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Ding H, Xing F, Zou L, Zhao L. QSAR analysis of VEGFR-2 inhibitors based on machine learning, Topomer CoMFA and molecule docking. BMC Chem 2024; 18:59. [PMID: 38555462 PMCID: PMC10981835 DOI: 10.1186/s13065-024-01165-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2023] [Accepted: 03/12/2024] [Indexed: 04/02/2024] Open
Abstract
VEGFR-2 kinase inhibitors are clinically approved drugs that can effectively target cancer angiogenesis. However, such inhibitors have adverse effects such as skin toxicity, gastrointestinal reactions and hepatic impairment. In this study, machine learning and Topomer CoMFA, which is an alignment-dependent, descriptor-based method, were employed to build structural activity relationship models of potentially new VEGFR-2 inhibitors. The prediction ac-curacy of the training and test sets of the 2D-SAR model were 82.4 and 80.1%, respectively, with KNN. Topomer CoMFA approach was then used for 3D-QSAR modeling of VEGFR-2 inhibitors. The coefficient of q2 for cross-validation of the model 1 was greater than 0.5, suggesting that a stable drug activity-prediction model was obtained. Molecular docking was further performed to simulate the interactions between the five most promising compounds and VEGFR-2 target protein and the Total Scores were all greater than 6, indicating that they had a strong hydrogen bond interactions were present. This study successfully used machine learning to obtain five potentially novel VEGFR-2 inhibitors to increase our arsenal of drugs to combat cancer.
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Affiliation(s)
- Hao Ding
- Department of Ultrasound, Shengjing Hospital of China Medical University, Shenyang, 110004, Liaoning, China
| | - Fei Xing
- Department of Oncology, Shengjing Hospital of China Medical University, Shenyang, 110004, Liaoning, China
| | - Lin Zou
- Medical College of Guangxi University, Nanning, 530004, Guangxi, China
| | - Liang Zhao
- Hepatobiliary and Splenic Surgery Ward, Department of General Surgery, Shengjing Hospital of China Medical University, Shenyang, 110004, Liaoning, China.
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2
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Shen Y, Huang J, Jia L, Zhang C, Xu J. Bioinformatics and machine learning driven key genes screening for hepatocellular carcinoma. Biochem Biophys Rep 2024; 37:101587. [PMID: 38107663 PMCID: PMC10724547 DOI: 10.1016/j.bbrep.2023.101587] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2023] [Revised: 11/01/2023] [Accepted: 11/17/2023] [Indexed: 12/19/2023] Open
Abstract
Liver cancer, a global menace, ranked as the sixth most prevalent and third deadliest cancer in 2020. The challenge of early diagnosis and treatment, especially for hepatocellular carcinoma (HCC), persists due to late-stage detections. Understanding HCC's complex pathogenesis is vital for advancing diagnostics and therapies. This study combines bioinformatics and machine learning, examining HCC comprehensively. Three datasets underwent meticulous scrutiny, employing various analytical tools such as Gene Ontology (GO) function and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis, protein interaction assessment, and survival analysis. These rigorous investigations uncovered twelve pivotal genes intricately linked with HCC's pathophysiological intricacies. Among them, CYP2C8, CYP2C9, EPHX2, and ESR1 were significantly positively correlated with overall patient survival, while AKR1B10 and NQO1 displayed a negative correlation. Moreover, the Adaboost prediction model yielded an 86.8 % accuracy, showcasing machine learning's potential in deciphering complex dataset patterns for clinically relevant predictions. These findings promise to contribute valuable insights into the elusive mechanisms driving liver cancer (HCC). They hold the potential to guide the development of more precise diagnostic methods and treatment strategies in the future. In the fight against this global health challenge, unraveling HCC's intricacies is of paramount importance.
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Affiliation(s)
- Ye Shen
- Department of Radiology, Wujin Hospital Affiliated with Jiangsu University, Changzhou, 213002, China
| | - Juanjie Huang
- Department of General Surgery, Dongguan Qingxi Hospital, Dongguan, 523660, China
| | - Lei Jia
- International Health Medicine Innovation Center, Shenzhen University, ShenZhen, 518060, China
| | - Chi Zhang
- Huaxia Eye Hospital of Foshan, Huaxia Eye Hospital Group, Foshan, Guangdong, 528000, China
| | - Jianxing Xu
- Department of Radiology, Wujin Hospital Affiliated with Jiangsu University, Changzhou, 213002, China
- Department of Radiology, The Wujin Clinical College of Xuzhou Medical University, Changzhou, 213002, China
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3
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Lieberman B, Kong JD, Gusinow R, Asgary A, Bragazzi NL, Choma J, Dahbi SE, Hayashi K, Kar D, Kawonga M, Mbada M, Monnakgotla K, Orbinski J, Ruan X, Stevenson F, Wu J, Mellado B. Big data- and artificial intelligence-based hot-spot analysis of COVID-19: Gauteng, South Africa, as a case study. BMC Med Inform Decis Mak 2023; 23:19. [PMID: 36703133 PMCID: PMC9879257 DOI: 10.1186/s12911-023-02098-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2022] [Accepted: 01/02/2023] [Indexed: 01/27/2023] Open
Abstract
The coronavirus disease 2019 (COVID-19) has developed into a pandemic. Data-driven techniques can be used to inform and guide public health decision- and policy-makers. In generalizing the spread of a virus over a large area, such as a province, it must be assumed that the transmission occurs as a stochastic process. It is therefore very difficult for policy and decision makers to understand and visualize the location specific dynamics of the virus on a more granular level. A primary concern is exposing local virus hot-spots, in order to inform and implement non-pharmaceutical interventions. A hot-spot is defined as an area experiencing exponential growth relative to the generalised growth of the pandemic. This paper uses the first and second waves of the COVID-19 epidemic in Gauteng Province, South Africa, as a case study. The study aims provide a data-driven methodology and comprehensive case study to expose location specific virus dynamics within a given area. The methodology uses an unsupervised Gaussian Mixture model to cluster cases at a desired granularity. This is combined with an epidemiological analysis to quantify each cluster's severity, progression and whether it can be defined as a hot-spot.
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Affiliation(s)
- Benjamin Lieberman
- grid.11951.3d0000 0004 1937 1135School of Physics and Institute for Collider Particle Physics, University of the Witwatersrand, Johannesburg, South Africa ,Africa-Canada Artificial Intelligence and Data Innovation Consortium (ACADIC), Toronto, Canada
| | - Jude Dzevela Kong
- grid.21100.320000 0004 1936 9430Department of Mathematics and Statistics, York University, Toronto, Canada ,Africa-Canada Artificial Intelligence and Data Innovation Consortium (ACADIC), Toronto, Canada
| | - Roy Gusinow
- grid.11951.3d0000 0004 1937 1135School of Physics and Institute for Collider Particle Physics, University of the Witwatersrand, Johannesburg, South Africa ,Africa-Canada Artificial Intelligence and Data Innovation Consortium (ACADIC), Toronto, Canada
| | - Ali Asgary
- grid.21100.320000 0004 1936 9430Disaster and Emergency Management, School of Administrative Studies and Advanced Disaster, Emergency and Rapid-response Simulation, York University, Toronto, Canada ,Africa-Canada Artificial Intelligence and Data Innovation Consortium (ACADIC), Toronto, Canada
| | - Nicola Luigi Bragazzi
- grid.21100.320000 0004 1936 9430Department of Mathematics and Statistics, York University, Toronto, Canada ,grid.21100.320000 0004 1936 9430Laboratory for Industrial and Applied Mathematics (LIAM), York University, Toronto, Canada ,Africa-Canada Artificial Intelligence and Data Innovation Consortium (ACADIC), Toronto, Canada
| | - Joshua Choma
- grid.11951.3d0000 0004 1937 1135School of Physics and Institute for Collider Particle Physics, University of the Witwatersrand, Johannesburg, South Africa ,Africa-Canada Artificial Intelligence and Data Innovation Consortium (ACADIC), Toronto, Canada
| | - Salah-Eddine Dahbi
- grid.11951.3d0000 0004 1937 1135School of Physics and Institute for Collider Particle Physics, University of the Witwatersrand, Johannesburg, South Africa ,Africa-Canada Artificial Intelligence and Data Innovation Consortium (ACADIC), Toronto, Canada
| | - Kentaro Hayashi
- grid.11951.3d0000 0004 1937 1135School of Computer Science and Applied Mathematics, University of the Witwatersrand, Johannesburg, South Africa ,Africa-Canada Artificial Intelligence and Data Innovation Consortium (ACADIC), Toronto, Canada
| | - Deepak Kar
- grid.11951.3d0000 0004 1937 1135School of Physics and Institute for Collider Particle Physics, University of the Witwatersrand, Johannesburg, South Africa ,Africa-Canada Artificial Intelligence and Data Innovation Consortium (ACADIC), Toronto, Canada
| | - Mary Kawonga
- grid.11951.3d0000 0004 1937 1135School of Public Health, University of the Witwatersrand, Johannesburg, South Africa ,Gauteng Provincial Department of Health, Johannesburg, South Africa ,Africa-Canada Artificial Intelligence and Data Innovation Consortium (ACADIC), Toronto, Canada
| | - Mduduzi Mbada
- Africa-Canada Artificial Intelligence and Data Innovation Consortium (ACADIC), Toronto, Canada ,Gauteng Office of the Premier, Johannesburg, South Africa
| | - Kgomotso Monnakgotla
- grid.11951.3d0000 0004 1937 1135School of Physics and Institute for Collider Particle Physics, University of the Witwatersrand, Johannesburg, South Africa ,Africa-Canada Artificial Intelligence and Data Innovation Consortium (ACADIC), Toronto, Canada
| | - James Orbinski
- Africa-Canada Artificial Intelligence and Data Innovation Consortium (ACADIC), Toronto, Canada ,grid.21100.320000 0004 1936 9430Dahdaleh Institute for Global Health Research, York University, Toronto, Canada
| | - Xifeng Ruan
- grid.11951.3d0000 0004 1937 1135School of Physics and Institute for Collider Particle Physics, University of the Witwatersrand, Johannesburg, South Africa ,Africa-Canada Artificial Intelligence and Data Innovation Consortium (ACADIC), Toronto, Canada
| | - Finn Stevenson
- grid.11951.3d0000 0004 1937 1135School of Physics and Institute for Collider Particle Physics, University of the Witwatersrand, Johannesburg, South Africa ,Africa-Canada Artificial Intelligence and Data Innovation Consortium (ACADIC), Toronto, Canada
| | - Jianhong Wu
- grid.21100.320000 0004 1936 9430Department of Mathematics and Statistics, York University, Toronto, Canada ,grid.21100.320000 0004 1936 9430Laboratory for Industrial and Applied Mathematics (LIAM), York University, Toronto, Canada ,Africa-Canada Artificial Intelligence and Data Innovation Consortium (ACADIC), Toronto, Canada
| | - Bruce Mellado
- grid.11951.3d0000 0004 1937 1135School of Physics and Institute for Collider Particle Physics, University of the Witwatersrand, Johannesburg, South Africa ,Africa-Canada Artificial Intelligence and Data Innovation Consortium (ACADIC), Toronto, Canada ,grid.462638.d0000 0001 0696 719XiThemba LABS, National Research Foundation, Somerset West, South Africa
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4
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Saegner T, Austys D. Forecasting and Surveillance of COVID-19 Spread Using Google Trends: Literature Review. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:12394. [PMID: 36231693 PMCID: PMC9566212 DOI: 10.3390/ijerph191912394] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/19/2022] [Revised: 09/23/2022] [Accepted: 09/26/2022] [Indexed: 06/16/2023]
Abstract
The probability of future Coronavirus Disease (COVID)-19 waves remains high, thus COVID-19 surveillance and forecasting remains important. Online search engines harvest vast amounts of data from the general population in real time and make these data publicly accessible via such tools as Google Trends (GT). Therefore, the aim of this study was to review the literature about possible use of GT for COVID-19 surveillance and prediction of its outbreaks. We collected and reviewed articles about the possible use of GT for COVID-19 surveillance published in the first 2 years of the pandemic. We resulted in 54 publications that were used in this review. The majority of the studies (83.3%) included in this review showed positive results of the possible use of GT for forecasting COVID-19 outbreaks. Most of the studies were performed in English-speaking countries (61.1%). The most frequently used keyword was "coronavirus" (53.7%), followed by "COVID-19" (31.5%) and "COVID" (20.4%). Many authors have made analyses in multiple countries (46.3%) and obtained the same results for the majority of them, thus showing the robustness of the chosen methods. Various methods including long short-term memory (3.7%), random forest regression (3.7%), Adaboost algorithm (1.9%), autoregressive integrated moving average, neural network autoregression (1.9%), and vector error correction modeling (1.9%) were used for the analysis. It was seen that most of the publications with positive results (72.2%) were using data from the first wave of the COVID-19 pandemic. Later, the search volumes reduced even though the incidence peaked. In most countries, the use of GT data showed to be beneficial for forecasting and surveillance of COVID-19 spread.
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Affiliation(s)
- Tobias Saegner
- Department of Public Health, Institute of Health Sciences, Faculty of Medicine, Vilnius University, M. K. Čiurlionio 21/27, LT-03101 Vilnius, Lithuania
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5
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Zhang Y, Zhang Q, Zhao Y, Deng Y, Zheng H. Urban spatial risk prediction and optimization analysis of POI based on deep learning from the perspective of an epidemic. INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION : ITC JOURNAL 2022; 112:102942. [PMID: 35945962 PMCID: PMC9353319 DOI: 10.1016/j.jag.2022.102942] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/17/2022] [Revised: 07/19/2022] [Accepted: 07/27/2022] [Indexed: 06/15/2023]
Abstract
From an epidemiological perspective, previous research on COVID-19 has generally been based on classical statistical analyses. As a result, spatial information is often not used effectively. This paper uses image-based neural networks to explore the relationship between urban spatial risk and the distribution of infected populations, and the design of urban facilities. To achieve this objective, we use spatio-temporal data of people infected with new coronary pneumonia prior to 28 February 2020 in Wuhan. We then use kriging, which is a method of spatial interpolation, as well as core density estimation technology to establish the epidemic heat distribution on fine grid units. We further evaluate the influence of nine major spatial risk factors, including the distribution of agencies, hospitals, park squares, sports fields, banks and hotels, by testing them for significant positive correlation with the distribution of the epidemic. The weights of these spatial risk factors are used for training Generative Adversarial Network (GAN) models, which predict the distribution of cases in a given area. The input image for the machine learning model is a city plan converted by public infrastructures, and the output image is a map of urban spatial risk factors in the given area. The results of the trained model demonstrate that optimising the relevant point of interests (POI) in urban areas to effectively control potential risk factors can aid in managing the epidemic and preventing it from dispersing further.
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Affiliation(s)
- Yecheng Zhang
- College of Architecture & Art, Hefei University of Technology, Hefei, China
| | - Qimin Zhang
- School of Mechanical Engineering, Hefei University of Technology, Hefei, China
| | - Yuxuan Zhao
- College of Architecture & Art, Hefei University of Technology, Hefei, China
| | - Yunjie Deng
- College of Architecture & Art, Hefei University of Technology, Hefei, China
| | - Hao Zheng
- Stuart Weitzman School of Design, University of Pennsylvania, Philadelphia, United States
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6
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Nazia N, Butt ZA, Bedard ML, Tang WC, Sehar H, Law J. Methods Used in the Spatial and Spatiotemporal Analysis of COVID-19 Epidemiology: A Systematic Review. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:8267. [PMID: 35886114 PMCID: PMC9324591 DOI: 10.3390/ijerph19148267] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/04/2022] [Revised: 07/01/2022] [Accepted: 07/04/2022] [Indexed: 02/04/2023]
Abstract
The spread of the COVID-19 pandemic was spatially heterogeneous around the world; the transmission of the disease is driven by complex spatial and temporal variations in socioenvironmental factors. Spatial tools are useful in supporting COVID-19 control programs. A substantive review of the merits of the methodological approaches used to understand the spatial epidemiology of the disease is hardly undertaken. In this study, we reviewed the methodological approaches used to identify the spatial and spatiotemporal variations of COVID-19 and the socioeconomic, demographic and climatic drivers of such variations. We conducted a systematic literature search of spatial studies of COVID-19 published in English from Embase, Scopus, Medline, and Web of Science databases from 1 January 2019 to 7 September 2021. Methodological quality assessments were also performed using the Joanna Briggs Institute (JBI) risk of bias tool. A total of 154 studies met the inclusion criteria that used frequentist (85%) and Bayesian (15%) modelling approaches to identify spatial clusters and the associated risk factors. Bayesian models in the studies incorporated various spatial, temporal and spatiotemporal effects into the modelling schemes. This review highlighted the need for more local-level advanced Bayesian spatiotemporal modelling through the multi-level framework for COVID-19 prevention and control strategies.
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Affiliation(s)
- Nushrat Nazia
- School of Public Health Sciences, University of Waterloo, 200 University Ave West, Waterloo, ON N2L 3G1, Canada; (Z.A.B.); (M.L.B.); (W.-C.T.); (H.S.); (J.L.)
| | - Zahid Ahmad Butt
- School of Public Health Sciences, University of Waterloo, 200 University Ave West, Waterloo, ON N2L 3G1, Canada; (Z.A.B.); (M.L.B.); (W.-C.T.); (H.S.); (J.L.)
| | - Melanie Lyn Bedard
- School of Public Health Sciences, University of Waterloo, 200 University Ave West, Waterloo, ON N2L 3G1, Canada; (Z.A.B.); (M.L.B.); (W.-C.T.); (H.S.); (J.L.)
| | - Wang-Choi Tang
- School of Public Health Sciences, University of Waterloo, 200 University Ave West, Waterloo, ON N2L 3G1, Canada; (Z.A.B.); (M.L.B.); (W.-C.T.); (H.S.); (J.L.)
| | - Hibah Sehar
- School of Public Health Sciences, University of Waterloo, 200 University Ave West, Waterloo, ON N2L 3G1, Canada; (Z.A.B.); (M.L.B.); (W.-C.T.); (H.S.); (J.L.)
| | - Jane Law
- School of Public Health Sciences, University of Waterloo, 200 University Ave West, Waterloo, ON N2L 3G1, Canada; (Z.A.B.); (M.L.B.); (W.-C.T.); (H.S.); (J.L.)
- School of Planning, University of Waterloo, 200 University Ave West, Waterloo, ON N2L 3G1, Canada
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7
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Zuo Q, Du J, Di B, Zhou J, Zhang L, Liu H, Hou X. Research on Spatial-temporal Spread and Risk Profile of the COVID-19 Epidemic Based on Mobile Phone Trajectory Data. Front Big Data 2022; 5:705698. [PMID: 35574574 PMCID: PMC9092495 DOI: 10.3389/fdata.2022.705698] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2021] [Accepted: 03/23/2022] [Indexed: 12/03/2022] Open
Abstract
The COVID-19 epidemic poses a significant challenge to the operation of society and the resumption of work and production. How to quickly track the resident location and activity trajectory of the population, and identify the spread risk of the COVID-19 in geospatial space has important theoretical and practical significance for controlling the spread of the virus on a large scale. In this study, we take the geographical community as the research object, and use the mobile phone trajectory data to construct the spatiotemporal profile of the potential high-risk population. First, by using the spatiotemporal data collision method, identify, and recover the trajectories of the people who were in the same area with the confirmed patients during the same time. Then, based on the range of activities of both cohorts (the confirmed cases and the potentially infected groups), we analyze the risk level of the relevant places and evaluate the scale of potential spread. Finally, we calculate the probability of infection for different communities and construct the spatiotemporal profile for the transmission to help guide the distribution of preventive materials and human resources. The proposed method is verified using survey data of 10 confirmed cases and statistical data of 96 high-risk neighborhoods in Chengdu, China, between 15 January 2020 and 15 February 2020. The analysis finds that the method accurately simulates the spatiotemporal spread of the epidemic in Chengdu and measures the risk level in specific areas, which provides an objective basis for the government and relevant parties to plan and manage the prevention and control of the epidemic.
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Affiliation(s)
- Qi Zuo
- Institute for Disaster Management and Reconstruction, Sichuan University, Chengdu, China
- *Correspondence: Qi Zuo
| | - Jiaman Du
- The School of International Studies, Sichuan University, Chengdu, China
| | - Baofeng Di
- Institute for Disaster Management and Reconstruction, Sichuan University, Chengdu, China
| | - Junrong Zhou
- Chengdu Fangwei Technology Co., Ltd., Chengdu, China
| | - Lixia Zhang
- Sichuan Wisesoft System Integration Co., Ltd., Chengdu, China
| | - Hongxia Liu
- West China Hospital, Sichuan University, Chengdu, China
| | - Xiaoyu Hou
- SinoMaps Press Co., Ltd., Beijing, China
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8
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Faisal K, Alshammari S, Alotaibi R, Alhothali A, Bamasag O, Alghanmi N, Bin Yamin M. Spatial Analysis of COVID-19 Vaccine Centers Distribution: A Case Study of the City of Jeddah, Saudi Arabia. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:3526. [PMID: 35329216 PMCID: PMC8948971 DOI: 10.3390/ijerph19063526] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/14/2022] [Revised: 03/12/2022] [Accepted: 03/14/2022] [Indexed: 02/04/2023]
Abstract
The COVID-19 pandemic is one of the most devastating public health emergencies in history. In late 2020 and after almost a year from the initial outbreak of the novel coronavirus (SARS-CoV-2), several vaccines were approved and administered in most countries. Saudi Arabia has established COVID-19 vaccination centers in all regions. Various facilities were selected to set up these vaccination centers, including conference and exhibition centers, old airport terminals, pre-existing medical facilities, and primary healthcare centers. Deciding the number and locations of these facilities is a fundamental objective for successful epidemic responses to ensure the delivery of vaccines and other health services to the entire population. This study analyzed the spatial distribution of COVID-19 vaccination centers in Jeddah, a major city in Saudi Arabia, by using GIS tools and methods to provide insight on the effectiveness of the selection and distribution of the COVID-19 vaccination centers in terms of accessibility and coverage. Based on a spatial analysis of vaccine centers' coverage in 2020 and 2021 in Jeddah presented in this study, coverage deficiency would have been addressed earlier if the applied GIS analysis methods had been used by authorities while gradually increasing the number of vaccination centers. This study recommends that the Ministry of Health in Saudi Arabia evaluated the assigned vaccination centers to include the less-populated regions and to ensure equity and fairness in vaccine distribution. Adding more vaccine centers or reallocating some existing centers in the denser districts to increase the coverage in the uncovered sparse regions in Jeddah is also recommended. The methods applied in this study could be part of a strategic vaccination administration program for future public health emergencies and other vaccination campaigns.
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Affiliation(s)
- Kamil Faisal
- Geomatics Department, Faculty of Architecture and Planning, King Abdulaziz University, Jeddah 21589, Saudi Arabia
| | - Sultanah Alshammari
- Department of Computer Science, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia; (A.A.); (O.B.)
| | - Reem Alotaibi
- Department of Information Technology, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia; (R.A.); (N.A.)
| | - Areej Alhothali
- Department of Computer Science, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia; (A.A.); (O.B.)
| | - Omaimah Bamasag
- Department of Computer Science, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia; (A.A.); (O.B.)
| | - Nusaybah Alghanmi
- Department of Information Technology, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia; (R.A.); (N.A.)
| | - Manal Bin Yamin
- Planning and Transformation Department, Ministry of Health, Jeddah 21176, Saudi Arabia;
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9
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Zhou L, Wang H. A Combined Feature Screening Approach of Random Forest and Filter-based Methods for Ultra-high Dimensional Data. Curr Bioinform 2022. [DOI: 10.2174/1574893617666220221120618] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
Background:
Various feature (variable) screening approaches have been proposed in the past decade to mitigate the impact of ultra-high dimensionality in classification and regression problems, including filter based methods such as sure indepen¬dence screening, and wrapper based methods such random forest. However, the former type of methods rely heavily on strong modelling assumptions while the latter ones requires an adequate sample size to make the data speak for themselves. These require¬ments can seldom be met in biochemical studies in cases where we have only access to ultra-high dimensional data with a complex structure and a small number of observations.
Objective:
In this research, we want to investigate the possibility of combing both filter based screening methods and random forest based screening methods in the regression context.
Method:
We have combined four state-of-art filter approaches, namely, sure independence screening (SIS) , robust rank corre¬lation based screening (RRCS), high dimensional ordinary least squares projection (HOLP) and a model free sure independence screening procedure based on the distance correlation (DCSIS) from the statistical community with a random forest based Boruta screening method from the machine learning community for regression problems.
Result:
Among all combined methods, RF-DCSIS performs better than the other methods in terms of screening accuracy and prediction capability on the simulated scenarios and real benchmark datasets.
Conclusion:
By empirical study from both extensive simulation and real data, we have shown that both filter based screening and random forest based screening have their pros and cons while a combination of both may lead to a better feature screening result and prediction capability
Keywords:
feature screening, filter-based method, ultra-high dimensional data, variable selection, random forest,RF-DCSIS
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Affiliation(s)
- Lifeng Zhou
- School of Economics and Management, Changsha University, China
| | - Hong Wang
- School of Mathematics and Statistics, Central South University, China
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10
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Wang J, Chen J, Zhang S, Ding Y, Wang M, Zhang H, Liang R, Chen Q, Niu B. Risk assessment and integrated surveillance of foot-and-mouth disease outbreaks in Russia based on Monte Carlo simulation. BMC Vet Res 2021; 17:268. [PMID: 34376207 PMCID: PMC8353819 DOI: 10.1186/s12917-021-02967-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2019] [Accepted: 07/16/2021] [Indexed: 11/21/2022] Open
Abstract
Background Foot-and-mouth disease (FMD) is a highly contagious disease of livestock worldwide. Russia is a big agricultural country with a wide geographical area where FMD outbreaks have become an obstacle for the development of the animal and animal products trade. In this study, we aimed to assess the export risk of FMD from Russia. Results After simulation by Monte Carlo, the results showed that the probability of cattle infected with FMD in the surveillance zone (Surrounding the areas where no epidemic disease has occurred within the prescribed time limit, the construction of buffer areas is called surveillance zone.) of Russia was 1.29 × 10− 6. The probability that at least one FMD positive case was exported from Russia per year in the surveillance zone was 6 %. The predicted number of positive cattle of the 39,530 - 50,576 exported from Russia per year was 0.06. A key node in the impact model was the probability of occurrence of FMD outbreaks in the Russian surveillance zone. By semi-quantitative model calculation, the risk probability of FMD defense system defects was 1.84 × 10− 5, indicating that there was a potential risk in the prevention and control measures of FMD in Russia. The spatial time scan model found that the most likely FMD cluster (P < 0.01) was in the Eastern and Siberian Central regions. Conclusions There was a risk of FMDV among cattle exported from Russia, and the infection rate of cattle in the monitored area was the key factor. Understanding the export risk of FMD in Russia and relevant epidemic prevention measures will help policymakers to develop targeted surveillance plans. Supplementary Information The online version contains supplementary material available at 10.1186/s12917-021-02967-x.
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Affiliation(s)
- Jianying Wang
- Shanghai Key Laboratory of Bio-Energy Crops, School of Life Sciences, Shanghai University, 200444, Shanghai, People's Republic of China
| | - Jiahui Chen
- Shanghai Key Laboratory of Bio-Energy Crops, School of Life Sciences, Shanghai University, 200444, Shanghai, People's Republic of China
| | - Shuwen Zhang
- Shanghai Key Laboratory of Bio-Energy Crops, School of Life Sciences, Shanghai University, 200444, Shanghai, People's Republic of China
| | - Yanting Ding
- Shanghai Key Laboratory of Bio-Energy Crops, School of Life Sciences, Shanghai University, 200444, Shanghai, People's Republic of China
| | - Minjia Wang
- Shanghai Key Laboratory of Bio-Energy Crops, School of Life Sciences, Shanghai University, 200444, Shanghai, People's Republic of China
| | - Hui Zhang
- Shanghai Key Laboratory of Bio-Energy Crops, School of Life Sciences, Shanghai University, 200444, Shanghai, People's Republic of China
| | - Ruirui Liang
- Shanghai Key Laboratory of Bio-Energy Crops, School of Life Sciences, Shanghai University, 200444, Shanghai, People's Republic of China
| | - Qin Chen
- Shanghai Key Laboratory of Bio-Energy Crops, School of Life Sciences, Shanghai University, 200444, Shanghai, People's Republic of China.
| | - Bing Niu
- Shanghai Key Laboratory of Bio-Energy Crops, School of Life Sciences, Shanghai University, 200444, Shanghai, People's Republic of China.
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Modeling of Various Spatial Patterns of SARS-CoV-2: The Case of Germany. J Clin Med 2021; 10:jcm10071409. [PMID: 33915790 PMCID: PMC8036509 DOI: 10.3390/jcm10071409] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2021] [Revised: 03/29/2021] [Accepted: 03/30/2021] [Indexed: 12/20/2022] Open
Abstract
Among numerous publications about the SARS-CoV-2, many articles present research from the geographic point of view. The cartographic research method used in this area of science can be successfully applied to analyze the spatiotemporal characteristics of the pandemic using limited data and can be useful for a quick and preliminary assessment of the spread of infections. In this paper, research on the spatial differentiation of the structure and homogeneity of the system in which SARS-CoV-2 occurs, as well as spatial concentration of people infected was undertaken. The phenomena were investigated in a period of two infection waves in Germany: in spring and autumn 2020. We applied the potential model, entropy, centrographic method, and Lorenz curve in spatial analysis. The potentials model made it possible to distinguish core regions with a high level of the growth of new infections, along with areas of their impact, and regions with a low level of generation of new infections. The entropy showed the spatial distribution of differentiation of the studied system and the change of these characteristics between spring and autumn. The concentration method allowed for spatial and numerical demonstration of the concentration of infected population in a given area. We wanted to show that it is possible to draw meaningful conclusions about the pandemic characteristics using only basic data about infections, along with proper cartographic methods. The results can be used to designate the zones of the greatest threats, and thus, the areas where the most intense actions should be taken.
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