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Lv C, Guo W, Yin X, Liu L, Huang X, Li S, Zhang L. Innovative applications of artificial intelligence during the COVID-19 pandemic. INFECTIOUS MEDICINE 2024; 3:100095. [PMID: 38586543 PMCID: PMC10998276 DOI: 10.1016/j.imj.2024.100095] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Revised: 12/16/2023] [Accepted: 02/18/2024] [Indexed: 04/09/2024]
Abstract
The COVID-19 pandemic has created unprecedented challenges worldwide. Artificial intelligence (AI) technologies hold tremendous potential for tackling key aspects of pandemic management and response. In the present review, we discuss the tremendous possibilities of AI technology in addressing the global challenges posed by the COVID-19 pandemic. First, we outline the multiple impacts of the current pandemic on public health, the economy, and society. Next, we focus on the innovative applications of advanced AI technologies in key areas such as COVID-19 prediction, detection, control, and drug discovery for treatment. Specifically, AI-based predictive analytics models can use clinical, epidemiological, and omics data to forecast disease spread and patient outcomes. Additionally, deep neural networks enable rapid diagnosis through medical imaging. Intelligent systems can support risk assessment, decision-making, and social sensing, thereby improving epidemic control and public health policies. Furthermore, high-throughput virtual screening enables AI to accelerate the identification of therapeutic drug candidates and opportunities for drug repurposing. Finally, we discuss future research directions for AI technology in combating COVID-19, emphasizing the importance of interdisciplinary collaboration. Though promising, barriers related to model generalization, data quality, infrastructure readiness, and ethical risks must be addressed to fully translate these innovations into real-world impacts. Multidisciplinary collaboration engaging diverse expertise and stakeholders is imperative for developing robust, responsible, and human-centered AI solutions against COVID-19 and future public health emergencies.
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Affiliation(s)
- Chenrui Lv
- Huazhong Agricultural University, Wuhan 430070, China
| | - Wenqiang Guo
- Huazhong Agricultural University, Wuhan 430070, China
| | - Xinyi Yin
- Huazhong Agricultural University, Wuhan 430070, China
| | - Liu Liu
- National Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention; Chinese Center for Tropical Diseases Research, Shanghai 200001, China
| | - Xinlei Huang
- Huazhong Agricultural University, Wuhan 430070, China
| | - Shimin Li
- Huazhong Agricultural University, Wuhan 430070, China
| | - Li Zhang
- Huazhong Agricultural University, Wuhan 430070, China
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Gao W, Wang Y, Cao J, Liu Y. Final epidemic size and critical times for susceptible-infectious-recovered models with a generalized contact rate. CHAOS (WOODBURY, N.Y.) 2024; 34:013152. [PMID: 38294886 DOI: 10.1063/5.0185707] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/01/2023] [Accepted: 12/27/2023] [Indexed: 02/02/2024]
Abstract
During the spread of an infectious disease, the contact rate or the incidence rate may affect disease characteristics. For simplicity, most disease models assume standard incidence or mass action rates to calculate the basic reproduction number, final epidemic size, and peak time of an epidemic. For standard incidence, the contact rate remains constant resulting in the incidence rate is inversely proportional to the population size, while for the mass action rate, this contact rate is proportional to the total population size resulting in the incidence rate is independent of the population size. In this paper, we consider susceptible-infectious-recovered epidemic models with a generalized contact rate C(N) and a nonlinear incidence rate in view of the behavioral change from susceptible or infectious individuals when an infectious disease appears. The basic reproduction number and the final size equation are derived. The impact of different types of contact rates on them is studied. Moreover, two critical times (peak time and epidemic duration) of an epidemic are considered. Explicit formulas for the peak time and epidemic duration are obtained. These formulas are helpful not only for taking early effective epidemic precautions but also for understanding how the epidemic duration can be changed by acting on the model parameters, especially when the epidemic model is used to make public health policy.
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Affiliation(s)
- Wenhua Gao
- School of Mathematics and Physics, China University of Geosciences, Wuhan 430074, China
| | - Yi Wang
- School of Mathematics and Physics, China University of Geosciences, Wuhan 430074, China
- School of Mathematics, Southeast University, Nanjing 210096, China
| | - Jinde Cao
- School of Mathematics, Southeast University, Nanjing 210096, China
| | - Yang Liu
- Laboratory of Intelligent Education Technology and Application of Zhejiang Province, School of Mathematical Sciences, Zhejiang Normal University, Jinhua 321004, China
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Aslam H, Biswas S. Analysis of COVID-19 Death Cases Using Machine Learning. SN COMPUTER SCIENCE 2023; 4:403. [PMID: 37220559 PMCID: PMC10191086 DOI: 10.1007/s42979-023-01835-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/24/2022] [Accepted: 12/21/2022] [Indexed: 05/25/2023]
Abstract
COVID-19 has threatened the existence of human life for more than the last 2 years. More than 460 million confirmed cases and 6 million deaths have been reported worldwide due to COVID-19. To measure the severity of the COVID-19, the mortality rate plays an important role. Understanding the nature of COVID-19 and forecasting the death cases of COVID-19 require more investigation of the real effect for different risk factors. In this work, various regression machine learning models are proposed to extract the relationship between different factors and the death rate of COVID-19. The optimal regression tree algorithm employed in this work estimates the impact of essential causal variables that significantly affect the mortality rates. We have generated a real-time forecast for the death case of COVID-19 using machine learning techniques. The analysis is evaluated with the well-known regression models XGBoost, Random Forest, and SVM on the data sets of the US, India, Italy, and three continents Asia, Europe, and North America. The results show that the models can be used to forecast the death cases for the near future in case of an epidemic like Novel Coronavirus.
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Affiliation(s)
- Humaira Aslam
- Department of Mathematics, Adamas University, Barasat-Barrackpore Road, Jagannathpur, Kolkata, West Bengal 700126 India
| | - Santanu Biswas
- Department of Mathematics, Adamas University, Barasat-Barrackpore Road, Jagannathpur, Kolkata, West Bengal 700126 India
- Department Of Mathematics, Jadavpur University, Raja Subodh Chandra Mallick Road, Kolkata, West Bengal 700032 India
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Mahalakshmi V, Balobaid A, Kanisha B, Sasirekha R, Ramkumar Raja M. Artificial Intelligence: A Next-Level Approach in Confronting the COVID-19 Pandemic. Healthcare (Basel) 2023; 11:healthcare11060854. [PMID: 36981511 PMCID: PMC10048108 DOI: 10.3390/healthcare11060854] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2023] [Revised: 02/27/2023] [Accepted: 03/02/2023] [Indexed: 03/15/2023] Open
Abstract
The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) which caused coronavirus diseases (COVID-19) in late 2019 in China created a devastating economical loss and loss of human lives. To date, 11 variants have been identified with minimum to maximum severity of infection and surges in cases. Bacterial co-infection/secondary infection is identified during viral respiratory infection, which is a vital reason for morbidity and mortality. The occurrence of secondary infections is an additional burden to the healthcare system; therefore, the quick diagnosis of both COVID-19 and secondary infections will reduce work pressure on healthcare workers. Therefore, well-established support from Artificial Intelligence (AI) could reduce the stress in healthcare and even help in creating novel products to defend against the coronavirus. AI is one of the rapidly growing fields with numerous applications for the healthcare sector. The present review aims to access the recent literature on the role of AI and how its subfamily machine learning (ML) and deep learning (DL) are used to curb the pandemic’s effects. We discuss the role of AI in COVID-19 infections, the detection of secondary infections, technology-assisted protection from COVID-19, global laws and regulations on AI, and the impact of the pandemic on public life.
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Affiliation(s)
- V. Mahalakshmi
- Department of Computer Science, College of Computer Science & Information Technology, Jazan University, Jazan 45142, Saudi Arabia
- Correspondence: or
| | - Awatef Balobaid
- Department of Computer Science, College of Computer Science & Information Technology, Jazan University, Jazan 45142, Saudi Arabia
| | - B. Kanisha
- Department of Computer Science and Engineering, School of Computing, College of Engineering and Technology, SRM Institute of Science and Technology, Chengalpattu 603203, India
| | - R. Sasirekha
- Department of Computing Technologies, SRM Institute of Science and Technology, Kattankulathur Campus, Chengalpattu 603203, India
| | - M. Ramkumar Raja
- Department of Electrical Engineering, College of Engineering, King Khalid University, Abha 62529, Saudi Arabia
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Data analytics and knowledge management approach for COVID-19 prediction and control. INTERNATIONAL JOURNAL OF INFORMATION TECHNOLOGY : AN OFFICIAL JOURNAL OF BHARATI VIDYAPEETH'S INSTITUTE OF COMPUTER APPLICATIONS AND MANAGEMENT 2023; 15:937-954. [PMID: 35729979 PMCID: PMC9188422 DOI: 10.1007/s41870-022-00967-0] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/03/2021] [Accepted: 04/23/2022] [Indexed: 12/12/2022]
Abstract
The Coronavirus Disease (COVID-19) caused by SARS-CoV-2, continues to be a global threat. The major global concern among scientists and researchers is to develop innovative digital solutions for prediction and control of infection and to discover drugs for its cure. In this paper we developed a strategic technical solution for surveillance and control of COVID-19 in Delhi-National Capital Region (NCR). This work aims to elucidate the Delhi COVID-19 Data Management Framework, the backend mechanism of integrated Command and Control Center (iCCC) with plugged-in modules for various administrative, medical and field operations. Based on the time-series data extracted from iCCC repository, the forecasting of COVID-19 spread has been carried out for Delhi using the Auto-Regressive Integrated Moving Average (ARIMA) model as it can effectively predict the logistics requirements, active cases, positive patients, and death rate. The intelligence generated through this research has paved the way for the Government of National Capital Territory Delhi to strategize COVID-19 related policies formulation and implementation on real time basis. The outcome of this innovative work has led to the drastic reduction in COVID-19 positive cases and deaths in Delhi-NCR.
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Multi-class autoencoder-ensembled prediction model for detection of COVID-19 severity. EVOLUTIONARY INTELLIGENCE 2022. [DOI: 10.1007/s12065-022-00744-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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Heidari A, Jafari Navimipour N, Unal M, Toumaj S. Machine learning applications for COVID-19 outbreak management. Neural Comput Appl 2022; 34:15313-15348. [PMID: 35702664 PMCID: PMC9186489 DOI: 10.1007/s00521-022-07424-w] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2021] [Accepted: 05/10/2022] [Indexed: 12/29/2022]
Abstract
Recently, the COVID-19 epidemic has resulted in millions of deaths and has impacted practically every area of human life. Several machine learning (ML) approaches are employed in the medical field in many applications, including detecting and monitoring patients, notably in COVID-19 management. Different medical imaging systems, such as computed tomography (CT) and X-ray, offer ML an excellent platform for combating the pandemic. Because of this need, a significant quantity of study has been carried out; thus, in this work, we employed a systematic literature review (SLR) to cover all aspects of outcomes from related papers. Imaging methods, survival analysis, forecasting, economic and geographical issues, monitoring methods, medication development, and hybrid apps are the seven key uses of applications employed in the COVID-19 pandemic. Conventional neural networks (CNNs), long short-term memory networks (LSTM), recurrent neural networks (RNNs), generative adversarial networks (GANs), autoencoders, random forest, and other ML techniques are frequently used in such scenarios. Next, cutting-edge applications related to ML techniques for pandemic medical issues are discussed. Various problems and challenges linked with ML applications for this pandemic were reviewed. It is expected that additional research will be conducted in the upcoming to limit the spread and catastrophe management. According to the data, most papers are evaluated mainly on characteristics such as flexibility and accuracy, while other factors such as safety are overlooked. Also, Keras was the most often used library in the research studied, accounting for 24.4 percent of the time. Furthermore, medical imaging systems are employed for diagnostic reasons in 20.4 percent of applications.
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Affiliation(s)
- Arash Heidari
- Department of Computer Engineering, Tabriz Branch, Islamic Azad University, Tabriz, Iran
- Department of Computer Engineering, Shabestar Branch, Islamic Azad University, Shabestar, Iran
| | | | - Mehmet Unal
- Department of Computer Engineering, Nisantasi University, Istanbul, Turkey
| | - Shiva Toumaj
- Urmia University of Medical Sciences, Urmia, Iran
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Zhang Y, Zhang J, Tao M, Shu J, Zhu D. Forecasting patient arrivals at emergency department using calendar and meteorological information. APPL INTELL 2022; 52:11232-11243. [PMID: 35079202 PMCID: PMC8776398 DOI: 10.1007/s10489-021-03085-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/05/2021] [Indexed: 11/30/2022]
Abstract
Overcrowding in emergency departments (EDs) is a serious problem in many countries. Accurate ED patient arrival forecasts can serve as a management baseline to better allocate ED personnel and medical resources. We combined calendar and meteorological information and used ten modern machine learning methods to forecast patient arrivals. For daily patient arrival forecasting, two feature selection methods are proposed. One uses kernel principal component analysis(KPCA) to reduce the dimensionality of all of the features, and the other is to use the maximal information coefficient(MIC) method to select the features related to the daily data first and then perform KPCA dimensionality reduction. The current study focuses on a public hospital ED in Hefei, China. We used the data November 1, 2019 to August 31, 2020 for model training; and patient arrival data September 1, 2020 to November 31, 2020 for model validation. The results show that for hourly patient arrival forecasting, each machine learning model has better forecasting results than the traditional autoRegressive integrated moving average (ARIMA) model, especially long short-term memory (LSTM) model. For daily patient arrival forecasting, the feature selection method based on MIC-KPCA has a better forecasting effect, and the simpler models are better than the ensemble models. The method we proposed could be used for better planning of ED personnel resources.
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Affiliation(s)
- Yan Zhang
- Information Center of the First Affiliated Hospital of Anhui Medical University, Hefei, 230022 China
| | - Jie Zhang
- Information Center of the First Affiliated Hospital of Anhui Medical University, Hefei, 230022 China
| | - Min Tao
- Information Center of the First Affiliated Hospital of Anhui Medical University, Hefei, 230022 China
| | - Jian Shu
- School of Software, Nanchang Hangkong University, Nanchang, 330063 China
| | - Degang Zhu
- Information Center of the First Affiliated Hospital of Anhui Medical University, Hefei, 230022 China
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Biswas S. Forecasting and comparative analysis of Covid-19 cases in India and US. THE EUROPEAN PHYSICAL JOURNAL. SPECIAL TOPICS 2022; 231:3537-3544. [PMID: 35340736 PMCID: PMC8933206 DOI: 10.1140/epjs/s11734-022-00536-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/24/2021] [Accepted: 03/05/2022] [Indexed: 05/03/2023]
Abstract
The devastating waves of covid-19 have wreaked havoc on the world, particularly India and US. The article aims to predict the real-time forecasts of covid-19 confirm cases for India and US. To serve the purpose, ARIMA and NNAR based models have been used to the daily new covid-19 confirm cases. The proposed hybrid models are: (i) ARIMA-NNAR model, (ii) NNAR-ARIMA model, (iii) ARIMA-Wavelet ARIMA model, (iv) ARIMA-Wavelet ANN model, (v) NNAR-Wavelet ANN model, and (vi) NNAR-Wavelet ARIMA model. The models are performed to predict the next 45 days of daily new cases. These forecasts can help Govt. to predict the behavior of covid -19 and aware people about the upcoming third wave of covid-19. Our results suggest that hybrid models perform better than single models. We have also proved that our wavelet-based hybrid models can outdated the performance of previously defined hybrid models in terms of accuracy assessments (MAE and RMSE). We have also estimated the time-dependent reproduction number for India and US to observe the present situation.
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Affiliation(s)
- Santanu Biswas
- Department of Mathematics, Jadavpur University, Raja Subodh Chandra Mallick Road, Kolkata, 700032 India
- Department of Mathematics, Adamas University, Barasat-Barrackpore Road, Jagannathpur, Kolkata, West Bengal 700126 India
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