and Selection. MACHINE LEARNING AND KNOWLEDGE EXTRACTION 2019. [DOI: 10.3390/make1010021] [Citation(s) in RCA: 32] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Regression models are a form of supervised learning methods that are important for machine learning, statistics, and general data science. Despite the fact that classical ordinary least squares (OLS) regression models have been known for a long time, in recent years there are many new developments that extend this model significantly. Above all, the least absolute shrinkage and selection operator (LASSO) model gained considerable interest. In this paper, we review general regression models with a focus on the LASSO and extensions thereof, including the adaptive LASSO, elastic net, and group LASSO. We discuss the regularization terms responsible for inducing coefficient shrinkage and variable selection leading to improved performance metrics of these regression models. This makes these modern, computational regression models valuable tools for analyzing high-dimensional problems.
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21
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Chen Y, Ong JHY, Rajarethinam J, Yap G, Ng LC, Cook AR. Neighbourhood level real-time forecasting of dengue cases in tropical urban Singapore. BMC Med 2018; 16:129. [PMID: 30078378 PMCID: PMC6091171 DOI: 10.1186/s12916-018-1108-5] [Citation(s) in RCA: 47] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/28/2018] [Accepted: 06/21/2018] [Indexed: 12/27/2022] Open
Abstract
BACKGROUND Dengue, a vector-borne infectious disease caused by the dengue virus, has spread through tropical and subtropical regions of the world. All four serotypes of dengue viruses are endemic in the equatorial city state of Singapore, and frequent localised outbreaks occur, sometimes leading to national epidemics. Vector control remains the primary and most effective measure for dengue control and prevention. The objective of this study is to develop a novel framework for producing a spatio-temporal dengue forecast at a neighbourhood level spatial resolution that can be routinely used by Singapore's government agencies for planning of vector control for best efficiency. METHODS The forecasting algorithm uses a mixture of purely spatial, purely temporal and spatio-temporal data to derive dynamic risk maps for dengue transmission. LASSO-based regression was used for the prediction models and separate sub-models were constructed for each forecast window. Data were divided into training and testing sets for out-of-sample validation. Neighbourhoods were categorised as high or low risk based on the forecast number of cases within the cell. The predictive accuracy of the categorisation was measured. RESULTS Close concordance between the projections and the eventual incidence of dengue were observed. The average Matthew's correlation coefficient for a classification of the upper risk decile (operational capacity) is similar to the predictive performance at the optimal 30% cut-off. The quality of the spatial predictive algorithm as a classifier shows areas under the curve at all forecast windows being above 0.75 and above 0.80 within the next month. CONCLUSIONS Spatially resolved forecasts of geographically structured diseases like dengue can be obtained at a neighbourhood level in highly urban environments at a precision that is suitable for guiding control efforts. The same method can be adapted to other urban and even rural areas, with appropriate adjustment to the grid size and shape.
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Affiliation(s)
- Yirong Chen
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, 12 Science Drive 2, Singapore, 117549 Singapore
| | - Janet Hui Yi Ong
- Environmental Health Institute, 11 Biopolis Way, Singapore, 138667 Singapore
| | | | - Grace Yap
- Environmental Health Institute, 11 Biopolis Way, Singapore, 138667 Singapore
| | - Lee Ching Ng
- Environmental Health Institute, 11 Biopolis Way, Singapore, 138667 Singapore
| | - Alex R. Cook
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, 12 Science Drive 2, Singapore, 117549 Singapore
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