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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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Wang Y, Gao C, Zhao T, Jiao H, Liao Y, Hu Z, Wang L. A comparative study of three models to analyze the impact of air pollutants on the number of pulmonary tuberculosis cases in Urumqi, Xinjiang. PLoS One 2023; 18:e0277314. [PMID: 36649267 PMCID: PMC9844834 DOI: 10.1371/journal.pone.0277314] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2022] [Accepted: 10/25/2022] [Indexed: 01/18/2023] Open
Abstract
In this paper, we separately constructed ARIMA, ARIMAX, and RNN models to determine whether there exists an impact of the air pollutants (such as PM2.5, PM10, CO, O3, NO2, and SO2) on the number of pulmonary tuberculosis cases from January 2014 to December 2018 in Urumqi, Xinjiang. In addition, by using a new comprehensive evaluation index DISO to compare the performance of three models, it was demonstrated that ARIMAX (1,1,2) × (0,1,1)12 + PM2.5 (lag = 12) model was the optimal one, which was applied to predict the number of pulmonary tuberculosis cases in Urumqi from January 2019 to December 2019. The predicting results were in good agreement with the actual pulmonary tuberculosis cases and shown that pulmonary tuberculosis cases obviously declined, which indicated that the policies of environmental protection and universal health checkups in Urumqi have been very effective in recent years.
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Affiliation(s)
- Yingdan Wang
- College of Public Health, Xinjiang Medical University, Urumqi, Xinjiang, China
| | - Chunjie Gao
- College of Public Health, Xinjiang Medical University, Urumqi, Xinjiang, China
| | - Tiantian Zhao
- Department of Infection Prevention and Control, Puyang People’s Hospital, Puyang, Henan, China
| | - Haiyan Jiao
- College of Public Health, Xinjiang Medical University, Urumqi, Xinjiang, China
| | - Ying Liao
- College of Public Health, Xinjiang Medical University, Urumqi, Xinjiang, China
| | - Zengyun Hu
- State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi, Xinjiang, China
| | - Lei Wang
- Department of Medical Engineering and Technology, Xinjiang Medical University, Urumqi, Xinjiang, China
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Guo W, Xu D, Cong S, Du Z, Li L, Zhang M, Feng C, Bao G, Sun H, Yang Z, Ma S. Co-infection and enterovirus B: post EV-A71 mass vaccination scenario in China. BMC Infect Dis 2022; 22:671. [PMID: 35927711 PMCID: PMC9354358 DOI: 10.1186/s12879-022-07661-3] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2021] [Accepted: 07/30/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Hand, foot, and mouth disease (HFMD) is a common child infectious disease caused by more than 20 enterovirus (EV) serotypes. In recent years, enterovirus A71 (EV-A71) has been replaced by Coxsackievirus A6 (CV-A6) to become the predominant serotype. Multiple EV serotypes co-circulate in HFMD epidemics, and this study aimed to investigate the etiological epidemic characteristics of an HFMD outbreak in Kunming, China in 2019. METHODS The clinical samples of 459 EV-associated HFMD patients in 2019 were used to amplify the VP1 gene region by the three sets of primers and identify serotypes using the molecular biology method. Phylogenetic analyses were performed based on the VP1 gene. RESULTS Three hundred and forty-eight cases out of 459 HFMD patients were confirmed as EV infection. Of these 191 (41.61%) were single EV infections and 34.20% had co-infections. The EVs were assigned to 18 EV serotypes, of which CV-A6 was predominant (11.33%), followed by CV-B1 (8.93%), CV-A4 (5.23%), CV-A9 (4.58%), CV-A 16 (3.49%) and CV-A10 and CVA5 both 1.96%. Co-infection of CV-A6 with other EVs was present in 15.25% of these cases, followed by co-infection with CV-A16 and other EVs. The VP1 sequences used in the phylogenetic analyses showed that the CV-A6, CV-B1 and CV-A4 sequences belonged to the sub-genogroup D3 and genogroups F and E, respectively. CONCLUSION Co-circulation and co-infection of multiple serotypes were the etiological characteristic of the HFMD epidemic in Kunming China in 2019 with CV-A-6, CV-B1 and CV-A4 as the predominant serotypes. This is the first report of CV-B1 as a predominant serotype in China and may provide valuable information for the diagnosis, prevention and control of HFMD.
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Affiliation(s)
- Wei Guo
- Institute of Medical Biology, Chinese Academy of Medical Sciences, and Peking Union Medical College (CAMS & PUMC), 935 Jiao Ling Road, Kunming, 650118, Yunnan Province, People's Republic of China.,Yunnan Key Laboratory of Vaccine Research Development on Severe Infectious Disease, Kunming, 650118, People's Republic of China
| | - Danhan Xu
- Institute of Medical Biology, Chinese Academy of Medical Sciences, and Peking Union Medical College (CAMS & PUMC), 935 Jiao Ling Road, Kunming, 650118, Yunnan Province, People's Republic of China.,Yunnan Key Laboratory of Vaccine Research Development on Severe Infectious Disease, Kunming, 650118, People's Republic of China
| | - Shanri Cong
- Institute of Medical Biology, Chinese Academy of Medical Sciences, and Peking Union Medical College (CAMS & PUMC), 935 Jiao Ling Road, Kunming, 650118, Yunnan Province, People's Republic of China.,Yunnan Key Laboratory of Vaccine Research Development on Severe Infectious Disease, Kunming, 650118, People's Republic of China
| | - Zengqing Du
- Department of Infectious Diseases, Kunming Children's Hospital, Kunming, China
| | - Li Li
- Department of Clinical Laboratory, Kunming Maternal and Child Health Hospital, Kunming, 650031, China
| | - Ming Zhang
- Institute of Medical Biology, Chinese Academy of Medical Sciences, and Peking Union Medical College (CAMS & PUMC), 935 Jiao Ling Road, Kunming, 650118, Yunnan Province, People's Republic of China.,Yunnan Key Laboratory of Vaccine Research Development on Severe Infectious Disease, Kunming, 650118, People's Republic of China
| | - Changzeng Feng
- Institute of Medical Biology, Chinese Academy of Medical Sciences, and Peking Union Medical College (CAMS & PUMC), 935 Jiao Ling Road, Kunming, 650118, Yunnan Province, People's Republic of China.,Yunnan Key Laboratory of Vaccine Research Development on Severe Infectious Disease, Kunming, 650118, People's Republic of China
| | - Guohong Bao
- First People's Hospital of Yunnan Province, Kunming, 650032, People's Republic of China
| | - Hao Sun
- Institute of Medical Biology, Chinese Academy of Medical Sciences, and Peking Union Medical College (CAMS & PUMC), 935 Jiao Ling Road, Kunming, 650118, Yunnan Province, People's Republic of China.,Yunnan Key Laboratory of Vaccine Research Development on Severe Infectious Disease, Kunming, 650118, People's Republic of China
| | - Zhaoqing Yang
- Institute of Medical Biology, Chinese Academy of Medical Sciences, and Peking Union Medical College (CAMS & PUMC), 935 Jiao Ling Road, Kunming, 650118, Yunnan Province, People's Republic of China.,Yunnan Key Laboratory of Vaccine Research Development on Severe Infectious Disease, Kunming, 650118, People's Republic of China
| | - Shaohui Ma
- Institute of Medical Biology, Chinese Academy of Medical Sciences, and Peking Union Medical College (CAMS & PUMC), 935 Jiao Ling Road, Kunming, 650118, Yunnan Province, People's Republic of China. .,Yunnan Key Laboratory of Vaccine Research Development on Severe Infectious Disease, Kunming, 650118, People's Republic of China.
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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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Development of deep learning-based detecting systems for pathologic myopia using retinal fundus images. Commun Biol 2021; 4:1225. [PMID: 34702997 PMCID: PMC8548495 DOI: 10.1038/s42003-021-02758-y] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2020] [Accepted: 10/06/2021] [Indexed: 12/31/2022] Open
Abstract
Globally, cases of myopia have reached epidemic levels. High myopia and pathological myopia (PM) are the leading cause of visual impairment and blindness in China, demanding a large volume of myopia screening tasks to control the rapid growing myopic prevalence. It is desirable to develop the automatically intelligent system to facilitate these time- and labor- consuming tasks. In this study, we designed a series of deep learning systems to detect PM and myopic macular lesions according to a recent international photographic classification system (META-PM) classification based on color fundus images. Notably, our systems recorded robust performance both in the test and external validation dataset. The performance was comparable to the general ophthalmologist and retinal specialist. With the extensive adoption of this technology, effective mass screening for myopic population will become feasible on a national scale. Lu et al. develop a deep-learning based detection system for identifying pathological myopia and myopic macular legions in retinal fundus images. The system performance is comparable to human experts, but much faster, easing the burden of human time on screening for myopia.
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Emami N, Ferdousi R. AptaNet as a deep learning approach for aptamer-protein interaction prediction. Sci Rep 2021; 11:6074. [PMID: 33727685 PMCID: PMC7971039 DOI: 10.1038/s41598-021-85629-0] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2020] [Accepted: 03/03/2021] [Indexed: 02/08/2023] Open
Abstract
Aptamers are short oligonucleotides (DNA/RNA) or peptide molecules that can selectively bind to their specific targets with high specificity and affinity. As a powerful new class of amino acid ligands, aptamers have high potentials in biosensing, therapeutic, and diagnostic fields. Here, we present AptaNet-a new deep neural network-to predict the aptamer-protein interaction pairs by integrating features derived from both aptamers and the target proteins. Aptamers were encoded by using two different strategies, including k-mer and reverse complement k-mer frequency. Amino acid composition (AAC) and pseudo amino acid composition (PseAAC) were applied to represent target information using 24 physicochemical and conformational properties of the proteins. To handle the imbalance problem in the data, we applied a neighborhood cleaning algorithm. The predictor was constructed based on a deep neural network, and optimal features were selected using the random forest algorithm. As a result, 99.79% accuracy was achieved for the training dataset, and 91.38% accuracy was obtained for the testing dataset. AptaNet achieved high performance on our constructed aptamer-protein benchmark dataset. The results indicate that AptaNet can help identify novel aptamer-protein interacting pairs and build more-efficient insights into the relationship between aptamers and proteins. Our benchmark dataset and the source codes for AptaNet are available in: https://github.com/nedaemami/AptaNet .
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Affiliation(s)
- Neda Emami
- Department of Health Information Technology, School of Management and Medical Informatics, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Reza Ferdousi
- Department of Health Information Technology, School of Management and Medical Informatics, Tabriz University of Medical Sciences, Tabriz, Iran.
- Research Center for Pharmaceutical Nanotechnology, Biomedicine Institute, Tabriz University of Medical Sciences, Tabriz, Iran.
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