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Priyadarshini I, Chatterjee JM, Sujatha R, Jhanjhi N, Karime A, Masud M. Exploring Internet Meme Activity during COVID-19 Lockdown Using Artificial Intelligence Techniques. APPLIED ARTIFICIAL INTELLIGENCE 2021. [DOI: 10.1080/08839514.2021.2014218] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Affiliation(s)
- Ishaani Priyadarshini
- Department of Electrical and Computer Engineering, University of Delaware, Newark, Delaware, USA
| | | | - R. Sujatha
- School of Information Technology Engineering, Site, Vellore Institute of Technology, Vellore, India
| | - Nz Jhanjhi
- School of Computer Science and Engineering, Sce, Taylor’s University, Malaysia
| | - Ali Karime
- Department of Electrical & Computer Engineering, Royal Military College of Canada, Kingston, ON, Canada
| | - Mehedi Masud
- Department of Computer Science, College of Computers and Information Technology, Taif University, Taif, Saudi Arabia
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Kaur J, Kaur J, Dhama AS, Kumar V, Singh H. Management of COVID-19 Pandemic Data in India: Challenges Faced and Lessons Learnt. Front Big Data 2021; 4:790158. [PMID: 34917935 PMCID: PMC8670559 DOI: 10.3389/fdata.2021.790158] [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: 10/06/2021] [Accepted: 11/03/2021] [Indexed: 11/20/2022] Open
Abstract
COVID-19 is an ongoing pandemic, which has already claimed millions of lives worldwide. In the absence of prior information on the pandemic, the governments can use generated testing data to drive policy decisions. Thus, a one-stop repository is essential to ensure sharing of clean, de-duplicated, and updated records to all the stakeholders. In India, the government initiated the testing through a network of VRDLs headed by the Indian Council of Medical Research (ICMR). Initially, the generated data were captured and shared in Excel sheets. As the number of cases increased, there was a need for a data management system to ensure reliable and up-to-date data to drive policy decisions. Thus, the data management team at ICMR initiated the development of a national COVID-19 testing data management tool that is currently maintaining all the data in a central hub. The first version of the tool was released in March 2020 and was subsequently modified with the changing testing guidelines and strategies. On completing one and a half years of managing the data and collecting approximately 550 million records, the team analyzed the challenges faced and the strategies used to ensure a seamless flow of data to the system and its real-time analysis. In this study, the entire duration of the pandemic has been divided into four different phases based on the resourcefulness of the country. Since the pandemic is currently ongoing, this study can be useful for countries in a different phase of pandemic facing similar challenges.
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Affiliation(s)
- Jasmine Kaur
- Division of Biomedical Informatics, Indian Council of Medical Research, New Delhi, India
| | - Jasleen Kaur
- Division of Biomedical Informatics, Indian Council of Medical Research, New Delhi, India
| | - Ajay Singh Dhama
- Division of Biomedical Informatics, Indian Council of Medical Research, New Delhi, India
| | - Vinit Kumar
- Division of Biomedical Informatics, Indian Council of Medical Research, New Delhi, India
| | - Harpreet Singh
- Division of Biomedical Informatics, Indian Council of Medical Research, New Delhi, India
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53
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Accuracy versus reliability-based modelling approaches for medical decision making. Comput Biol Med 2021; 141:105138. [PMID: 34929467 DOI: 10.1016/j.compbiomed.2021.105138] [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: 11/02/2021] [Revised: 12/11/2021] [Accepted: 12/11/2021] [Indexed: 11/21/2022]
Abstract
Forecasting in the medical domain is critical to the quality of decisions made by physicians, patients, and health planners. Modeling is one of the most important components of decision support systems, which are frequently used to simulate and analyze under-studied systems in order to make more appropriate decisions in medical science. In the medical modeling literature, various approaches with varying structures and characteristics have been proposed to cover a wide range of application categories and domains. Regardless of the differences between modeling approaches, all of them aim to maximize the accuracy or reliability of the results in order to achieve the most generalizable model and, as a result, a higher level of profitability decisions. Despite the theoretical significance and practical impact of reliability on generalizability, particularly in high-risk decisions and applications, a significant number of models in the fields of medical forecasting, classification, and time series prediction have been developed to maximize accuracy in mind. In other words, given the volatility of medical variables, it is also necessary to have stable and reliable forecasts in order to make sound decisions. The quality of medical decisions resulting from accuracy and reliability-based intelligent and statistical modeling approaches is compared and evaluated in this paper in order to determine the relative importance of accuracy and reliability on the quality of made decisions in decision support systems. For this purpose, 33 different case studies from the UCI in three categories of supervised modeling, namely causal forecasting, time series prediction, and classification, were considered. These cases were chosen from various domains, such as disease diagnosis (obesity, Parkinson's disease, diabetes, hepatitis, stenosis of arteries, orthopedic disease, autism) and cancer (lung, breast, cervical), experiments, therapy (immunotherapy, cryotherapy), fertility prediction, and predicting the number of patients in the emergency room and ICU. According to empirical findings, the reliability-based strategy outperformed the accuracy-based strategy in causal forecasting cases by 2.26%, classification cases by 13.49%, and time series prediction cases by 3.08%. Furthermore, compared to similar accuracy-based models, the reliability-based models can generate a 6.28% improvement. As a result, they can be considered an appropriate alternative to traditional accuracy-based models for medical decision support systems modeling purposes.
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54
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The role of data-driven artificial intelligence on COVID-19 disease management in public sphere: a review. DECISION 2021. [PMCID: PMC8482354 DOI: 10.1007/s40622-021-00289-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Coronavirus disease 2019 (COVID-19) is an infectious disease with acute intense respiratory syndrome which spread around the world for the very first time impacting the way of life with drastic uncertainty. It rapidly reached almost every nook and corner of the world and the World Health Organization (WHO) has announced COVID-19 as a pandemic. The health care institutions around the globe are looking for viable and real-time technological solutions to handle the virus for evading its spread and circumvent probable demises. Importantly, the artificial intelligence tools and techniques are playing a major role in fighting the effect of virus on the economic jolt by mimicking human intelligence by screening, analyzing, predicting and tracking the existing and likely future patients. Since the first reported case, all the government organizations in the world jumped into action to prevent it and many studies reported the role of AI in taking decisions analyzing big data available in public sphere. Thereby, this review focuses on identifying the significant implication of AI techniques used for the COVID-19 disease management in the public sphere by agglomerating the latest available information. It also discusses the pitfalls and future directions in handling sensitive big data required for advanced neural networks.
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Saif S, Das P, Biswas S. A Hybrid Model based on mBA-ANFIS for COVID-19 Confirmed Cases Prediction and Forecast. JOURNAL OF THE INSTITUTION OF ENGINEERS (INDIA): SERIES B 2021. [PMCID: PMC7814866 DOI: 10.1007/s40031-021-00538-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Affiliation(s)
- Sohail Saif
- Department of Computer Science & Engineering, Maulana Abul Kalam Azad University of Technology, Kolkata, WB India
| | - Priya Das
- Department of Computer Science & Engineering, Maulana Abul Kalam Azad University of Technology, Kolkata, WB India
| | - Suparna Biswas
- Department of Computer Science & Engineering, Maulana Abul Kalam Azad University of Technology, Kolkata, WB India
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56
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dos Reis EVM, Savi MA. A dynamical map to describe COVID-19 epidemics. THE EUROPEAN PHYSICAL JOURNAL. SPECIAL TOPICS 2021; 231:893-904. [PMID: 34849187 PMCID: PMC8614223 DOI: 10.1140/epjs/s11734-021-00340-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/08/2021] [Accepted: 10/30/2021] [Indexed: 05/09/2023]
Abstract
Nonlinear dynamics perspective is an interesting approach to describe COVID-19 epidemics, providing information to support strategic decisions. This paper proposes a dynamical map to describe COVID-19 epidemics based on the classical susceptible-exposed-infected-recovered (SEIR) differential model, incorporating vaccinated population. On this basis, the novel map represents COVID-19 discrete-time dynamics by adopting three populations: infected, cumulative infected and vaccinated. The map promotes a dynamical description based on algebraic equations with a reduced number of variables and, due to its simplicity, it is easier to perform parameter adjustments. In addition, the map description allows analytical calculations of useful information to evaluate the epidemic scenario, being important to support strategic decisions. In this regard, it should be pointed out the estimation of the number deaths, infection rate and the herd immunization point. Numerical simulations show the model capability to describe COVID-19 dynamics, capturing the main features of the epidemic evolution. Reported data from Germany, Italy and Brazil are of concern showing the map ability to describe different scenario patterns that include multi-wave pattern with bell shape and plateaus characteristics. The effect of vaccination is analyzed considering different campaign strategies, showing its importance to control the epidemics.
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Affiliation(s)
- Eduardo V. M. dos Reis
- Department of Mechanical Engineering, Center for Nonlinear Mechanics, Universidade Federal do Rio de Janeiro, COPPE, P.O. Box 68 503, Rio de Janeiro, RJ Brazil
| | - Marcelo A. Savi
- Department of Mechanical Engineering, Center for Nonlinear Mechanics, Universidade Federal do Rio de Janeiro, COPPE, P.O. Box 68 503, Rio de Janeiro, RJ Brazil
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57
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Abirami RS, Kumar GS. Comparative Study Based on Analysis of Coronavirus Disease (COVID-19) Detection and Prediction Using Machine Learning Models. SN COMPUTER SCIENCE 2021; 3:79. [PMID: 34841267 PMCID: PMC8605773 DOI: 10.1007/s42979-021-00965-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/25/2021] [Accepted: 11/07/2021] [Indexed: 11/13/2022]
Abstract
As the number of COVID-19 cases increases day by day, the situation and livelihood of people throughout the world deteriorates. The goal of this study is to use machine learning models to identify disease and forecast whether or not a person is infected with the virus or another common illness. More articles about COVID-19 will be released starting in 2020, but we still do not have a reliable prediction mechanism to diagnose the disease with 100% accuracy. This comparison is done to see which model is the most effective in detecting and predicting disease. Despite the fact that we have immunizations, we require a best-prediction strategy to assist all humans in surviving. Researchers claimed that the supervised learning method predicts more accurately than the unsupervised learning method in the majority of studies. Supervised learning is the process of mapping inputs to derived outputs using a set of variables and created functions. This will also help us to optimize performance criteria using experience. It is further divided into two categories: classification and regression. According to recent studies, classification models are more accurate than other models.
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Affiliation(s)
- R. Sudha Abirami
- Department of Computer Science, School of Engineering and Technology, Pondicherry University, Puducherry, India
| | - G. Suresh Kumar
- Department of Computer Science, School of Engineering and Technology, Pondicherry University, Puducherry, India
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58
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Using Daily Nighttime Lights to Monitor Spatiotemporal Patterns of Human Lifestyle under COVID-19: The Case of Saudi Arabia. REMOTE SENSING 2021. [DOI: 10.3390/rs13224633] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
A novel coronavirus, COVID-19, appeared at the beginning of 2020 and within a few months spread worldwide. The COVID-19 pandemic had some of its greatest impacts on social, economic and religious activities. This study focused on the application of daily nighttime light (NTL) data (VNP46A2) to measure the spatiotemporal impact of the COVID-19 pandemic on the human lifestyle in Saudi Arabia at the national, province and governorate levels as well as on selected cities and sites. The results show that NTL brightness was reduced in all the pandemic periods in 2020 compared with a pre-pandemic period in 2019, and this was consistent with the socioeconomic results. An early pandemic period showed the greatest effects on the human lifestyle due to the closure of mosques and the implementation of a curfew. A slight improvement in the NTL intensity was observed in later pandemic periods, which represented Ramadan and Eid Alfiter days when Muslims usually increase the light of their houses. Closures of the two holy mosques in Makkah and Madinah affected the human lifestyle in these holy cities as well as that of Umrah pilgrims inside Saudi Arabia and abroad. The findings of this study confirm that the social and cultural context of each country must be taken into account when interpreting COVID-19 impacts, and that analysis of difference in nighttime lights is sensitive to these factors. In Saudi Arabia, the origin of Islam and one of the main sources of global energy, the preventive measures taken not only affected Saudi society; impacts spread further and reached the entire Islamic society and other societies, too.
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59
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Forecasting COVID-19 infections in the Arabian Gulf region. MODELING EARTH SYSTEMS AND ENVIRONMENT 2021; 8:3813-3822. [PMID: 34778510 PMCID: PMC8571680 DOI: 10.1007/s40808-021-01332-z] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/28/2021] [Accepted: 10/22/2021] [Indexed: 11/19/2022]
Abstract
In this paper, an empirical analysis of linear state space models and long short-term memory neural networks is performed to compare the statistical performance of these models in predicting the spread of COVID-19 infections. Data on the pandemic daily infections from the Arabian Gulf countries from 2020/03/24 to 2021/05/20 are fitted to each model and a statistical analysis is conducted to assess their short-term prediction accuracy. The results show that state space model predictions are more accurate with notably smaller root mean square errors than the deep learning forecasting method. The results also indicate that the poorer forecast performance of long short-term memory neural networks occurs in particular when health surveillance data are characterized by high fluctuations of the daily infection records and frequent occurrences of abrupt changes. One important result of this study is the possible relationship between data complexity and forecast accuracy with different models as suggested in the entropy analysis. It is concluded that state space models perform better than long short-term memory networks with highly irregular and more complex surveillance data.
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60
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Haghighat F. Predicting the trend of indicators related to Covid-19 using the combined MLP-MC model. CHAOS, SOLITONS, AND FRACTALS 2021; 152:111399. [PMID: 34511743 PMCID: PMC8416568 DOI: 10.1016/j.chaos.2021.111399] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/27/2021] [Revised: 08/23/2021] [Accepted: 08/24/2021] [Indexed: 06/13/2023]
Abstract
Although more than a year has passed since the coronavirus outbreak globally, the Covid-19 pandemic conditions still exist in many countries, including Iran. Predicting the number of future patients and deaths can help governments and policymakers make better decisions to enforce disease control restrictions. In this study, we aim to use a combined multilayer perceptron (MLP) neural network and Markov chain (MC) model to predict two indicators of the number of discharged and death cases according to their relationship with the number of hospitalized cases in Bushehr province, Iran. This hybrid model is called MLP-MC. In this study, 136 data (days) are collected from May 13, 2020, to April 1, 2021, divided into two parts: training and test. The training data are used to train the MLP network, and the trained MLP network is used to predict the test data and the next 40 days. Then the residual errors of actual and predicted values are calculated. In the next step, the MC model is used to classify the errors and predict the values of the indicators according to the probabilities related to the error states and improve the performance of the MLP model in forecasting. Finally, the prediction accuracy of MLP and MLP-MC models are compared using three evaluation metrics: MAD, MSE and RMSE. This comparison showed that the MLP-MC model has slightly higher prediction accuracy than the MLP model.
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Affiliation(s)
- Fatemeh Haghighat
- Department of Industrial Management, Faculty of Management, University of Tehran, Tehran, Iran
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61
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Abstract
The COVID-19 pandemic spread rapidly around the world and is currently one of the most leading causes of death and heath disaster in the world. Turkey, like most of the countries, has been negatively affected by COVID-19. The aim of this study is to design a predictive model based on artificial neural network (ANN) model to predict the future number of daily cases and deaths caused by COVID-19 in a generalized way to fit different countries’ spreads. In this study, we used a dataset between 11 March 2020 and 23 January 2021 for different countries. This study provides an ANN model to assist the government to take preventive action for hospitals and medical facilities. The results show that there is an 86% overall accuracy in predicting the mortality rate and 87% in predicting the number of cases.
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62
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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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63
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Aberi P, Arabzadeh R, Insam H, Markt R, Mayr M, Kreuzinger N, Rauch W. Quest for Optimal Regression Models in SARS-CoV-2 Wastewater Based Epidemiology. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:10778. [PMID: 34682523 PMCID: PMC8535556 DOI: 10.3390/ijerph182010778] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/11/2021] [Revised: 10/05/2021] [Accepted: 10/09/2021] [Indexed: 12/18/2022]
Abstract
Wastewater-based epidemiology is a recognised source of information for pandemic management. In this study, we investigated the correlation between a SARS-CoV-2 signal derived from wastewater sampling and COVID-19 incidence values monitored by means of individual testing programs. The dataset used in the study is composed of timelines (duration approx. five months) of both signals at four wastewater treatment plants across Austria, two of which drain large communities and the other two drain smaller communities. Eight regression models were investigated to predict the viral incidence under varying data inputs and pre-processing methods. It was found that population-based normalisation and smoothing as a pre-processing of the viral load data significantly influence the fitness of the regression models. Moreover, the time latency lag between the wastewater data and the incidence derived from the testing program was found to vary between 2 and 7 days depending on the time period and site. It was found to be necessary to take such a time lag into account by means of multivariate modelling to boost the performance of the regression. Comparing the models, no outstanding one could be identified as all investigated models are revealing a sufficient correlation for the task. The pre-processing of data and a multivariate model formulation is more important than the model structure.
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Affiliation(s)
- Parisa Aberi
- Department of Infrastructure, University Innsbruck, 6020 Innsbruck, Austria; (P.A.); (R.A.)
| | - Rezgar Arabzadeh
- Department of Infrastructure, University Innsbruck, 6020 Innsbruck, Austria; (P.A.); (R.A.)
| | - Heribert Insam
- Department of Microbiology, University Innsbruck, 6020 Innsbruck, Austria; (H.I.); (R.M.); (M.M.)
| | - Rudolf Markt
- Department of Microbiology, University Innsbruck, 6020 Innsbruck, Austria; (H.I.); (R.M.); (M.M.)
| | - Markus Mayr
- Department of Microbiology, University Innsbruck, 6020 Innsbruck, Austria; (H.I.); (R.M.); (M.M.)
| | - Norbert Kreuzinger
- Institute for Water Quality and Resource Management, Technology University Vienna, 1040 Vienna, Austria;
| | - Wolfgang Rauch
- Department of Infrastructure, University Innsbruck, 6020 Innsbruck, Austria; (P.A.); (R.A.)
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64
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A Self-Activated CNN Approach for Multi-Class Chest-Related COVID-19 Detection. APPLIED SCIENCES-BASEL 2021. [DOI: 10.3390/app11199023] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Chest diseases can be dangerous and deadly. They include many chest infections such as pneumonia, asthma, edema, and, lately, COVID-19. COVID-19 has many similar symptoms compared to pneumonia, such as breathing hardness and chest burden. However, it is a challenging task to differentiate COVID-19 from other chest diseases. Several related studies proposed a computer-aided COVID-19 detection system for the single-class COVID-19 detection, which may be misleading due to similar symptoms of other chest diseases. This paper proposes a framework for the detection of 15 types of chest diseases, including the COVID-19 disease, via a chest X-ray modality. Two-way classification is performed in proposed Framework. First, a deep learning-based convolutional neural network (CNN) architecture with a soft-max classifier is proposed. Second, transfer learning is applied using fully-connected layer of proposed CNN that extracted deep features. The deep features are fed to the classical Machine Learning (ML) classification methods. However, the proposed framework improves the accuracy for COVID-19 detection and increases the predictability rates for other chest diseases. The experimental results show that the proposed framework, when compared to other state-of-the-art models for diagnosing COVID-19 and other chest diseases, is more robust, and the results are promising.
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65
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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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66
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Shankar K, Perumal E, Díaz VG, Tiwari P, Gupta D, Saudagar AKJ, Muhammad K. An optimal cascaded recurrent neural network for intelligent COVID-19 detection using Chest X-ray images. Appl Soft Comput 2021; 113:107878. [PMID: 34512217 PMCID: PMC8423750 DOI: 10.1016/j.asoc.2021.107878] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2021] [Revised: 08/20/2021] [Accepted: 09/02/2021] [Indexed: 12/18/2022]
Abstract
In recent times, COVID-19, has a great impact on the healthcare sector and results in a wide range of respiratory illnesses. It is a type of Ribonucleic acid (RNA) virus, which affects humans as well as animals. Though several artificial intelligence-based COVID-19 diagnosis models have been presented in the literature, most of the works have not focused on the hyperparameter tuning process. Therefore, this paper proposes an intelligent COVID-19 diagnosis model using a barnacle mating optimization (BMO) algorithm with a cascaded recurrent neural network (CRNN) model, named BMO-CRNN. The proposed BMO-CRNN model aims to detect and classify the existence of COVID-19 from Chest X-ray images. Initially, pre-processing is applied to enhance the quality of the image. Next, the CRNN model is used for feature extraction, followed by hyperparameter tuning of CRNN via the BMO algorithm to improve the classification performance. The BMO algorithm determines the optimal values of the CRNN hyperparameters namely learning rate, batch size, activation function, and epoch count. The application of CRNN and hyperparameter tuning using the BMO algorithm shows the novelty of this work. A comprehensive simulation analysis is carried out to ensure the better performance of the BMO-CRNN model, and the experimental outcome is investigated using several performance metrics. The simulation results portrayed that the BMO-CRNN model has showcased optimal performance with an average sensitivity of 97.01%, specificity of 98.15%, accuracy of 97.31%, and F-measure of 97.73% compared to state-of-the-art methods.
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Affiliation(s)
- K Shankar
- Federal University of Piauí, Teresina 64049-550, Brazil
| | - Eswaran Perumal
- Department of Computer Applications, Alagappa University, Karaikudi, India
| | - Vicente García Díaz
- Department of Computer Science, School of Computer Science Engineering, University of Oviedo, Spain
| | - Prayag Tiwari
- Department of Computer Science, Aalto University, Finland
| | - Deepak Gupta
- Maharaja Agrasen Institute of Technology, New Delhi, India
| | - Abdul Khader Jilani Saudagar
- Information Systems Department, College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11432, Saudi Arabia
| | - Khan Muhammad
- Visual Analytics for Knowledge Laboratory (VIS2KNOW Lab), Department of Software, Sejong University, Seoul 143-747, Republic of Korea
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Kaliappan J, Srinivasan K, Mian Qaisar S, Sundararajan K, Chang CY, C S. Performance Evaluation of Regression Models for the Prediction of the COVID-19 Reproduction Rate. Front Public Health 2021; 9:729795. [PMID: 34595149 PMCID: PMC8476853 DOI: 10.3389/fpubh.2021.729795] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2021] [Accepted: 08/16/2021] [Indexed: 01/28/2023] Open
Abstract
This paper aims to evaluate the performance of multiple non-linear regression techniques, such as support-vector regression (SVR), k-nearest neighbor (KNN), Random Forest Regressor, Gradient Boosting, and XGBOOST for COVID-19 reproduction rate prediction and to study the impact of feature selection algorithms and hyperparameter tuning on prediction. Sixteen features (for example, Total_cases_per_million and Total_deaths_per_million) related to significant factors, such as testing, death, positivity rate, active cases, stringency index, and population density are considered for the COVID-19 reproduction rate prediction. These 16 features are ranked using Random Forest, Gradient Boosting, and XGBOOST feature selection algorithms. Seven features are selected from the 16 features according to the ranks assigned by most of the above mentioned feature-selection algorithms. Predictions by historical statistical models are based solely on the predicted feature and the assumption that future instances resemble past occurrences. However, techniques, such as Random Forest, XGBOOST, Gradient Boosting, KNN, and SVR considered the influence of other significant features for predicting the result. The performance of reproduction rate prediction is measured by mean absolute error (MAE), mean squared error (MSE), root mean squared error (RMSE), R-Squared, relative absolute error (RAE), and root relative squared error (RRSE) metrics. The performances of algorithms with and without feature selection are similar, but a remarkable difference is seen with hyperparameter tuning. The results suggest that the reproduction rate is highly dependent on many features, and the prediction should not be based solely upon past values. In the case without hyperparameter tuning, the minimum value of RAE is 0.117315935 with feature selection and 0.0968989 without feature selection, respectively. The KNN attains a low MAE value of 0.0008 and performs well without feature selection and with hyperparameter tuning. The results show that predictions performed using all features and hyperparameter tuning is more accurate than predictions performed using selected features.
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Affiliation(s)
- Jayakumar Kaliappan
- School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, India
| | - Kathiravan Srinivasan
- School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, India
| | - Saeed Mian Qaisar
- Electrical and Computer Engineering Department, Effat University, Jeddah, Saudi Arabia
| | - Karpagam Sundararajan
- School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, India
| | - Chuan-Yu Chang
- Department of Computer Science and Information Engineering, National Yunlin University of Science and Technology, Douliu, Taiwan
| | - Suganthan C
- School of Social Sciences and Languages, Vellore Institute of Technology, Vellore, India
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68
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Gill HK, Sehgal VK, Verma AK. CASE-CF: Context Aware Smart Epidemic Control Framework. NEW GENERATION COMPUTING 2021; 39:541-568. [PMID: 34511695 PMCID: PMC8418289 DOI: 10.1007/s00354-021-00135-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/01/2021] [Accepted: 08/26/2021] [Indexed: 05/21/2023]
Abstract
Novel Coronavirus (COVID-19) has become one of the deadliest pandemics that has affected almost all the nations in the world. Lockdown and systematic re-opening of shopping malls, offices, etc. is still one of the major weapons against this virus. However, the government and medical agencies take long time to reopen the places due to risks involved in this deadly virus. The delay to reopen places has resulted in sharp decline in the growth of economy. In this paper a current context aware framework is proposed which uses multiple inputs for a specific region to decide whether to open it or not. The proposed framework used series of deep neural network models to generate recommendations specific to a particular region. Most of the inputs are real-time and readily available with the government. The main aim is to develop framework which can be used in any kind of pandemic even in small region to easily contain it. However, it has been tested using opensource data available for COVID-19. Data was crawled from web for 22 districts of Haryana state of India. Experimental result proved the efficiency of proposed framework.
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Affiliation(s)
- Harsuminder Kaur Gill
- Department of Computer Science and Engineering & Information Technology, Jaypee University of Information Technology, Solan, Himachal Pradesh India
| | - Vivek Kumar Sehgal
- Department of Computer Science and Engineering & Information Technology, Jaypee University of Information Technology, Solan, Himachal Pradesh India
| | - Anil Kumar Verma
- Department of Computer Science and Engineering, Thapar Institute of Engineering and Technology, Patiala, Punjab India
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69
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Shakeel SM, Kumar NS, Madalli PP, Srinivasaiah R, Swamy DR. COVID-19 prediction models: a systematic literature review. Osong Public Health Res Perspect 2021; 12:215-229. [PMID: 34465071 PMCID: PMC8408413 DOI: 10.24171/j.phrp.2021.0100] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2021] [Accepted: 07/12/2021] [Indexed: 12/24/2022] Open
Abstract
As the world grapples with the problem of the coronavirus disease 2019 (COVID-19) pandemic and its devastating effects, scientific groups are working towards solutions to mitigate the effects of the virus. This paper aimed to collate information on COVID-19 prediction models. A systematic literature review is reported, based on a manual search of 1,196 papers published from January to December 2020. Various databases such as Google Scholar, Web of Science, and Scopus were searched. The search strategy was formulated and refined in terms of subject keywords, geographical purview, and time period according to a predefined protocol. Visualizations were created to present the data trends according to different parameters. The results of this systematic literature review show that the study findings are critically relevant for both healthcare managers and prediction model developers. Healthcare managers can choose the best prediction model output for their organization or process management. Meanwhile, prediction model developers and managers can identify the lacunae in their models and improve their data-driven approaches.
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Affiliation(s)
- Sheikh Muzaffar Shakeel
- Department of Industrial Engineering and Management, JSS Academy of Technical Education, Bengaluru, India
| | - Nithya Sathya Kumar
- Department of Industrial Engineering and Management, JSS Academy of Technical Education, Bengaluru, India
| | - Pranita Pandurang Madalli
- Department of Industrial Engineering and Management, JSS Academy of Technical Education, Bengaluru, India
| | - Rashmi Srinivasaiah
- Department of Industrial Engineering and Management, JSS Academy of Technical Education, Bengaluru, India
| | - Devappa Renuka Swamy
- Department of Industrial Engineering and Management, JSS Academy of Technical Education, Bengaluru, India
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70
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COVID-19 Mortality Prediction Using Machine Learning-Integrated Random Forest Algorithm under Varying Patient Frailty. MATHEMATICS 2021. [DOI: 10.3390/math9172043] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
The abundance of type and quantity of available data in the healthcare field has led many to utilize machine learning approaches to keep up with this influx of data. Data pertaining to COVID-19 is an area of recent interest. The widespread influence of the virus across the United States creates an obvious need to identify groups of individuals that are at an increased risk of mortality from the virus. We propose a so-called clustered random forest approach to predict COVID-19 patient mortality. We use this approach to examine the hidden heterogeneity of patient frailty by examining demographic information for COVID-19 patients. We find that our clustered random forest approach attains predictive performance comparable to other published methods. We also find that follow-up analysis with neural network modeling and k-means clustering provide insight into the type and magnitude of mortality risks associated with COVID-19.
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71
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Chatterjee S, Chakrabarty D, Mukhopadhyay A. Fuzzy association analysis for identifying climatic and socio-demographic factors impacting the spread of COVID-19. Methods 2021; 203:511-522. [PMID: 34433092 PMCID: PMC8380196 DOI: 10.1016/j.ymeth.2021.08.005] [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: 04/06/2021] [Revised: 07/13/2021] [Accepted: 08/18/2021] [Indexed: 11/30/2022] Open
Abstract
Recently, the whole world witnessed the fatal outbreak of COVID-19 epidemic originating at Wuhan, Hubei province, China, during a mass gathering in a film festival. World Health Organization (WHO) has declared this COVID-19 as a pandemic due to its rapid spread across different countries within a few days. Several research works are being performed to understand the various influential factors responsible for spreading COVID. However, limited studies have been performed on how climatic and socio-demographic conditions may impact the spread of the virus. In this work, we aim to find the relationship of socio-demographic conditions, such as temperature, humidity, and population density of the regions, with the spread of COVID-19. The COVID data for different countries along with the social data are collected. For the experimental purpose, Fuzzy association rule mining is employed to infer the various relationships from the data. Moreover, to examine the seasonal effect, a streaming setting is also considered. The experimental results demonstrate various interesting insights to understand the impact of different factors on spreading COVID-19.
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Affiliation(s)
- Sujoy Chatterjee
- Department of Informatics, School of Computer Science, University of Petroleum and Energy Studies, Dehradun, India
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72
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Arora G, Joshi J, Mandal RS, Shrivastava N, Virmani R, Sethi T. Artificial Intelligence in Surveillance, Diagnosis, Drug Discovery and Vaccine Development against COVID-19. Pathogens 2021; 10:1048. [PMID: 34451513 PMCID: PMC8399076 DOI: 10.3390/pathogens10081048] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2021] [Revised: 08/11/2021] [Accepted: 08/11/2021] [Indexed: 12/15/2022] Open
Abstract
As of August 6th, 2021, the World Health Organization has notified 200.8 million laboratory-confirmed infections and 4.26 million deaths from COVID-19, making it the worst pandemic since the 1918 flu. The main challenges in mitigating COVID-19 are effective vaccination, treatment, and agile containment strategies. In this review, we focus on the potential of Artificial Intelligence (AI) in COVID-19 surveillance, diagnosis, outcome prediction, drug discovery and vaccine development. With the help of big data, AI tries to mimic the cognitive capabilities of a human brain, such as problem-solving and learning abilities. Machine Learning (ML), a subset of AI, holds special promise for solving problems based on experiences gained from the curated data. Advances in AI methods have created an unprecedented opportunity for building agile surveillance systems using the deluge of real-time data generated within a short span of time. During the COVID-19 pandemic, many reports have discussed the utility of AI approaches in prioritization, delivery, surveillance, and supply chain of drugs, vaccines, and non-pharmaceutical interventions. This review will discuss the clinical utility of AI-based models and will also discuss limitations and challenges faced by AI systems, such as model generalizability, explainability, and trust as pillars for real-life deployment in healthcare.
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Affiliation(s)
- Gunjan Arora
- Department of Internal Medicine, Yale University School of Medicine, New Haven, CT 06520, USA
| | - Jayadev Joshi
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44106, USA;
| | - Rahul Shubhra Mandal
- Department of Cancer Biology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA;
| | - Nitisha Shrivastava
- Department of Pathology, Albert Einstein College of Medicine/Montefiore Medical Center, Bronx, NY 10461, USA;
| | - Richa Virmani
- Confo Therapeutics, Technologiepark 94, 9052 Ghent, Belgium;
| | - Tavpritesh Sethi
- Indraprastha Institute of Information Technology, New Delhi 110020, India;
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73
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Arora G, Joshi J, Mandal RS, Shrivastava N, Virmani R, Sethi T. Artificial Intelligence in Surveillance, Diagnosis, Drug Discovery and Vaccine Development against COVID-19. Pathogens 2021; 10:1048. [PMID: 34451513 PMCID: PMC8399076 DOI: 10.3390/pathogens10081048,] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
As of August 6th, 2021, the World Health Organization has notified 200.8 million laboratory-confirmed infections and 4.26 million deaths from COVID-19, making it the worst pandemic since the 1918 flu. The main challenges in mitigating COVID-19 are effective vaccination, treatment, and agile containment strategies. In this review, we focus on the potential of Artificial Intelligence (AI) in COVID-19 surveillance, diagnosis, outcome prediction, drug discovery and vaccine development. With the help of big data, AI tries to mimic the cognitive capabilities of a human brain, such as problem-solving and learning abilities. Machine Learning (ML), a subset of AI, holds special promise for solving problems based on experiences gained from the curated data. Advances in AI methods have created an unprecedented opportunity for building agile surveillance systems using the deluge of real-time data generated within a short span of time. During the COVID-19 pandemic, many reports have discussed the utility of AI approaches in prioritization, delivery, surveillance, and supply chain of drugs, vaccines, and non-pharmaceutical interventions. This review will discuss the clinical utility of AI-based models and will also discuss limitations and challenges faced by AI systems, such as model generalizability, explainability, and trust as pillars for real-life deployment in healthcare.
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Affiliation(s)
- Gunjan Arora
- Department of Internal Medicine, Yale University School of Medicine, New Haven, CT 06520, USA
- Correspondence: or
| | - Jayadev Joshi
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44106, USA;
| | - Rahul Shubhra Mandal
- Department of Cancer Biology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA;
| | - Nitisha Shrivastava
- Department of Pathology, Albert Einstein College of Medicine/Montefiore Medical Center, Bronx, NY 10461, USA;
| | - Richa Virmani
- Confo Therapeutics, Technologiepark 94, 9052 Ghent, Belgium;
| | - Tavpritesh Sethi
- Indraprastha Institute of Information Technology, New Delhi 110020, India;
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74
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Rahman MM, Islam MM, Manik MMH, Islam MR, Al-Rakhami MS. Machine Learning Approaches for Tackling Novel Coronavirus (COVID-19) Pandemic. SN COMPUTER SCIENCE 2021; 2:384. [PMID: 34308367 PMCID: PMC8287848 DOI: 10.1007/s42979-021-00774-7] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/05/2021] [Accepted: 07/11/2021] [Indexed: 12/24/2022]
Abstract
Novel coronavirus (COVID-19) has become a global problem in recent times due to the rapid spread of this disease. Almost all the countries of the world have been affected by this pandemic that made a major consequence on the medical system and healthcare facilities. The healthcare system is going through a critical time because of the COVID-19 pandemic. Modern technologies such as deep learning, machine learning, and data science are contributing to fight COVID-19. The paper aims to highlight the role of machine learning approaches in this pandemic situation. We searched for the latest literature regarding machine learning approaches for COVID-19 from various sources like IEEE Xplore, PubMed, Google Scholar, Research Gate, and Scopus. Then, we analyzed this literature and described them throughout the study. In this study, we noticed four different applications of machine learning methods to combat COVID-19. These applications are trying to contribute in various aspects like helping physicians to make confident decisions, policymakers to take fruitful decisions, and identifying potentially infected people. The major challenges of existing systems with possible future trends are outlined in this paper. The researchers are coming with various technologies using machine learning techniques to face the COVID-19 pandemic. These techniques are serving the healthcare system in a great deal. We recommend that machine learning can be a useful tool for proper analyzing, screening, tracking, forecasting, and predicting the characteristics and trends of COVID-19.
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Affiliation(s)
- Mohammad Marufur Rahman
- Department of Computer Science and Engineering, Khulna University of Engineering and Technology, Khulna, 9203 Bangladesh
| | - Md. Milon Islam
- Department of Computer Science and Engineering, Khulna University of Engineering and Technology, Khulna, 9203 Bangladesh
| | - Md. Motaleb Hossen Manik
- Department of Computer Science and Engineering, Khulna University of Engineering and Technology, Khulna, 9203 Bangladesh
| | - Md. Rabiul Islam
- Department of Electrical and Electronic Engineering, Bangladesh Army University of Engineering and Technology, Natore, 6431 Bangladesh
| | - Mabrook S. Al-Rakhami
- Research Chair of Pervasive and Mobile Computing, Information Systems Department, College of Computer and Information Sciences, King Saud University, Riyadh, 11543 Saudi Arabia
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75
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Shetty RP, Pai PS. Forecasting of COVID 19 Cases in Karnataka State using Artificial Neural Network (ANN). JOURNAL OF THE INSTITUTION OF ENGINEERS (INDIA): SERIES B 2021. [PMCID: PMC8212275 DOI: 10.1007/s40031-021-00623-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
COVID-19 is a pandemic that has caused lot of deaths and infections in the last 2 months and is showing an increasing trend not only in the number of infections and deaths, but also in the recovery rate. Accurate prediction models are very much essential to make proper forecasts and take necessary actions. This study demonstrates the capability of Multilayer Perceptron (MLP), an Artificial Neural network (ANN) model for forecasting the number of infected cases in the state of Karnataka in India. It is trained using a fast training algorithm namely, Extreme Learning machine to reduce the training time required. The parameters required for the forecasting model have been selected using partial autocorrelation function, which is a conventional method, and its performance has been compared with parameters selected using cuckoo search (CS) algorithm, which is a very popular metaheuristic optimization algorithm. The testing of the forecasting model has been done, and comparison between the two parameter selection methods as well as with MLP with conventional backpropagation has been carried out. Use of CS algorithm has resulted in a better forecasting performance based on mean absolute percentage error (MAPE), with a value of 6.62% on training data and 7.03% on the test data. Further to check the efficacy of the model, the data of COVID-19 cases of Hungary from 4 March to 19 April 2020 have been used, which resulted in a MAPE of 1.55%, thereby establishing the robustness of the proposed ANN model for forecasting COVID-19 cases for the state of Karnataka.
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76
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Rasheed J, Jamil A, Hameed AA, Al-Turjman F, Rasheed A. COVID-19 in the Age of Artificial Intelligence: A Comprehensive Review. Interdiscip Sci 2021; 13:153-175. [PMID: 33886097 PMCID: PMC8060789 DOI: 10.1007/s12539-021-00431-w] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2020] [Revised: 04/03/2021] [Accepted: 04/09/2021] [Indexed: 12/23/2022]
Abstract
The recent COVID-19 pandemic, which broke at the end of the year 2019 in Wuhan, China, has infected more than 98.52 million people by today (January 23, 2021) with over 2.11 million deaths across the globe. To combat the growing pandemic on urgent basis, there is need to design effective solutions using new techniques that could exploit recent technology, such as machine learning, deep learning, big data, artificial intelligence, Internet of Things, for identification and tracking of COVID-19 cases in near real time. These technologies have offered inexpensive and rapid solution for proper screening, analyzing, prediction and tracking of COVID-19 positive cases. In this paper, a detailed review of the role of AI as a decisive tool for prognosis, analyze, and tracking the COVID-19 cases is performed. We searched various databases including Google Scholar, IEEE Library, Scopus and Web of Science using a combination of different keywords consisting of COVID-19 and AI. We have identified various applications, where AI can help healthcare practitioners in the process of identification and monitoring of COVID-19 cases. A compact summary of the corona virus cases are first highlighted, followed by the application of AI. Finally, we conclude the paper by highlighting new research directions and discuss the research challenges. Even though scientists and researchers have gathered and exchanged sufficient knowledge over last couple of months, but this structured review also examined technological perspectives while encompassing the medical aspect to help the healthcare practitioners, policymakers, decision makers, policymakers, AI scientists and virologists to quell this infectious COVID-19 pandemic outbreak.
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Affiliation(s)
- Jawad Rasheed
- Department of Computer Engineering, Istanbul Aydin University, Istanbul, 34295, Turkey.
| | - Akhtar Jamil
- Department of Computer Engineering, Istanbul Sabahattin Zaim University, Istanbul, 34303, Turkey
| | - Alaa Ali Hameed
- Department of Computer Engineering, Istanbul Sabahattin Zaim University, Istanbul, 34303, Turkey
| | - Fadi Al-Turjman
- Artificial Intelligence Engineering Department, Research Center for AI and IoT, Near East University, Nicosia, Mersin 10, Turkey
| | - Ahmad Rasheed
- Department of Electrical and Electronics Engineering, Eastern Mediterranean University, Famagusta, Mersin 10, Turkey
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77
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Mazurek J. The evaluation of COVID-19 prediction precision with a Lyapunov-like exponent. PLoS One 2021; 16:e0252394. [PMID: 34048475 PMCID: PMC8162606 DOI: 10.1371/journal.pone.0252394] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2021] [Accepted: 05/16/2021] [Indexed: 12/23/2022] Open
Abstract
In the field of machine learning, building models and measuring their performance are two equally important tasks. Currently, measures of precision of regression models' predictions are usually based on the notion of mean error, where by error we mean a deviation of a prediction from an observation. However, these mean based measures of models' performance have two drawbacks. Firstly, they ignore the length of the prediction, which is crucial when dealing with chaotic systems, where a small deviation at the beginning grows exponentially with time. Secondly, these measures are not suitable in situations where a prediction is made for a specific point in time (e.g. a date), since they average all errors from the start of the prediction to its end. Therefore, the aim of this paper is to propose a new measure of models' prediction precision, a divergence exponent, based on the notion of the Lyapunov exponent which overcomes the aforementioned drawbacks. The proposed approach enables the measuring and comparison of models' prediction precision for time series with unequal length and a given target date in the framework of chaotic phenomena. Application of the divergence exponent to the evaluation of models' accuracy is demonstrated by two examples and then a set of selected predictions of COVID-19 spread from other studies is evaluated to show its potential.
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Affiliation(s)
- Jiří Mazurek
- Silesian University in Opava, School of Business Administration in Karvina, Karvina, Czech Republic
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78
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Rahman MM, Paul KC, Hossain MA, Ali GGMN, Rahman MS, Thill JC. Machine Learning on the COVID-19 Pandemic, Human Mobility and Air Quality: A Review. IEEE ACCESS : PRACTICAL INNOVATIONS, OPEN SOLUTIONS 2021; 9:72420-72450. [PMID: 34786314 PMCID: PMC8545207 DOI: 10.1109/access.2021.3079121] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/25/2021] [Accepted: 05/07/2021] [Indexed: 05/19/2023]
Abstract
The ongoing COVID-19 global pandemic is touching every facet of human lives (e.g., public health, education, economy, transportation, and the environment). This novel pandemic and non-pharmaceutical interventions of lockdown and confinement implemented citywide, regionally or nationally are affecting virus transmission, people's travel patterns, and air quality. Many studies have been conducted to predict the diffusion of the COVID-19 disease, assess the impacts of the pandemic on human mobility and on air quality, and assess the impacts of lockdown measures on viral spread with a range of Machine Learning (ML) techniques. This literature review aims to analyze the results from past research to understand the interactions among the COVID-19 pandemic, lockdown measures, human mobility, and air quality. The critical review of prior studies indicates that urban form, people's socioeconomic and physical conditions, social cohesion, and social distancing measures significantly affect human mobility and COVID-19 viral transmission. During the COVID-19 pandemic, many people are inclined to use private transportation for necessary travel to mitigate coronavirus-related health problems. This review study also noticed that COVID-19 related lockdown measures significantly improve air quality by reducing the concentration of air pollutants, which in turn improves the COVID-19 situation by reducing respiratory-related sickness and deaths. It is argued that ML is a powerful, effective, and robust analytic paradigm to handle complex and wicked problems such as a global pandemic. This study also explores the spatio-temporal aspects of lockdown and confinement measures on coronavirus diffusion, human mobility, and air quality. Additionally, we discuss policy implications, which will be helpful for policy makers to take prompt actions to moderate the severity of the pandemic and improve urban environments by adopting data-driven analytic methods.
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Affiliation(s)
- Md. Mokhlesur Rahman
- The William States Lee College of EngineeringUniversity of North Carolina at CharlotteCharlotteNC28223USA
- Department of Urban and Regional PlanningKhulna University of Engineering and Technology (KUET)Khulna9203Bangladesh
| | - Kamal Chandra Paul
- Department of Electrical and Computer EngineeringThe William States Lee College of EngineeringUniversity of North Carolina at CharlotteCharlotteNC28223USA
| | - Md. Amjad Hossain
- Department of Computer Science, Mathematics and EngineeringShepherd UniversityShepherdstownWV25443USA
| | - G. G. Md. Nawaz Ali
- Department of Applied Computer ScienceUniversity of CharlestonCharlestonWV25304USA
| | - Md. Shahinoor Rahman
- Department of Earth and Environmental SciencesNew Jersey City UniversityJersey CityNJ07305USA
| | - Jean-Claude Thill
- Department of Geography and Earth SciencesSchool of Data ScienceUniversity of North Carolina at CharlotteCharlotteNC28223USA
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79
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Short-Term Prediction of COVID-19 Cases Using Machine Learning Models. APPLIED SCIENCES-BASEL 2021. [DOI: 10.3390/app11094266] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
The first case in Bangladesh of the novel coronavirus disease (COVID-19) was reported on 8 March 2020, with the number of confirmed cases rapidly rising to over 175,000 by July 2020. In the absence of effective treatment, an essential tool of health policy is the modeling and forecasting of the progress of the pandemic. We, therefore, developed a cloud-based machine learning short-term forecasting model for Bangladesh, in which several regression-based machine learning models were applied to infected case data to estimate the number of COVID-19-infected people over the following seven days. This approach can accurately forecast the number of infected cases daily by training the prior 25 days sample data recorded on our web application. The outcomes of these efforts could aid the development and assessment of prevention strategies and identify factors that most affect the spread of COVID-19 infection in Bangladesh.
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80
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Patrício A, Costa RS, Henriques R. Predictability of COVID-19 Hospitalizations, Intensive Care Unit Admissions, and Respiratory Assistance in Portugal: Longitudinal Cohort Study. J Med Internet Res 2021; 23:e26075. [PMID: 33835931 PMCID: PMC8080965 DOI: 10.2196/26075] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2020] [Revised: 02/14/2021] [Accepted: 03/18/2021] [Indexed: 01/17/2023] Open
Abstract
BACKGROUND In the face of the current COVID-19 pandemic, the timely prediction of upcoming medical needs for infected individuals enables better and quicker care provision when necessary and management decisions within health care systems. OBJECTIVE This work aims to predict the medical needs (hospitalizations, intensive care unit admissions, and respiratory assistance) and survivability of individuals testing positive for SARS-CoV-2 infection in Portugal. METHODS A retrospective cohort of 38,545 infected individuals during 2020 was used. Predictions of medical needs were performed using state-of-the-art machine learning approaches at various stages of a patient's cycle, namely, at testing (prehospitalization), at posthospitalization, and during postintensive care. A thorough optimization of state-of-the-art predictors was undertaken to assess the ability to anticipate medical needs and infection outcomes using demographic and comorbidity variables, as well as dates associated with symptom onset, testing, and hospitalization. RESULTS For the target cohort, 75% of hospitalization needs could be identified at the time of testing for SARS-CoV-2 infection. Over 60% of respiratory needs could be identified at the time of hospitalization. Both predictions had >50% precision. CONCLUSIONS The conducted study pinpoints the relevance of the proposed predictive models as good candidates to support medical decisions in the Portuguese population, including both monitoring and in-hospital care decisions. A clinical decision support system is further provided to this end.
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Affiliation(s)
- André Patrício
- Instituto Superior Técnico, Universidade de Lisboa, Lisboa, Portugal
| | - Rafael S Costa
- LAQV-REQUIMTE, NOVA School of Science and Technology, Universidade NOVA de Lisboa, Caparica, Portugal
- IDMEC, Instituto Superior Técnico, Universidade de Lisboa, Lisboa, Portugal
| | - Rui Henriques
- Instituto Superior Técnico, Universidade de Lisboa, Lisboa, Portugal
- Instituto de Engenharia de Sistemas e Computadores-Investigação e Desenvolvimento, Lisboa, Portugal
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81
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Torres–Signes A, Frías MP, Ruiz-Medina MD. COVID-19 mortality analysis from soft-data multivariate curve regression and machine learning. STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT : RESEARCH JOURNAL 2021; 35:2659-2678. [PMID: 33897300 PMCID: PMC8053745 DOI: 10.1007/s00477-021-02021-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 04/03/2021] [Indexed: 05/25/2023]
Abstract
UNLABELLED A multiple objective space-time forecasting approach is presented involving cyclical curve log-regression, and multivariate time series spatial residual correlation analysis. Specifically, the mean quadratic loss function is minimized in the framework of trigonometric regression. While, in our subsequent spatial residual correlation analysis, maximization of the likelihood allows us to compute the posterior mode in a Bayesian multivariate time series soft-data framework. The presented approach is applied to the analysis of COVID-19 mortality in the first wave affecting the Spanish Communities, since March 8, 2020 until May 13, 2020. An empirical comparative study with Machine Learning (ML) regression, based on random k-fold cross-validation, and bootstrapping confidence interval and probability density estimation, is carried out. This empirical analysis also investigates the performance of ML regression models in a hard- and soft-data frameworks. The results could be extrapolated to other counts, countries, and posterior COVID-19 waves. SUPPLEMENTARY INFORMATION The online version contains supplementary material available at 10.1007/s00477-021-02021-0.
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Affiliation(s)
- Antoni Torres–Signes
- Department of Statistics and Operation Research, Faculty of Sciences, University of Málaga, Málaga, Spain
| | - María P. Frías
- Department of Statistics and Operation Research, Faculty of Sciences, University of Jaén, Jaén, Spain
| | - María D. Ruiz-Medina
- Department of Statistics and Operation Research, Faculty of Sciences, University of Granada, Granada, Spain
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82
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Musulin J, Baressi Šegota S, Štifanić D, Lorencin I, Anđelić N, Šušteršič T, Blagojević A, Filipović N, Ćabov T, Markova-Car E. Application of Artificial Intelligence-Based Regression Methods in the Problem of COVID-19 Spread Prediction: A Systematic Review. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:4287. [PMID: 33919496 PMCID: PMC8073788 DOI: 10.3390/ijerph18084287] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/23/2021] [Revised: 04/14/2021] [Accepted: 04/16/2021] [Indexed: 02/07/2023]
Abstract
COVID-19 is one of the greatest challenges humanity has faced recently, forcing a change in the daily lives of billions of people worldwide. Therefore, many efforts have been made by researchers across the globe in the attempt of determining the models of COVID-19 spread. The objectives of this review are to analyze some of the open-access datasets mostly used in research in the field of COVID-19 regression modeling as well as present current literature based on Artificial Intelligence (AI) methods for regression tasks, like disease spread. Moreover, we discuss the applicability of Machine Learning (ML) and Evolutionary Computing (EC) methods that have focused on regressing epidemiology curves of COVID-19, and provide an overview of the usefulness of existing models in specific areas. An electronic literature search of the various databases was conducted to develop a comprehensive review of the latest AI-based approaches for modeling the spread of COVID-19. Finally, a conclusion is drawn from the observation of reviewed papers that AI-based algorithms have a clear application in COVID-19 epidemiological spread modeling and may be a crucial tool in the combat against coming pandemics.
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Affiliation(s)
- Jelena Musulin
- Faculty of Engineering, University of Rijeka, Vukovarska 58, 51000 Rijeka, Croatia; (J.M.); (D.Š.); (I.L.); (N.A.)
| | - Sandi Baressi Šegota
- Faculty of Engineering, University of Rijeka, Vukovarska 58, 51000 Rijeka, Croatia; (J.M.); (D.Š.); (I.L.); (N.A.)
| | - Daniel Štifanić
- Faculty of Engineering, University of Rijeka, Vukovarska 58, 51000 Rijeka, Croatia; (J.M.); (D.Š.); (I.L.); (N.A.)
| | - Ivan Lorencin
- Faculty of Engineering, University of Rijeka, Vukovarska 58, 51000 Rijeka, Croatia; (J.M.); (D.Š.); (I.L.); (N.A.)
| | - Nikola Anđelić
- Faculty of Engineering, University of Rijeka, Vukovarska 58, 51000 Rijeka, Croatia; (J.M.); (D.Š.); (I.L.); (N.A.)
| | - Tijana Šušteršič
- Faculty of Engineering, University of Kragujevac, Sestre Janjić, 34000 Kragujevac, Serbia; (T.Š.); (A.B.); (N.F.)
- Bioengineering Research and Development Centre (BioIRC), Prvoslava Stojanovića 6, 34000 Kragujevac, Serbia
| | - Anđela Blagojević
- Faculty of Engineering, University of Kragujevac, Sestre Janjić, 34000 Kragujevac, Serbia; (T.Š.); (A.B.); (N.F.)
- Bioengineering Research and Development Centre (BioIRC), Prvoslava Stojanovića 6, 34000 Kragujevac, Serbia
| | - Nenad Filipović
- Faculty of Engineering, University of Kragujevac, Sestre Janjić, 34000 Kragujevac, Serbia; (T.Š.); (A.B.); (N.F.)
- Bioengineering Research and Development Centre (BioIRC), Prvoslava Stojanovića 6, 34000 Kragujevac, Serbia
| | - Tomislav Ćabov
- Faculty of Dental Medicine, University of Rijeka, Krešimirova ul. 40, 51000 Rijeka, Croatia;
| | - Elitza Markova-Car
- Department of Biotechnology, University of Rijeka, Radmile Matejčić 2, 51000 Rijeka, Croatia;
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83
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Sheng H, Wu L, Wu T, Peng B. Network dynamic model of epidemic transmission introducing a heterogeneous control factor. J Med Virol 2021; 93:6496-6505. [PMID: 33851729 PMCID: PMC8250401 DOI: 10.1002/jmv.27025] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2020] [Revised: 04/05/2021] [Accepted: 04/12/2021] [Indexed: 01/14/2023]
Abstract
The COVID-19 epidemic is not only a medical issue but also a sophisticated social problem. We propose a network dynamics model of epidemic transmission introducing a heterogeneous control factor. The proposed model applied the classical susceptible- exposed-infectious-recovered model to the network based on effective distance and was modified by introducing a heterogeneous control factor with temporal and spatial characteristics. International aviation data were approximately used to estimate the flux fraction matrix, and the effective distance was calculated. Through parameter estimation and simulation, the theoretical values of the modified model fit well with practical values. By adjusting the parameters and observing the change of the results, we found that the modified model is more in line with the actual needs and has higher credibility in the comprehensive analysis. The assessment shows that the number of confirmed cases worldwide will reach about 20 million optimistically. In severe cases, the peak value will exceed 80 million, and the late stage of the epidemic shows a long tail shape, lasting more than one and a half years. The effective way to control the global epidemic is to strengthen international cooperation and to impose international travel restrictions and other measures.
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Affiliation(s)
- Huaxiong Sheng
- Graduate School of National Defense University, Beijing, China
| | - Lin Wu
- Joint Operation College of National Defense University, Beijing, China
| | - Tingting Wu
- Graduate School of National Defense University, Beijing, China
| | - Bo Peng
- Joint Operation College of National Defense University, Beijing, China
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84
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Chowdhury AA, Hasan KT, Hoque KKS. Analysis and Prediction of COVID-19 Pandemic in Bangladesh by Using ANFIS and LSTM Network. Cognit Comput 2021; 13:761-770. [PMID: 33868501 PMCID: PMC8041393 DOI: 10.1007/s12559-021-09859-0] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2020] [Accepted: 03/30/2021] [Indexed: 02/05/2023]
Abstract
The dangerously contagious virus named "COVID-19" has struck the world strong and has locked down billions of people in their homes to stop the further spread. All the researchers and scientists in various fields are continually developing a vaccine and prevention methods to aid the world from this challenging situation. However, a reliable prediction of the epidemic may help control this contiguous disease until the cure is available. The machine learning techniques are one of the frontiers in predicting this outbreak's future trend and behavior. Our research is focused on finding a suitable machine learning algorithm that can predict the COVID-19 daily new cases with higher accuracy. This research has used the adaptive neuro-fuzzy inference system (ANFIS) and the long short-term memory (LSTM) to foresee the newly infected cases in Bangladesh. We have compared both the experiments' results, and it can be forenamed that LSTM has shown more satisfactory results. Upon study and testing on several models, we have shown that LSTM works better on a scenario-based model for Bangladesh with mean absolute percentage error (MAPE)-4.51, root-mean-square error (RMSE)-6.55, and correlation coefficient-0.75. This study is expected to shed light on COVID-19 prediction models for researchers working with machine learning techniques and avoid proven failures, especially for small imprecise datasets.
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Affiliation(s)
- Anjir Ahmed Chowdhury
- Department of Computer Science and Engineering, American International University-Bangladesh, Dhaka, Bangladesh
| | - Khandaker Tabin Hasan
- Department of Computer Science and Engineering, American International University-Bangladesh, Dhaka, Bangladesh
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85
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Lv Y, Ma C, Li X, Wu M. Big data driven COVID-19 pandemic crisis management: potential approach for global health. Arch Med Sci 2021; 17:829-837. [PMID: 34025856 PMCID: PMC8130465 DOI: 10.5114/aoms/133522] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/13/2020] [Accepted: 02/21/2021] [Indexed: 12/23/2022] Open
Abstract
INTRODUCTION Information has the power to protect against unexpected events and control any crisis such as the COVID-19 pandemic. Since COVID-19 has already rapidly spread all over the world, only technology-driven data management can provide accurate information to manage the crisis. This study aims to explore the potential of big data technologies for controlling COVID-19 transmission and managing it effectively. METHODS A systematic review guided by PRISMA guidelines has been performed to obtain the key elements. RESULTS This study identified the thirty-two most relevant documents for qualitative analysis. This study also reveals 10 possible sources and 8 key applications of big data for analyzing the virus infection trend, transmission pattern, virus association, and differences of genetic modifications. It also explores several limitations of big data usage including unethical use, privacy, and exploitative use of data. CONCLUSIONS The findings of the study will provide new insight and help policymakers and administrators to develop data-driven initiatives to tackle and manage the COVID-19 crisis.
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Affiliation(s)
- Yang Lv
- School of Public Administration, Sichuan University, China
| | - Chenwei Ma
- School of Public Administration, Sichuan University, China
| | - Xiaohan Li
- School of Public Administration, Sichuan University, China
| | - Min Wu
- School of Public Administration, Sichuan University, China
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86
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Braga MDB, Fernandes RDS, de Souza GN, da Rocha JEC, Dolácio CJF, Tavares IDS, Pinheiro RR, Noronha FN, Rodrigues LLS, Ramos RTJ, Carneiro AR, de Brito SR, Diniz HAC, Botelho MDN, Vallinoto ACR. Artificial neural networks for short-term forecasting of cases, deaths, and hospital beds occupancy in the COVID-19 pandemic at the Brazilian Amazon. PLoS One 2021; 16:e0248161. [PMID: 33705453 PMCID: PMC7951831 DOI: 10.1371/journal.pone.0248161] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2020] [Accepted: 02/20/2021] [Indexed: 12/24/2022] Open
Abstract
The first case of the novel coronavirus in Brazil was notified on February 26, 2020. After 21 days, the first case was reported in the second largest State of the Brazilian Amazon. The State of Pará presented difficulties in combating the pandemic, ranging from underreporting and a low number of tests to a large territorial distance between cities with installed hospital capacity. Due to these factors, mathematical data-driven short-term forecasting models can be a promising initiative to assist government officials in more agile and reliable actions. This study presents an approach based on artificial neural networks for the daily and cumulative forecasts of cases and deaths caused by COVID-19, and the forecast of demand for hospital beds. Six scenarios with different periods were used to identify the quality of the generated forecasting and the period in which they start to deteriorate. Results indicated that the computational model adapted capably to the training period and was able to make consistent short-term forecasts, especially for the cumulative variables and for demand hospital beds.
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Affiliation(s)
| | | | | | | | | | - Ivaldo da Silva Tavares
- Forestry Engineering Department, Universidade Federal de Viçosa, Viçosa, Minas Gerais, Brazil
| | | | | | - Luana Lorena Silva Rodrigues
- Postgraduate Program in Health Sciences, Institute of Collective Health, Universidade Federal do Oeste do Pará, Santarém, Pará, Brazil
| | | | | | | | - Hugo Alex Carneiro Diniz
- Institute of Educational Sciences, Universidade Federal do Oeste do Pará, Santarém, Pará, Brazil
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87
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Dharani NP, Bojja P, Raja Kumari P. Evaluation of Performance of an LR and SVR models to predict COVID-19 Pandemic. MATERIALS TODAY. PROCEEDINGS 2021:S2214-7853(21)01248-7. [PMID: 33614417 PMCID: PMC7885699 DOI: 10.1016/j.matpr.2021.02.166] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/16/2021] [Accepted: 02/05/2021] [Indexed: 12/12/2022]
Abstract
Recently, in December 2019 the Coronavirus disease surprisingly influenced the lives of millions of people in the world with its swift spread. To support medical experts/doctors with the overpowering challenge of prediction of total cases in India, a machine-learning algorithm was developed. In this research article, the author describes the possibility of predicting the COVID-19 total, active cases, death and cured cases in India up to 25th June 2020 by applying linear regression and support vector machine. It is extremely tricky to manage the occurrence of corona virus since it is expanding exponentially day to day and is difficult to handle with a limited number of doctors and beds to treat the infected individuals with limited time. Hence, it is essential to develop a machine learning based computerized predicting model. The development effort in this article is based on publicly available data that is downloaded from KAGGLE to estimate the spread of the disease within a short period. We have calculated the RMSE, R2, MAE of LR and SVR models and concluded that the RMSE of linear regression is less than the SVR. Therefore, the LR will help doctors to forecast for the next few days.
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Affiliation(s)
- N P Dharani
- Department of ECE, Koneru Lakshmaiah Education Foundation, Guntur, India
| | - Polaiah Bojja
- Department of ECE, Koneru Lakshmaiah Education Foundation, Guntur, India
| | - Pamula Raja Kumari
- Department of ECE, Koneru Lakshmaiah Education Foundation, Guntur, India
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88
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Sun C, Hong S, Song M, Li H, Wang Z. Predicting COVID-19 disease progression and patient outcomes based on temporal deep learning. BMC Med Inform Decis Mak 2021; 21:45. [PMID: 33557818 PMCID: PMC7869774 DOI: 10.1186/s12911-020-01359-9] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2020] [Accepted: 11/30/2020] [Indexed: 01/08/2023] Open
Abstract
BACKGROUND The coronavirus disease 2019 (COVID-19) pandemic has caused health concerns worldwide since December 2019. From the beginning of infection, patients will progress through different symptom stages, such as fever, dyspnea or even death. Identifying disease progression and predicting patient outcome at an early stage helps target treatment and resource allocation. However, there is no clear COVID-19 stage definition, and few studies have addressed characterizing COVID-19 progression, making the need for this study evident. METHODS We proposed a temporal deep learning method, based on a time-aware long short-term memory (T-LSTM) neural network and used an online open dataset, including blood samples of 485 patients from Wuhan, China, to train the model. Our method can grasp the dynamic relations in irregularly sampled time series, which is ignored by existing works. Specifically, our method predicted the outcome of COVID-19 patients by considering both the biomarkers and the irregular time intervals. Then, we used the patient representations, extracted from T-LSTM units, to subtype the patient stages and describe the disease progression of COVID-19. RESULTS Using our method, the accuracy of the outcome of prediction results was more than 90% at 12 days and 98, 95 and 93% at 3, 6, and 9 days, respectively. Most importantly, we found 4 stages of COVID-19 progression with different patient statuses and mortality risks. We ranked 40 biomarkers related to disease and gave the reference values of them for each stage. Top 5 is Lymph, LDH, hs-CRP, Indirect Bilirubin, Creatinine. Besides, we have found 3 complications - myocardial injury, liver function injury and renal function injury. Predicting which of the 4 stages the patient is currently in can help doctors better assess and cure the patient. CONCLUSIONS To combat the COVID-19 epidemic, this paper aims to help clinicians better assess and treat infected patients, provide relevant researchers with potential disease progression patterns, and enable more effective use of medical resources. Our method predicted patient outcomes with high accuracy and identified a four-stage disease progression. We hope that the obtained results and patterns will aid in fighting the disease.
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Affiliation(s)
- Chenxi Sun
- School of Electronics Engineering and Computer Science, Peking University, Beijing, People's Republic of China
- Key Laboratory of Machine Perception (Ministry of Education), Peking University, Beijing, People's Republic of China
| | - Shenda Hong
- National Institute of Health Data Science, Peking University, Beijing, People's Republic of China
- Institute of Medical Technology, Health Science Center of Peking University, Beijing, People's Republic of China
| | - Moxian Song
- School of Electronics Engineering and Computer Science, Peking University, Beijing, People's Republic of China
- Key Laboratory of Machine Perception (Ministry of Education), Peking University, Beijing, People's Republic of China
| | - Hongyan Li
- School of Electronics Engineering and Computer Science, Peking University, Beijing, People's Republic of China.
- Key Laboratory of Machine Perception (Ministry of Education), Peking University, Beijing, People's Republic of China.
| | - Zhenjie Wang
- Institute of Population Research, Peking University, No.5 Yiheyuan Road, Beijing, 100871, People's Republic of China.
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89
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Rahimi I, Chen F, Gandomi AH. A review on COVID-19 forecasting models. Neural Comput Appl 2021; 35:1-11. [PMID: 33564213 PMCID: PMC7861008 DOI: 10.1007/s00521-020-05626-8] [Citation(s) in RCA: 40] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2020] [Accepted: 12/11/2020] [Indexed: 12/23/2022]
Abstract
The novel coronavirus (COVID-19) has spread to more than 200 countries worldwide, leading to more than 36 million confirmed cases as of October 10, 2020. As such, several machine learning models that can forecast the outbreak globally have been released. This work presents a review and brief analysis of the most important machine learning forecasting models against COVID-19. The work presented in this study possesses two parts. In the first section, a detailed scientometric analysis presents an influential tool for bibliometric analyses, which were performed on COVID-19 data from the Scopus and Web of Science databases. For the above-mentioned analysis, keywords and subject areas are addressed, while the classification of machine learning forecasting models, criteria evaluation, and comparison of solution approaches are discussed in the second section of the work. The conclusion and discussion are provided as the final sections of this study.
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Affiliation(s)
- Iman Rahimi
- Department of Mechanical and Manufacturing Engineering, Faculty of Engineering, Universiti Putra Malaysia, Seri Kembangan, Malaysia
| | - Fang Chen
- Data Science Institute, University of Technology Sydney, Ultimo, 2007 NSW Australia
| | - Amir H. Gandomi
- Data Science Institute, University of Technology Sydney, Ultimo, 2007 NSW Australia
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90
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Silva JCS, de Lima Silva DF, Delgado Neto ADS, Ferraz A, Melo JL, Ferreira Júnior NR, de Almeida Filho AT. A city cluster risk-based approach for Sars-CoV-2 and isolation barriers based on anonymized mobile phone users' location data. SUSTAINABLE CITIES AND SOCIETY 2021; 65:102574. [PMID: 33178556 PMCID: PMC7644257 DOI: 10.1016/j.scs.2020.102574] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/17/2020] [Revised: 10/22/2020] [Accepted: 10/23/2020] [Indexed: 05/04/2023]
Abstract
Given the recent outbreak of Sars-CoV-2, several countries started to seek different strategies to control contamination and minimize fatalities, which are usually the primary objectives for all strategies. Secondary objectives are related to economic factors, therefore ensuring that society would be able is to keep its essential activities and avoid supply disruptions. This paper presents an application of anonymized mobile phone users' location data to estimate population flow amongst cities with an origin-destination matrix. The work includes a clustering analysis of cities, which may enable policymakers (and epidemiologists) to develop public policies giving the appropriate consideration for each set of cities within a Province or State. Risk measures are included to analyze the severity of the spread among the clusters, which can be ranked. Then, intelligence can be obtained from the analysis, and some clusters could be isolated to avoid contagion while keeping their economic activities. Therefore, this analysis is reproducible for other states of Brazil and other countries and can be adapted for districts within a city, especially considering the possibility of a second wave COVID-19 pandemic.
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91
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Syeda HB, Syed M, Sexton KW, Syed S, Begum S, Syed F, Prior F, Yu F. Role of Machine Learning Techniques to Tackle the COVID-19 Crisis: Systematic Review. JMIR Med Inform 2021; 9:e23811. [PMID: 33326405 PMCID: PMC7806275 DOI: 10.2196/23811] [Citation(s) in RCA: 71] [Impact Index Per Article: 23.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2020] [Revised: 10/27/2020] [Accepted: 11/15/2020] [Indexed: 12/15/2022] Open
Abstract
BACKGROUND SARS-CoV-2, the novel coronavirus responsible for COVID-19, has caused havoc worldwide, with patients presenting a spectrum of complications that have pushed health care experts to explore new technological solutions and treatment plans. Artificial Intelligence (AI)-based technologies have played a substantial role in solving complex problems, and several organizations have been swift to adopt and customize these technologies in response to the challenges posed by the COVID-19 pandemic. OBJECTIVE The objective of this study was to conduct a systematic review of the literature on the role of AI as a comprehensive and decisive technology to fight the COVID-19 crisis in the fields of epidemiology, diagnosis, and disease progression. METHODS A systematic search of PubMed, Web of Science, and CINAHL databases was performed according to PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analysis) guidelines to identify all potentially relevant studies published and made available online between December 1, 2019, and June 27, 2020. The search syntax was built using keywords specific to COVID-19 and AI. RESULTS The search strategy resulted in 419 articles published and made available online during the aforementioned period. Of these, 130 publications were selected for further analyses. These publications were classified into 3 themes based on AI applications employed to combat the COVID-19 crisis: Computational Epidemiology, Early Detection and Diagnosis, and Disease Progression. Of the 130 studies, 71 (54.6%) focused on predicting the COVID-19 outbreak, the impact of containment policies, and potential drug discoveries, which were classified under the Computational Epidemiology theme. Next, 40 of 130 (30.8%) studies that applied AI techniques to detect COVID-19 by using patients' radiological images or laboratory test results were classified under the Early Detection and Diagnosis theme. Finally, 19 of the 130 studies (14.6%) that focused on predicting disease progression, outcomes (ie, recovery and mortality), length of hospital stay, and number of days spent in the intensive care unit for patients with COVID-19 were classified under the Disease Progression theme. CONCLUSIONS In this systematic review, we assembled studies in the current COVID-19 literature that utilized AI-based methods to provide insights into different COVID-19 themes. Our findings highlight important variables, data types, and available COVID-19 resources that can assist in facilitating clinical and translational research.
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Affiliation(s)
- Hafsa Bareen Syeda
- Department of Neurology, University of Arkansas for Medical Sciences, Little Rock, AR, United States
| | - Mahanazuddin Syed
- Department of Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, AR, United States
| | - Kevin Wayne Sexton
- Department of Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, AR, United States
- Department of Surgery, University of Arkansas for Medical Sciences, Little Rock, AR, United States
- Department of Health Policy and Management, University of Arkansas for Medical Sciences, Little Rock, AR, United States
| | - Shorabuddin Syed
- Department of Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, AR, United States
| | - Salma Begum
- Department of Information Technology, University of Arkansas for Medical Sciences, Little Rock, AR, United States
| | - Farhanuddin Syed
- College of Medicine, Shadan Institute of Medical Sciences, Hyderabad, India
| | - Fred Prior
- Department of Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, AR, United States
- Department of Radiology, University of Arkansas for Medical Sciences, Little Rock, AR, United States
| | - Feliciano Yu
- Department of Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, AR, United States
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92
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Jalilian A, Mateu J. A hierarchical spatio-temporal model to analyze relative risk variations of COVID-19: a focus on Spain, Italy and Germany. STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT : RESEARCH JOURNAL 2021; 35:797-812. [PMID: 33776559 PMCID: PMC7985594 DOI: 10.1007/s00477-021-02003-2] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 03/08/2021] [Indexed: 05/07/2023]
Abstract
The novel coronavirus disease (COVID-19) has spread rapidly across the world in a short period of time and with a heterogeneous pattern. Understanding the underlying temporal and spatial dynamics in the spread of COVID-19 can result in informed and timely public health policies. In this paper, we use a spatio-temporal stochastic model to explain the temporal and spatial variations in the daily number of new confirmed cases in Spain, Italy and Germany from late February 2020 to mid January 2021. Using a hierarchical Bayesian framework, we found that the temporal trends of the epidemic in the three countries rapidly reached their peaks and slowly started to decline at the beginning of April and then increased and reached their second maximum in the middle of November. However decline and increase of the temporal trend seems to show different patterns in Spain, Italy and Germany.
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Affiliation(s)
- Abdollah Jalilian
- Department of Statistics, Razi University, Kermanshah, 67149-67346 Iran
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93
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Tayarani N MH. Applications of artificial intelligence in battling against covid-19: A literature review. CHAOS, SOLITONS, AND FRACTALS 2021; 142:110338. [PMID: 33041533 PMCID: PMC7532790 DOI: 10.1016/j.chaos.2020.110338] [Citation(s) in RCA: 59] [Impact Index Per Article: 19.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/21/2020] [Accepted: 10/01/2020] [Indexed: 05/14/2023]
Abstract
Colloquially known as coronavirus, the Severe Acute Respiratory Syndrome CoronaVirus 2 (SARS-CoV-2), that causes CoronaVirus Disease 2019 (COVID-19), has become a matter of grave concern for every country around the world. The rapid growth of the pandemic has wreaked havoc and prompted the need for immediate reactions to curb the effects. To manage the problems, many research in a variety of area of science have started studying the issue. Artificial Intelligence is among the area of science that has found great applications in tackling the problem in many aspects. Here, we perform an overview on the applications of AI in a variety of fields including diagnosis of the disease via different types of tests and symptoms, monitoring patients, identifying severity of a patient, processing covid-19 related imaging tests, epidemiology, pharmaceutical studies, etc. The aim of this paper is to perform a comprehensive survey on the applications of AI in battling against the difficulties the outbreak has caused. Thus we cover every way that AI approaches have been employed and to cover all the research until the writing of this paper. We try organize the works in a way that overall picture is comprehensible. Such a picture, although full of details, is very helpful in understand where AI sits in current pandemonium. We also tried to conclude the paper with ideas on how the problems can be tackled in a better way and provide some suggestions for future works.
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Affiliation(s)
- Mohammad-H Tayarani N
- Biocomputation Group, School of Computer Science, University of Hertfordshire, Hatfield, AL10 9AB, United Kingdom
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94
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Fedorowski JJ. Could amantadine interfere with COVID-19 vaccines based on the LNP-mRNA platform? Arch Med Sci 2021; 17:827-828. [PMID: 34025855 PMCID: PMC8130463 DOI: 10.5114/aoms/134716] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/14/2021] [Accepted: 03/21/2021] [Indexed: 11/17/2022] Open
Abstract
INTRODUCTION Amantadine is a well-known medication with indications in neurology and infectious diseases. It is currently FDA approved for Parkinson's disease, drug-induced extrapyramidal symptoms, and influenza. METHODS The article is the author's original research hypothesis. RESULTS Because more people are going to be vaccinated and additional similar vaccines are going to be introduced, we should take into consideration the potential of amantadine to interfere with LNP-mRNA COVID-19 vaccine delivery into the target cells. CONCLUSIONS A more cautious approach to the patients taking amantadine as far as vaccination utilizing LNP-mRNA platform should be considered.
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Affiliation(s)
- Jaroslaw J. Fedorowski
- Polish Hospital Federation, Poland
- Collegium Humanum Warsaw Management University, Warsaw, Poland
- College of Medicine and Health Network, University of Vermont, Vermont, United States
- Warsaw Maria Curie-Sklodowska Medical University, Warsaw, Poland
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95
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Abstract
This 21st century is notable for experiencing so many disturbances at economic, social, cultural, and political levels in the entire world. The outbreak of novel corona virus 2019 (COVID-19) has been treated as a Public Health crisis of global Concern by the World Health Organization (WHO). Various outbreak models for COVID-19 are being utilized by researchers throughout the world to get well-versed decisions and impose significant control measures. Amid the standard methods for COVID-19 worldwide epidemic prediction, easy statistical, as well as epidemiological methods have got more consideration by researchers and authorities. One main difficulty in controlling the spreading of COVID-19 is the inadequacy and lack of medical tests for detecting as well as identifying a solution. To solve this problem, a few statistical-based advances are being enhanced and turn into a partial resolution up-to some level. To deal with the challenges of the medical field, a broad range of intelligent based methods, frameworks, and equipment have been recommended by Machine Learning (ML) and Deep Learning. As ML and DL have the ability of identifying and predicting patterns in complex large datasets, they are recognized as a suitable procedure for producing effective solutions for the diagnosis of COVID-19. In this paper, a perspective research has been conducted in the applicability of intelligent systems such as ML, DL and others in solving COVID-19 related outbreak issues. The main intention behind this study is (i) to understand the importance of intelligent approaches such as ML and DL for COVID-19 pandemic, (ii) discussing the efficiency and impact of these methods in the prognosis of COVID-19, (iii) the growth in the development of type of ML and advanced ML methods for COVID-19 prognosis,(iv) analyzing the impact of data types and the nature of data along with challenges in processing the data for COVID-19,(v) to focus on some future challenges in COVID-19 prognosis to inspire the researchers for innovating and enhancing their knowledge and research on other impacted sectors due to COVID-19.
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96
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Briz-Redón Á. The impact of modelling choices on modelling outcomes: a spatio-temporal study of the association between COVID-19 spread and environmental conditions in Catalonia (Spain). STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT : RESEARCH JOURNAL 2021; 35:1701-1713. [PMID: 33424434 PMCID: PMC7778699 DOI: 10.1007/s00477-020-01965-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 12/24/2020] [Indexed: 05/07/2023]
Abstract
The choices that researchers make while conducting a statistical analysis usually have a notable impact on the results. This fact has become evident in the ongoing research of the association between the environment and the evolution of the coronavirus disease 2019 (COVID-19) pandemic, in light of the hundreds of contradictory studies that have already been published on this issue in just a few months. In this paper, a COVID-19 dataset containing the number of daily cases registered in the regions of Catalonia (Spain) since the start of the pandemic to the end of August 2020 is analysed using statistical models of diverse levels of complexity. Specifically, the possible effect of several environmental variables (solar exposure, mean temperature, and wind speed) on the number of cases is assessed. Thus, the first objective of the paper is to show how the choice of a certain type of statistical model to conduct the analysis can have a severe impact on the associations that are inferred between the covariates and the response variable. Secondly, it is shown how the use of spatio-temporal models accounting for the nature of the data allows understanding the evolution of the pandemic in space and time. The results suggest that even though the models fitted to the data correctly capture the evolution of COVID-19 in space and time, determining whether there is an association between the spread of the pandemic and certain environmental conditions is complex, as it is severely affected by the choice of the model.
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97
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Zhang WB, Ge Y, Liu M, Atkinson PM, Wang J, Zhang X, Tian Z. Risk assessment of the step-by-step return-to-work policy in Beijing following the COVID-19 epidemic peak. STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT : RESEARCH JOURNAL 2020; 35:481-498. [PMID: 33223954 PMCID: PMC7664171 DOI: 10.1007/s00477-020-01929-3] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 10/30/2020] [Indexed: 05/29/2023]
Abstract
Novel coronavirus (COVID-19) is a new strain of coronavirus first identified in Wuhan, China. As the virus spread worldwide causing a global pandemic, China reduced transmission at considerable social and economic cost. Post-lockdown, resuming work safely, that is, while avoiding a second epidemic outbreak, is a major challenge. Exacerbating this challenge, Beijing hosts many residents and workers with origins elsewhere, making it a relatively high-risk region in which to resume work. Nevertheless, the step-by-step approach taken by Beijing appears to have been effective so far. To learn from the epidemic progression and return-to-work measures undertaken in Beijing, and to inform efforts to avoid a second outbreak of COVID-19, we simulated the epidemiological progression of COVID-19 in Beijing under the real scenario of multiple stages of resuming work. A new epidemic transmission model was developed from a modified SEIR model for SARS, tailored to the situation of Beijing and fitted using multi-source data. Because of strong spatial heterogeneity amongst the population, socio-economic factors and medical capacity of Beijing, the risk assessment was undertaken spatiotemporally with respect to each district of Beijing. The epidemic simulation confirmed that the policy of resuming work step-by step, as implemented in Beijing, was sufficient to avoid a recurrence of the epidemic. Moreover, because of the structure of the model, the simulation provided insights into the specific factors at play at different stages of resuming work, allowing district-specific recommendations to be made with respect to monitoring at different stages of resuming work . As such, this research provides important lessons for other cities and regions dealing with outbreaks of COVID-19 and implementing return-to-work policies.
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Affiliation(s)
- Wen-bin Zhang
- State Key Laboratory of Resources and Environmental Information Systems, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101 China
- University of Chinese Academy of Sciences, Beijing, 100049 China
| | - Yong Ge
- State Key Laboratory of Resources and Environmental Information Systems, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101 China
- University of Chinese Academy of Sciences, Beijing, 100049 China
| | - Mengxiao Liu
- State Key Laboratory of Resources and Environmental Information Systems, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101 China
- University of Chinese Academy of Sciences, Beijing, 100049 China
| | - Peter M. Atkinson
- State Key Laboratory of Resources and Environmental Information Systems, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101 China
- Lancaster Environment Center, Faculty of Science and Technology, Lancaster University, Lancaster, LA1 4YR UK
| | - Jinfeng Wang
- State Key Laboratory of Resources and Environmental Information Systems, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101 China
- University of Chinese Academy of Sciences, Beijing, 100049 China
| | - Xining Zhang
- State Key Laboratory of Resources and Environmental Information Systems, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101 China
- University of Chinese Academy of Sciences, Beijing, 100049 China
| | - Zhaoxing Tian
- Emergency Department of Peking, University International Hospital, Beijing, China
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98
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Dhamodharavadhani S, Rathipriya R, Chatterjee JM. COVID-19 Mortality Rate Prediction for India Using Statistical Neural Network Models. Front Public Health 2020; 8:441. [PMID: 32984242 PMCID: PMC7485390 DOI: 10.3389/fpubh.2020.00441] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2020] [Accepted: 07/17/2020] [Indexed: 02/05/2023] Open
Abstract
The primary aim of this study is to investigate suitable Statistical Neural Network (SNN) models and their hybrid version for COVID-19 mortality prediction in Indian populations and is to estimate the future COVID-19 death cases for India. SNN models such as Probabilistic Neural Network (PNN), Radial Basis Function Neural Network (RBFNN), and Generalized Regression Neural Network (GRNN) are applied to develop the COVID-19 Mortality Rate Prediction (MRP) model for India. For this purpose, we have used two datasets as D1 and D2. The performances of these models are evaluated using Root Mean Square Error (RMSE) and "R," a correlation value between actual and predicted value. To improve prediction accuracy, the new hybrid models have been constructed by combining SNN models and the Non-linear Autoregressive Neural Network (NAR-NN). This is to predict the future error of the SNN models, which adds to the predicted value of these models for getting better MRP value. The results showed that the PNN and RBFNN-based MRP model performed better than the other models for COVID-19 datasets D2 and D1, respectively.
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Affiliation(s)
| | - R Rathipriya
- Department of Computer Science, Periyar University, Salem, India
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99
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Machine Learning Applied to Diagnosis of Human Diseases: A Systematic Review. APPLIED SCIENCES-BASEL 2020. [DOI: 10.3390/app10155135] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Human healthcare is one of the most important topics for society. It tries to find the correct effective and robust disease detection as soon as possible to patients receipt the appropriate cares. Because this detection is often a difficult task, it becomes necessary medicine field searches support from other fields such as statistics and computer science. These disciplines are facing the challenge of exploring new techniques, going beyond the traditional ones. The large number of techniques that are emerging makes it necessary to provide a comprehensive overview that avoids very particular aspects. To this end, we propose a systematic review dealing with the Machine Learning applied to the diagnosis of human diseases. This review focuses on modern techniques related to the development of Machine Learning applied to diagnosis of human diseases in the medical field, in order to discover interesting patterns, making non-trivial predictions and useful in decision-making. In this way, this work can help researchers to discover and, if necessary, determine the applicability of the machine learning techniques in their particular specialties. We provide some examples of the algorithms used in medicine, analysing some trends that are focused on the goal searched, the algorithm used, and the area of applications. We detail the advantages and disadvantages of each technique to help choose the most appropriate in each real-life situation, as several authors have reported. The authors searched Scopus, Journal Citation Reports (JCR), Google Scholar, and MedLine databases from the last decades (from 1980s approximately) up to the present, with English language restrictions, for studies according to the objectives mentioned above. Based on a protocol for data extraction defined and evaluated by all authors using PRISMA methodology, 141 papers were included in this advanced review.
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100
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Iwendi C, Bashir AK, Peshkar A, Sujatha R, Chatterjee JM, Pasupuleti S, Mishra R, Pillai S, Jo O. COVID-19 Patient Health Prediction Using Boosted Random Forest Algorithm. Front Public Health 2020; 8:357. [PMID: 32719767 PMCID: PMC7350612 DOI: 10.3389/fpubh.2020.00357] [Citation(s) in RCA: 179] [Impact Index Per Article: 44.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2020] [Accepted: 06/23/2020] [Indexed: 02/05/2023] Open
Abstract
Integration of artificial intelligence (AI) techniques in wireless infrastructure, real-time collection, and processing of end-user devices is now in high demand. It is now superlative to use AI to detect and predict pandemics of a colossal nature. The Coronavirus disease 2019 (COVID-19) pandemic, which originated in Wuhan China, has had disastrous effects on the global community and has overburdened advanced healthcare systems throughout the world. Globally; over 4,063,525 confirmed cases and 282,244 deaths have been recorded as of 11th May 2020, according to the European Centre for Disease Prevention and Control agency. However, the current rapid and exponential rise in the number of patients has necessitated efficient and quick prediction of the possible outcome of an infected patient for appropriate treatment using AI techniques. This paper proposes a fine-tuned Random Forest model boosted by the AdaBoost algorithm. The model uses the COVID-19 patient's geographical, travel, health, and demographic data to predict the severity of the case and the possible outcome, recovery, or death. The model has an accuracy of 94% and a F1 Score of 0.86 on the dataset used. The data analysis reveals a positive correlation between patients' gender and deaths, and also indicates that the majority of patients are aged between 20 and 70 years.
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Affiliation(s)
- Celestine Iwendi
- BCC of Central South University of Forestry and Technology, Changsha, China
| | - Ali Kashif Bashir
- Department of Computing and Mathematics, Manchester Metropolitan University, Manchester, United Kingdom
| | - Atharva Peshkar
- Department of Information Technology, G H Raisoni College of Engineering, Nagpur, India
| | - R. Sujatha
- School of Information Technology and Engineering, VIT University, Vellore, India
| | - Jyotir Moy Chatterjee
- Department of Information Technology, Lord Buddha Education Foundation, Kathmandu, Nepal
| | - Swetha Pasupuleti
- School of Civil Engineering, Galgotias University, Greater Noida, India
| | - Rishita Mishra
- Department of Electronics and Telecommunications Engineering, G H Raisoni College of Engineering, Nagpur, India
| | - Sofia Pillai
- School of Civil Engineering, Galgotias University, Greater Noida, India
| | - Ohyun Jo
- Department of Computer Science, College of Electrical and Computer Engineering, Chungbuk National University, Cheongju-si, South Korea
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