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Ge S, Wu K, Li S, Li R, Yang C. Machine learning methods for adult OSAHS risk prediction. BMC Health Serv Res 2024; 24:706. [PMID: 38840121 PMCID: PMC11151612 DOI: 10.1186/s12913-024-11081-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2024] [Accepted: 05/07/2024] [Indexed: 06/07/2024] Open
Abstract
BACKGROUND Obstructive sleep apnea hypopnea syndrome (OSAHS) is a common disease that can cause multiple organ damage in the whole body. Our aim was to use machine learning (ML) to build an independent polysomnography (PSG) model to analyze risk factors and predict OSAHS. MATERIALS AND METHODS Clinical data of 2064 snoring patients who underwent physical examination in the Health Management Center of the First Affiliated Hospital of Shanxi Medical University from July 2018 to July 2023 were retrospectively collected, involving 24 characteristic variables. Then they were randomly divided into training group and verification group according to the ratio of 7:3. By analyzing the importance of these features, it was concluded that LDL-C, Cr, common carotid artery plaque, A1c and BMI made major contributions to OSAHS. Moreover, five kinds of machine learning algorithm models such as logistic regression, support vector machine, Boosting, Random Forest and MLP were further established, and cross validation was used to adjust the model hyperparameters to determine the final prediction model. We compared the accuracy, Precision, Recall rate, F1-score and AUC indexes of the model, and finally obtained that MLP was the optimal model with an accuracy of 85.80%, Precision of 0.89, Recall of 0.75, F1-score of 0.82, and AUC of 0.938. CONCLUSION We established the risk prediction model of OSAHS using ML method, and proved that the MLP model performed best among the five ML models. This predictive model helps to identify patients with OSAHS and provide early, personalized diagnosis and treatment options.
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Affiliation(s)
- Shanshan Ge
- Health Management Center, the First Hospital of Shanxi Medical University, Taiyuan, 030001, China.
| | - Kainan Wu
- Health Management Center, the First Hospital of Shanxi Medical University, Taiyuan, 030001, China
| | - Shuhui Li
- Nursing College of Shanxi Medical University, Taiyuan, 030001, China
| | - Ruiling Li
- Nursing College of Shanxi Medical University, Taiyuan, 030001, China
| | - Caizheng Yang
- Nursing College of Shanxi Medical University, Taiyuan, 030001, China
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Saingam P, Jain T, Woicik A, Li B, Candry P, Redcorn R, Wang S, Himmelfarb J, Bryan A, Winkler MKH, Gattuso M. Integrating socio-economic vulnerability factors improves neighborhood-scale wastewater-based epidemiology for public health applications. WATER RESEARCH 2024; 254:121415. [PMID: 38479175 DOI: 10.1016/j.watres.2024.121415] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/23/2023] [Revised: 02/28/2024] [Accepted: 03/03/2024] [Indexed: 04/06/2024]
Abstract
Wastewater Based Epidemiology (WBE) of COVID-19 is a low-cost, non-invasive, and inclusive early warning tool for disease spread. Previously studied WBE focused on sampling at wastewater treatment plant scale, limiting the level at which demographic and geographic variations in disease dynamics can be incorporated into the analysis of certain neighborhoods. This study demonstrates the integration of demographic mapping to improve the WBE of COVID-19 and associated post-COVID disease prediction (here kidney disease) at the neighborhood level using machine learning. WBE was conducted at six neighborhoods in Seattle during October 2020 - February 2022. Wastewater processing and RT-qPCR were performed to obtain SARS-CoV-2 RNA concentration. Census data, clinical data of COVID-19, as well as patient data of acute kidney injury (AKI) cases reported during the study period were collected and the distribution across the city was studied using Geographic Information System (GIS) mapping. Further, we analyzed the data set to better understand socioeconomic impacts on disease prevalence of COVID-19 and AKI per neighborhood. The heterogeneity of eleven demographic factors (such as education and age among others) was observed within neighborhoods across the city of Seattle. Dynamics of COVID-19 clinical cases and wastewater SARS-CoV-2 varied across neighborhood with different levels of demographics. Machine learning models trained with data from the earlier stages of the pandemic were able to predict both COVID-19 and AKI incidence in the later stages of the pandemic (Spearman correlation coefficient of 0·546 - 0·904), with the most predictive model trained on the combination of wastewater data and demographics. The integration of demographics strengthened machine learning models' capabilities to predict prevalence of COVID-19, and of AKI as a marker for post-COVID sequelae. Demographic-based WBE presents an effective tool to monitor and manage public health beyond COVID-19 at the neighborhood level.
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Affiliation(s)
- Prakit Saingam
- Department of Civil and Environmental Engineering, University of Washington, Seattle, WA, United States.
| | - Tanisha Jain
- Department of Civil and Environmental Engineering, University of Washington, Seattle, WA, United States
| | - Addie Woicik
- Department of Computer Science & Engineering, University of Washington, Seattle, WA, United States
| | - Bo Li
- Department of Civil and Environmental Engineering, University of Washington, Seattle, WA, United States
| | - Pieter Candry
- Department of Civil and Environmental Engineering, University of Washington, Seattle, WA, United States
| | - Raymond Redcorn
- Department of Civil and Environmental Engineering, University of Washington, Seattle, WA, United States
| | - Sheng Wang
- Department of Computer Science & Engineering, University of Washington, Seattle, WA, United States
| | - Jonathan Himmelfarb
- Kidney Research Institute, University of Washington, Seattle, WA, United States; Center for Dialysis Innovation, University of Washington, Seattle, WA, United States
| | - Andrew Bryan
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA, United States
| | - Mari K H Winkler
- Department of Civil and Environmental Engineering, University of Washington, Seattle, WA, United States
| | - Meghan Gattuso
- Seattle Public Utilities, Project Delivery and Engineering, 700 5th Ave, Seattle, WA 98104, United States
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Tariq MU, Ismail SB. AI-powered COVID-19 forecasting: a comprehensive comparison of advanced deep learning methods. Osong Public Health Res Perspect 2024; 15:115-136. [PMID: 38621765 PMCID: PMC11082441 DOI: 10.24171/j.phrp.2023.0287] [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: 10/13/2023] [Revised: 01/07/2024] [Accepted: 01/26/2024] [Indexed: 04/17/2024] Open
Abstract
BACKGROUND The coronavirus disease 2019 (COVID-19) pandemic continues to pose significant challenges to the public health sector, including that of the United Arab Emirates (UAE). The objective of this study was to assess the efficiency and accuracy of various deep-learning models in forecasting COVID-19 cases within the UAE, thereby aiding the nation's public health authorities in informed decision-making. METHODS This study utilized a comprehensive dataset encompassing confirmed COVID-19 cases, demographic statistics, and socioeconomic indicators. Several advanced deep learning models, including long short-term memory (LSTM), bidirectional LSTM, convolutional neural network (CNN), CNN-LSTM, multilayer perceptron, and recurrent neural network (RNN) models, were trained and evaluated. Bayesian optimization was also implemented to fine-tune these models. RESULTS The evaluation framework revealed that each model exhibited different levels of predictive accuracy and precision. Specifically, the RNN model outperformed the other architectures even without optimization. Comprehensive predictive and perspective analytics were conducted to scrutinize the COVID-19 dataset. CONCLUSION This study transcends academic boundaries by offering critical insights that enable public health authorities in the UAE to deploy targeted data-driven interventions. The RNN model, which was identified as the most reliable and accurate for this specific context, can significantly influence public health decisions. Moreover, the broader implications of this research validate the capability of deep learning techniques in handling complex datasets, thus offering the transformative potential for predictive accuracy in the public health and healthcare sectors.
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Affiliation(s)
- Muhammad Usman Tariq
- Marketing, Operations, and Information System, Abu Dhabi University, Abu Dhabi, United Arab Emirates
- Faculty of Computer Science and Information Technology, Univesiti Tun Hussien Onn Malaysia, Parit Raja, Malaysia
| | - Shuhaida Binti Ismail
- Faculty of Computer Science and Information Technology, Univesiti Tun Hussien Onn Malaysia, Parit Raja, Malaysia
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Song TH, Clemente L, Pan X, Jang J, Santillana M, Lee K. Fine-Grained Forecasting of COVID-19 Trends at the County Level in the United States. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.01.13.24301248. [PMID: 38293076 PMCID: PMC10827234 DOI: 10.1101/2024.01.13.24301248] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2024]
Abstract
The novel coronavirus (COVID-19) pandemic, first identified in Wuhan China in December 2019, has profoundly impacted various aspects of daily life, society, healthcare systems, and global health policies. There have been more than half a billion human infections and more than 6 million deaths globally attributable to COVID-19. Although treatments and vaccines to protect against COVID-19 are now available, people continue being hospitalized and dying due to COVID-19 infections. Real-time surveillance of population-level infections, hospitalizations, and deaths has helped public health officials better allocate healthcare resources and deploy mitigation strategies. However, producing reliable, real-time, short-term disease activity forecasts (one or two weeks into the future) remains a practical challenge. The recent emergence of robust time-series forecasting methodologies based on deep learning approaches has led to clear improvements in multiple research fields. We propose a recurrent neural network model named Fine-Grained Infection Forecast Network (FIGI-Net), which utilizes a stacked bidirectional LSTM structure designed to leverage fine-grained county-level data, to produce daily forecasts of COVID-19 infection trends up to two weeks in advance. We show that FIGI-Net improves existing COVID-19 forecasting approaches and delivers accurate county-level COVID-19 disease estimates. Specifically, FIGI-Net is capable of anticipating upcoming sudden changes in disease trends such as the onset of a new outbreak or the peak of an ongoing outbreak, a skill that multiple existing state-of-the-art models fail to achieve. This improved performance is observed across locations and periods. Our enhanced forecasting methodologies may help protect human populations against future disease outbreaks.
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Affiliation(s)
- Tzu-Hsi Song
- Vascular Biology Program and Department of Surgery, Boston Children’s Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Leonardo Clemente
- Department of Physics and Department of Electrical and Computer Engineering, Northeastern University, Boston, MA 02115, USA
| | - Xiang Pan
- Vascular Biology Program and Department of Surgery, Boston Children’s Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Junbong Jang
- Vascular Biology Program and Department of Surgery, Boston Children’s Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Mauricio Santillana
- Department of Physics and Department of Electrical and Computer Engineering, Northeastern University, Boston, MA 02115, USA
| | - Kwonmoo Lee
- Vascular Biology Program and Department of Surgery, Boston Children’s Hospital, Harvard Medical School, Boston, MA 02115, USA
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Marinda E, Mathentamo Q, Coulson N, Parker S, Dmc Katoto P, Houston G, Magampa M, Pillay N, Ngungu M, Wiysonge CS, van Rooyen H. Impact evaluation of a youth led intervention to increase COVID-19 vaccine uptake in Kwazulu-Natal, South Africa. Vaccine 2024; 42:2089-2098. [PMID: 38423816 DOI: 10.1016/j.vaccine.2024.02.052] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2023] [Revised: 02/14/2024] [Accepted: 02/17/2024] [Indexed: 03/02/2024]
Abstract
BACKGROUND COVID-19 vaccination rates in South Africa remain low at 51% of the adult population being fully vaccinated, defined as having two shorts of the COVID-19 vaccine with or without a booster. To improve vaccine uptake, a community-based intervention was tested in a high vaccine hesitancy community in South Africa. Trained community youths used social media, face to face interactions, door to door and neighbourhood outreach activities to deliver the intervention. METHODS To assess if the intervention had an impact, data was collected before the intervention and after the intervention in two districts, Wentworth an intervention site and Newlands East a control site. Both districts are in KwaZulu Natal Province, South Africa. The following outcomes, changes on perceptions and knowledge about COVID-19, intention to get vaccinated for those who were not fully vaccinated and vaccination uptake were assessed using difference-in-difference methods applied through Augmented Inverse-Probability Weighting and contrasts of Potential Outcome Means (POM). RESULTS One thousand, one hundred and fifty (1 150) participants agreed to take part in the study at baseline, and 916 (80%) were followed up after the 9-week intervention period. Intention to get vaccinated for COVID-19 was higher (difference-in-difference, DID 20%, 95% CI 6% - 35% higher), more people were fully vaccinated (DID 10%, 95% CI 0% - 20%) or partially vaccinated (DID 16%, 95% CI 6% - 26%) in Wentworth the intervention site compared to Newlands East, the control site. There were noticeable increases on the percentage of study participants indicating trust on the Government's COVID 19 programme, from 24% at baseline to 48% after the intervention in the intervention group than in the control group, 26% baseline and 29% at follow-up. There was a 10% (absolute) increase on the percentage of participants' saying they believed health care workers provided reliable information, 58% at baseline and 68% at follow-up in the intervention group, but there was little change in the control group 56% and 57% for baseline and follow-up respectively. CONCLUSION The youth-led intervention implemented in Wentworth, a community with a high rate of vaccine hesitancy, was effective in increasing vaccination uptake. Given the low COVID-19 vaccine coverage in South Africa and across the African region, as well as the new emerging variant of concern (XBB 1.5), there is an urgent need to scale up such intervention at the community level to address persistent misinformation and promote vaccine equality.
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Affiliation(s)
- Edmore Marinda
- Human Sciences Research Council, Pretoria, South Africa; School of Public Health, University of the Witwatersrand, Johannesburg 2050, South Africa.
| | | | - Nancy Coulson
- The Sarraounia Public Health Trust, Johannesburg 2193, South Africa; Wits Mining Institute, University of the Witwatersrand, Johannesburg 2050, South Africa
| | | | - Patrick Dmc Katoto
- Cochrane South Africa, South African Medical Research Council, Cape Town 7501, South Africa; Department of Global Health, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town 7505, South Africa; Centre for General Medicine and Global Health, Department of Medicine, University of Cape Town, Cape Town 7505, South Africa
| | | | | | - Nirvana Pillay
- School of Public Health, University of the Witwatersrand, Johannesburg 2050, South Africa; The Sarraounia Public Health Trust, Johannesburg 2193, South Africa
| | - Mercy Ngungu
- Human Sciences Research Council, Pretoria, South Africa
| | - Charles S Wiysonge
- Cochrane South Africa, South African Medical Research Council, Cape Town 7501, South Africa; HIV and Other Infectious Diseases Research Unit, South African Medical Research Council, Durban, 4001, South Africa
| | - Heidi van Rooyen
- Human Sciences Research Council, Pretoria, South Africa; SAMRC-Wits Developmental Pathways for Health Research Unit, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg 2050, South Africa
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Li J, Jia K, Zhao W, Yuan B, Liu Y. Natural and socio-environmental factors contribute to the transmissibility of COVID-19: evidence from an improved SEIR model. INTERNATIONAL JOURNAL OF BIOMETEOROLOGY 2023; 67:1789-1802. [PMID: 37561207 DOI: 10.1007/s00484-023-02539-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/06/2021] [Revised: 06/28/2023] [Accepted: 08/01/2023] [Indexed: 08/11/2023]
Abstract
COVID-19 has ravaged Brazil, and its spread showed spatial heterogeneity. Changes in the environment have been implicated as potential factors involved in COVID-19 transmission. However, considerable research efforts have not elucidated the risk of environmental factors on COVID-19 transmission from the perspective of infectious disease dynamics. The aim of this study is to model the influence of the environment on COVID-19 transmission and to analyze how the socio-ecological factors affecting the probability of virus transmission in 10 states dramatically shifted during the early stages of the epidemic in Brazil. First, this study used a Pearson correlation to analyze the interconnection between COVID-19 morbidity and socio-ecological factors and identified factors with significant correlations as the dominant factors affecting COVID-19 transmission. Then, the time-lag effect of dominant factors on the morbidity of COVID-19 was investigated by constructing a distributed lag nonlinear model and standard two-stage meta-analytic model, and the results were considered in the improved SEIR model. Lastly, a machine learning method was introduced to explore the nonlinear relationship between the environmental propagation probability and socio-ecological factors. By analyzing the impact of environmental factors on virus transmission, it can be found that population mobility directly caused by human activities had a greater impact on virus transmission than temperature and humidity. The heterogeneity of meteorological factors can be accounted for by the diverse climate patterns in Brazil. The improved SEIR model was adopted to explore the interconnection of COVID-19 transmission and the environment, which revealed a new strategy to probe the causal links between them.
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Affiliation(s)
- Jie Li
- State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing, 100875, China
| | - Kun Jia
- State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing, 100875, China.
| | - Wenwu Zhao
- Stake Key Laboratory of Earth Surface Processes and Resource Ecology, Faculty of Geographical Science, Beijing Normal University, Beijing, 100875, China
- Institute of Land Surface System and Sustainable Development, Faculty of Geographical Science, Beijing Normal University, Beijing, 100875, China
| | - Bo Yuan
- State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing, 100875, China
| | - Yanxu Liu
- Stake Key Laboratory of Earth Surface Processes and Resource Ecology, Faculty of Geographical Science, Beijing Normal University, Beijing, 100875, China
- Institute of Land Surface System and Sustainable Development, Faculty of Geographical Science, Beijing Normal University, Beijing, 100875, China
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Narasimhan G, Victor A. Analysis of computational intelligence approaches for predicting disease severity in humans: Challenges and research guidelines. JOURNAL OF EDUCATION AND HEALTH PROMOTION 2023; 12:334. [PMID: 38023081 PMCID: PMC10671019 DOI: 10.4103/jehp.jehp_298_23] [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: 03/02/2023] [Accepted: 04/12/2023] [Indexed: 12/01/2023]
Abstract
The word disease is a common word and there are many diseases like heart disease, diabetes, breast cancer, COVID-19, and kidney disease that threaten humans. Data-mining methods are proving to be increasingly beneficial in the present day, especially in the field of medical applications; through the use of machine-learning methods, that are used to extract valuable information from healthcare data, which can then be used to predict and treat diseases early, reducing the risk of human life. Machine-learning techniques are useful especially in the field of health care in extracting information from healthcare data. These data are very much helpful in predicting the disease early and treating the patients to reduce the risk of human life. For classification and decision-making, data mining is very much suitable. In this paper, a comprehensive study on several diseases and diverse machine-learning approaches that are functional to predict those diseases and also the different datasets used in prediction and making decisions are discussed in detail. The drawbacks of the models from various research papers have been observed and reveal countless computational intelligence approaches. Naïve Bayes, logistic regression (LR), SVM, and random forest are able to produce the best accuracy. With further optimization algorithms like genetic algorithm, particle swarm optimization, and ant colony optimization combined with machine learning, better performance can be achieved in terms of accuracy, specificity, precision, recall, and specificity.
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Affiliation(s)
- Geetha Narasimhan
- School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India
| | - Akila Victor
- School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India
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Zaeri N. Artificial intelligence and machine learning responses to COVID-19 related inquiries. J Med Eng Technol 2023; 47:301-320. [PMID: 38625639 DOI: 10.1080/03091902.2024.2321846] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2021] [Accepted: 02/18/2024] [Indexed: 04/17/2024]
Abstract
Researchers and scientists can use computational-based models to turn linked data into useful information, aiding in disease diagnosis, examination, and viral containment due to recent artificial intelligence and machine learning breakthroughs. In this paper, we extensively study the role of artificial intelligence and machine learning in delivering efficient responses to the COVID-19 pandemic almost four years after its start. In this regard, we examine a large number of critical studies conducted by various academic and research communities from multiple disciplines, as well as practical implementations of artificial intelligence algorithms that suggest potential solutions in investigating different COVID-19 decision-making scenarios. We identify numerous areas where artificial intelligence and machine learning can impact this context, including diagnosis (using chest X-ray imaging and CT imaging), severity, tracking, treatment, and the drug industry. Furthermore, we analyse the dilemma's limits, restrictions, and hazards.
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Affiliation(s)
- Naser Zaeri
- Faculty of Computer Studies, Arab Open University, Kuwait
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Tariq MU, Ismail SB, Babar M, Ahmad A. Harnessing the power of AI: Advanced deep learning models optimization for accurate SARS-CoV-2 forecasting. PLoS One 2023; 18:e0287755. [PMID: 37471397 PMCID: PMC10359009 DOI: 10.1371/journal.pone.0287755] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2023] [Accepted: 06/09/2023] [Indexed: 07/22/2023] Open
Abstract
The pandemic has significantly affected many countries including the USA, UK, Asia, the Middle East and Africa region, and many other countries. Similarly, it has substantially affected Malaysia, making it crucial to develop efficient and precise forecasting tools for guiding public health policies and approaches. Our study is based on advanced deep-learning models to predict the SARS-CoV-2 cases. We evaluate the performance of Long Short-Term Memory (LSTM), Bi-directional LSTM, Convolutional Neural Networks (CNN), CNN-LSTM, Multilayer Perceptron, Gated Recurrent Unit (GRU), and Recurrent Neural Networks (RNN). We trained these models and assessed them using a detailed dataset of confirmed cases, demographic data, and pertinent socio-economic factors. Our research aims to determine the most reliable and accurate model for forecasting SARS-CoV-2 cases in the region. We were able to test and optimize deep learning models to predict cases, with each model displaying diverse levels of accuracy and precision. A comprehensive evaluation of the models' performance discloses the most appropriate architecture for Malaysia's specific situation. This study supports ongoing efforts to combat the pandemic by offering valuable insights into the application of sophisticated deep-learning models for precise and timely SARS-CoV-2 case predictions. The findings hold considerable implications for public health decision-making, empowering authorities to create targeted and data-driven interventions to limit the virus's spread and minimize its effects on Malaysia's population.
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Affiliation(s)
- Muhammad Usman Tariq
- Abu Dhabi University, Abu Dhabi, United Arab Emirates
- Universiti Tun Hussein Onn Malaysia (UTHM), Parit Raja, Malaysia
| | | | - Muhammad Babar
- Robotics and Internet of Things Lab, Prince Sultan University, Riyadh, Saudi Arabia
| | - Ashir Ahmad
- College of Computer and Information Sciences, Prince Sultan University, Riyadh, Saudi Arabia
- Swinburne University of Technology, Melbourne, Australia
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Butt MJ, Malik AK, Qamar N, Yar S, Malik AJ, Rauf U. A Survey on COVID-19 Data Analysis Using AI, IoT, and Social Media. SENSORS (BASEL, SWITZERLAND) 2023; 23:5543. [PMID: 37420714 DOI: 10.3390/s23125543] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/30/2023] [Revised: 06/04/2023] [Accepted: 06/07/2023] [Indexed: 07/09/2023]
Abstract
Coronaviruses are a well-established and deadly group of viruses that cause illness in both humans and animals. The novel type of this virus group, named COVID-19, was firstly reported in December 2019, and, with the passage of time, coronavirus has spread to almost all parts of the world. Coronavirus has been the cause of millions of deaths around the world. Furthermore, many countries are struggling with COVID-19 and have experimented with various kinds of vaccines to eliminate the deadly virus and its variants. This survey deals with COVID-19 data analysis and its impact on human social life. Data analysis and information related to coronavirus can greatly help scientists and governments in controlling the spread and symptoms of the deadly coronavirus. In this survey, we cover many areas of discussion related to COVID-19 data analysis, such as how artificial intelligence, along with machine learning, deep learning, and IoT, have worked together to fight against COVID-19. We also discuss artificial intelligence and IoT techniques used to forecast, detect, and diagnose patients of the novel coronavirus. Moreover, this survey also describes how fake news, doctored results, and conspiracy theories were spread over social media sites, such as Twitter, by applying various social network analysis and sentimental analysis techniques. A comprehensive comparative analysis of existing techniques has also been conducted. In the end, the Discussion section presents different data analysis techniques, provides future directions for research, and suggests general guidelines for handling coronavirus, as well as changing work and life conditions.
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Affiliation(s)
- Muhammad Junaid Butt
- Department of Computer Science, COMSATS University Islamabad (CUI), Islamabad 45550, Pakistan
| | - Ahmad Kamran Malik
- Department of Computer Science, COMSATS University Islamabad (CUI), Islamabad 45550, Pakistan
| | - Nafees Qamar
- School of Health and Behavioral Sciences, Bryant University, Smithfield, RI 02917, USA
| | - Samad Yar
- Department of Computer Science, COMSATS University Islamabad (CUI), Islamabad 45550, Pakistan
| | - Arif Jamal Malik
- Department of Software Engineering, Foundation University, Islamabad 44000, Pakistan
| | - Usman Rauf
- Department of Mathematics and Computer Science, Mercy College, Dobbs Ferry, NY 10522, USA
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Koichubekov B, Takuadina A, Korshukov I, Turmukhambetova A, Sorokina M. Is It Possible to Predict COVID-19? Stochastic System Dynamic Model of Infection Spread in Kazakhstan. Healthcare (Basel) 2023; 11:752. [PMID: 36900757 PMCID: PMC10000940 DOI: 10.3390/healthcare11050752] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2023] [Revised: 02/21/2023] [Accepted: 02/26/2023] [Indexed: 03/08/2023] Open
Abstract
BACKGROUND Since the start of the COVID-19 pandemic, scientists have begun to actively use models to determine the epidemiological characteristics of the pathogen. The transmission rate, recovery rate and loss of immunity to the COVID-19 virus change over time and depend on many factors, such as the seasonality of pneumonia, mobility, testing frequency, the use of masks, the weather, social behavior, stress, public health measures, etc. Therefore, the aim of our study was to predict COVID-19 using a stochastic model based on the system dynamics approach. METHOD We developed a modified SIR model in AnyLogic software. The key stochastic component of the model is the transmission rate, which we consider as an implementation of Gaussian random walks with unknown variance, which was learned from real data. RESULTS The real data of total cases turned out to be outside the predicted minimum-maximum interval. The minimum predicted values of total cases were closest to the real data. Thus, the stochastic model we propose gives satisfactory results for predicting COVID-19 from 25 to 100 days. The information we currently have about this infection does not allow us to make predictions with high accuracy in the medium and long term. CONCLUSIONS In our opinion, the problem of the long-term forecasting of COVID-19 is associated with the absence of any educated guess regarding the dynamics of β(t) in the future. The proposed model requires improvement with the elimination of limitations and the inclusion of more stochastic parameters.
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Affiliation(s)
- Berik Koichubekov
- Department of Informatics and Biostatistics, Karaganda Medical University, Gogol St. 40, Karaganda 100008, Kazakhstan
| | - Aliya Takuadina
- Department of Informatics and Biostatistics, Karaganda Medical University, Gogol St. 40, Karaganda 100008, Kazakhstan
| | - Ilya Korshukov
- Department of Informatics and Biostatistics, Karaganda Medical University, Gogol St. 40, Karaganda 100008, Kazakhstan
| | - Anar Turmukhambetova
- Institute of Life Sciences, Karaganda Medical University, Gogol St. 40, Karaganda 100008, Kazakhstan
| | - Marina Sorokina
- Department of Informatics and Biostatistics, Karaganda Medical University, Gogol St. 40, Karaganda 100008, Kazakhstan
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Barnawi A, Boulares M, Somai R. Simple and Powerful PCG Classification Method Based on Selection and Transfer Learning for Precision Medicine Application. Bioengineering (Basel) 2023; 10:bioengineering10030294. [PMID: 36978685 PMCID: PMC10045405 DOI: 10.3390/bioengineering10030294] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2023] [Revised: 02/03/2023] [Accepted: 02/15/2023] [Indexed: 03/03/2023] Open
Abstract
The World Health Organization (WHO) highlights that cardiovascular diseases (CVDs) are one of the leading causes of death globally, with an estimated rise to over 23.6 million deaths by 2030. This alarming trend can be attributed to our unhealthy lifestyles and lack of attention towards early CVD diagnosis. Traditional cardiac auscultation, where a highly qualified cardiologist listens to the heart sounds, is a crucial diagnostic method, but not always feasible or affordable. Therefore, developing accessible and user-friendly CVD recognition solutions can encourage individuals to integrate regular heart screenings into their routine. Although many automatic CVD screening methods have been proposed, most of them rely on complex prepocessing steps and heart cycle segmentation processes. In this work, we introduce a simple and efficient approach for recognizing normal and abnormal PCG signals using Physionet data. We employ data selection techniques such as kernel density estimation (KDE) for signal duration extraction, signal-to-noise Ratio (SNR), and GMM clustering to improve the performance of 17 pretrained Keras CNN models. Our results indicate that using KDE to select the appropriate signal duration and fine-tuning the VGG19 model results in excellent classification performance with an overall accuracy of 0.97, sensitivity of 0.946, precision of 0.944, and specificity of 0.946.
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Affiliation(s)
- Ahmed Barnawi
- Information Technology Department, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia
- Correspondence:
| | - Mehrez Boulares
- Information Technology Department, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia
- Research Laboratory of Technologies of Information and Communication and Electrical Engineering (LaTICE), Higher National School of Engineers of Tunis (ENSIT), University of Tunis, Tunis 1008, Tunisia
| | - Rim Somai
- ESPRIT School of Engineering, Tunis 2035, Tunisia
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Improved autoregressive integrated moving average model for COVID-19 prediction by using statistical significance and clustering techniques. Heliyon 2023; 9:e13483. [PMID: 36776910 PMCID: PMC9896886 DOI: 10.1016/j.heliyon.2023.e13483] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2022] [Revised: 01/28/2023] [Accepted: 01/31/2023] [Indexed: 02/05/2023] Open
Abstract
Purpose The COVID-19 pandemic has affected more than 192 countries. The condition results in a respiratory illness (e.g., influenza) with signs and symptoms such as cold, cough, fever, and breathing difficulties. Predicting new instances of COVID-19 is always a challenging task. Methods This study improved the autoregressive integrated moving average (ARIMA)-based time series prediction model by incorporating statistical significance for feature selection and k-means clustering for outlier detection. The accuracy of the improved model (ARIMAI) was examined using World Health Organization's official data on the COVID-19 pandemic worldwide and compared with that of many modern, cutting-edge algorithms. Results The ARIMAI model (RSS score = 0.279, accuracy = 97.75%) outperformed the current ARIMA model (RSS score = 0.659, accuracy = 93%). Conclusions The ARIMAI model is not only an efficient but also a rapid and simple technique to forecast COVID-19 trends. The usage of this model enables the prediction of any disease that will affect patients in the future pandemics.
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14
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Mohammadi F, Dehbozorgi L, Akbari‐Hasanjani HR, Joz Abbasalian Z, Akbari‐Hasanjani R, Sabbaghi‐Nadooshan R, Moradi Tabriz H. Evaluation of effective features in the diagnosis of Covid-19 infection from routine blood tests with multilayer perceptron neural network: A cross-sectional study. Health Sci Rep 2023; 6:e1048. [PMID: 36620509 PMCID: PMC9817491 DOI: 10.1002/hsr2.1048] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2022] [Revised: 12/04/2022] [Accepted: 12/27/2022] [Indexed: 01/09/2023] Open
Abstract
Background and Aim Coronavirus is an infectious disease that is now known as an epidemic, early and accurate diagnosis helps the patient receive more care. The aim of this study is to investigate Covid-19 using blood tests and multilayer perceptron neural network and affective factors in improving and preventing Covid-19. Methods This cross-sectional study was performed on 200 patients referred to Sina Hospital, Tehran, Iran, who were confirmed cases of Covid-19 by computerized tomography-scan analysis between 2 March 2020 to 5 April 2020. After verification of lung involvement, blood sampling was done to separate the sera for C-reactive protein (CRP), magnesium (Mg), lymphocyte percentage, and vitamin D analysis in healthy and unhealthy people. Blood samples from healthy and sick people were applied to the multilayer perceptron network for 70% of the data for training and 30% for testing. Result By examining the features, it was found that in patients with Covid-19, there was a significant relationship between increased CRP and decreased lymphocyte levels, and increased Mg (p < 0.01). In these patients, the amount of CRP and Mg in women and the number of lymphocytes and vitamin D in men were significantly higher (p < 0.01). Conclusion The important advantage of using a multilayer perceptron neural network is to speed up the diagnosis and treatment.
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Affiliation(s)
- Fatemeh Mohammadi
- Department of Pathology, Sina Clinical‐Research CenterTehran University of Medical SciencesTehranIran
| | - Leila Dehbozorgi
- Department of Electrical Engineering, Central Tehran BranchIslamic Azad UniversityTehranIran
| | | | - Zahra Joz Abbasalian
- Department of Pathology, Sina Clinical‐Research CenterTehran University of Medical SciencesTehranIran
| | - Reza Akbari‐Hasanjani
- Department of Electrical Engineering, Central Tehran BranchIslamic Azad UniversityTehranIran
| | - Reza Sabbaghi‐Nadooshan
- Department of Electrical Engineering, Central Tehran BranchIslamic Azad UniversityTehranIran
| | - Hedieh Moradi Tabriz
- Department of Pathology, Sina Clinical‐Research CenterTehran University of Medical SciencesTehranIran
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15
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Rane R, Dubey A, Rasool A, Wadhvani R. Data Mining Based Techniques for Covid-19 Predictions. PROCEDIA COMPUTER SCIENCE 2023; 218:210-219. [PMID: 36743794 PMCID: PMC9886325 DOI: 10.1016/j.procs.2023.01.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
COVID-19 is a pandemic that has resulted in numerous fatalities and infections in recent years, with a rising tendency in both the number of infections and deaths and the pace of recovery. Accurate forecasting models are important for making accurate forecasts and taking relevant actions. As a result, accurate short-term forecasting of the number of new cases that are contaminated and recovered is essential for making the best use of the resources at hand and stopping or delaying the spread of such illnesses. This paper shows the various techniques for forecasting the covid-19 cases. This paper classifies the various models according to their category and shows the merits and demerits of various fore-casting techniques. The research provides insight into potential issues that may arise during the forecasting of covid-19 instances for predicting the positive, negative, and death cases in this pandemic. In this paper, numerous forecasting techniques and their categories have been studied. The goal of this work is to aggregate the findings of several forecasting techniques to aid in the fight against the pandemic.
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Affiliation(s)
- Rahul Rane
- Department of Computer Science and Engineering, Maulana Azad National Institute of Technology, Bhopal and 462003, India
| | - Aditya Dubey
- Department of Computer Science and Engineering, Maulana Azad National Institute of Technology, Bhopal and 462003, India
| | - Akhtar Rasool
- Department of Computer Science and Engineering, Maulana Azad National Institute of Technology, Bhopal and 462003, India
| | - Rajesh Wadhvani
- Department of Computer Science and Engineering, Maulana Azad National Institute of Technology, Bhopal and 462003, India
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16
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Namasudra S, Dhamodharavadhani S, Rathipriya R. Nonlinear Neural Network Based Forecasting Model for Predicting COVID-19 Cases. Neural Process Lett 2023; 55:171-191. [PMID: 33821142 PMCID: PMC8012519 DOI: 10.1007/s11063-021-10495-w] [Citation(s) in RCA: 28] [Impact Index Per Article: 28.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/12/2021] [Indexed: 02/07/2023]
Abstract
The recent COVID-19 outbreak has severely affected people around the world. There is a need of an efficient decision making tool to improve awareness about the spread of COVID-19 infections among the common public. An accurate and reliable neural network based tool for predicting confirmed, recovered and death cases of COVID-19 can be very helpful to the health consultants for taking appropriate actions to control the outbreak. This paper proposes a novel Nonlinear Autoregressive (NAR) Neural Network Time Series (NAR-NNTS) model for forecasting COVID-19 cases. This NAR-NNTS model is trained with Scaled Conjugate Gradient (SCG), Levenberg Marquardt (LM) and Bayesian Regularization (BR) training algorithms. The performance of the proposed model has been compared by using Root Mean Square Error (RMSE), Mean Square Error (MSE) and correlation co-efficient i.e. R-value. The results show that NAR-NNTS model trained with LM training algorithm performs better than other models for COVID-19 epidemiological data prediction.
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Affiliation(s)
- Suyel Namasudra
- Department of Computer Science and Engineering, National Institute of Technology Patna, Bihar, India
| | | | - R Rathipriya
- Department of Computer Science, Periyar University, Salem, India
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17
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Safaie N, Kaveie M, Mardanian S, Mohammadi M, Abdol Mohamadi R, Nasri SA. Investigation of Factors Affecting COVID-19 and Sixth Wave Management Using a System Dynamics Approach. JOURNAL OF HEALTHCARE ENGINEERING 2022; 2022:4079685. [PMID: 36471726 PMCID: PMC9719431 DOI: 10.1155/2022/4079685] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/16/2022] [Revised: 07/12/2022] [Accepted: 07/29/2022] [Indexed: 11/04/2023]
Abstract
The COVID-19 pandemic has plunged the world into a health and economic crisis never seen before since the Spanish flu pandemic in 1918. The closure of schools and universities, the banning of rallies, and other social distancing in countries have been done to disrupt the transmission of the virus. Governments have planned to reduce restrictions on corona management by implementing vaccination programs. This research aims to better understand the Coronavirus disease's behavior, identify the prevalent factors, and adopt effective policies to control the pandemic. This study examines the different scenarios of releasing the constraints and returning to normal conditions before Corona to analyze the results of different scenarios to prevent the occurrence of subsequent peaks. The system dynamics approach is an effective means of studying COVID-19's behavioral characteristics. The factors that affect Coronavirus disease outbreak and control by expanding the basic SEIR model, interventions, and policies, such as vaccination, were investigated in this research. Based on the obtained results, the most critical factor in reducing the prevalence of the disease is reducing the behavioral risks of people and increasing the vaccination process. Observance of hygienic principles leads to disruption of the transmission chain, and vaccination increases the immunity of individuals against the acute type of infection. In addition, the closure of businesses and educational centers, along with government support for incomes, effectively controls and reduces the pandemic, which requires cooperation between the people and the government. In a situation where a new type of corona has spread, if the implementation of the policy of reducing restrictions and reopening schools and universities is done without planning, it will cause a lot of people to suffer.
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Affiliation(s)
- Nasser Safaie
- Department of Industrial Engineering, K. N. Toosi University of Technology, Tehran, Iran
| | - Maryam Kaveie
- Department of Industrial Engineering, K. N. Toosi University of Technology, Tehran, Iran
| | - Siroos Mardanian
- Department of Industrial Engineering, K. N. Toosi University of Technology, Tehran, Iran
| | - Mina Mohammadi
- Department of Industrial Engineering, K. N. Toosi University of Technology, Tehran, Iran
| | - Rasoul Abdol Mohamadi
- Department of Industrial Engineering, K. N. Toosi University of Technology, Tehran, Iran
| | - Seyed Amir Nasri
- Department of Industrial Engineering, K. N. Toosi University of Technology, Tehran, Iran
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18
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Distributed lag inspired machine learning for predicting vaccine-induced changes in COVID-19 hospitalization and intensive care unit admission. Sci Rep 2022; 12:18748. [PMID: 36335113 PMCID: PMC9637108 DOI: 10.1038/s41598-022-21969-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2022] [Accepted: 10/05/2022] [Indexed: 11/08/2022] Open
Abstract
Distributed lags play important roles in explaining the short-run dynamic and long-run cumulative effects of features on a response variable. Unlike the usual lag length selection, important lags with significant weights are selected in a distributed lag model (DLM). Inspired by the importance of distributed lags, this research focuses on the construction of distributed lag inspired machine learning (DLIML) for predicting vaccine-induced changes in COVID-19 hospitalization and intensive care unit (ICU) admission rates. Importance of a lagged feature in DLM is examined by hypothesis testing and a subset of important features are selected by evaluating an information criterion. Akin to the DLM, we demonstrate the selection of distributed lags in machine learning by evaluating importance scores and objective functions. Finally, we apply the DLIML with supervised learning for forecasting daily changes in COVID-19 hospitalization and ICU admission rates in United Kingdom (UK) and United States of America (USA). A sharp decline in hospitalization and ICU admission rates are observed when around 40% people are vaccinated. For one percent more vaccination, daily changes in hospitalization and ICU admission rates are expected to reduce by 4.05 and 0.74 per million after 14 days in UK, and 5.98 and 1.04 per million after 20 days in USA, respectively. Long-run cumulative effects in the DLM demonstrate that the daily changes in hospitalization and ICU admission rates are expected to jitter around the zero line in a long-run. Application of the DLIML selects fewer lagged features but provides qualitatively better forecasting outcome for data-driven healthcare service planning.
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19
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Deep learning for Covid-19 forecasting: State-of-the-art review. Neurocomputing 2022; 511:142-154. [PMID: 36097509 PMCID: PMC9454152 DOI: 10.1016/j.neucom.2022.09.005] [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: 04/11/2022] [Revised: 07/03/2022] [Accepted: 09/04/2022] [Indexed: 11/21/2022]
Abstract
The Covid-19 pandemic has galvanized scientists to apply machine learning methods to help combat the crisis. Despite the significant amount of research there exists no comprehensive survey devoted specifically to examining deep learning methods for Covid-19 forecasting. In this paper, we fill the gap in the literature by reviewing and analyzing the current studies that use deep learning for Covid-19 forecasting. In our review, all published papers and preprints, discoverable through Google Scholar, for the period from Apr 1, 2020 to Feb 20, 2022 which describe deep learning approaches to forecasting Covid-19 were considered. Our search identified 152 studies, of which 53 passed the initial quality screening and were included in our survey. We propose a model-based taxonomy to categorize the literature. We describe each model and highlight its performance. Finally, the deficiencies of the existing approaches are identified and the necessary improvements for future research are elucidated. The study provides a gateway for researchers who are interested in forecasting Covid-19 using deep learning.
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20
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Atchadé MN, Tchanati P. P. On computational analysis of nonlinear regression models addressing heteroscedasticity and autocorrelation issues: An application to COVID-19 data. Heliyon 2022; 8:e11057. [PMID: 36254279 PMCID: PMC9568860 DOI: 10.1016/j.heliyon.2022.e11057] [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: 04/05/2022] [Revised: 07/29/2022] [Accepted: 10/07/2022] [Indexed: 11/06/2022] Open
Abstract
This paper develops a method for nonlinear regression models estimation that is robust to heteroscedasticity and autocorrelation of errors. Using nonlinear least squares estimation, four popular growth models (Exponential, Gompertz, Verhulst, and Weibull) were computed. Some assumptions on the errors of these models (independence, normality, and homoscedasticity) being violated, the estimates are improved by modeling the residuals using the ETS method. For an application purpose, this approach has been used to predict the daily cumulative number of novel coronavirus (COVID-19) cases in Africa for the study period, from March 13, 2020, to June 26, 2021. The comparison of the proposed model to the competitors was done using statistical metrics such as MAPE, MAE, RMSE, AIC, BIC, and AICc. The findings revealed that the modified Gompertz model is the most accurate in forecasting the total number of COVID-19 cases in Africa. Moreover, the developed approach will be useful for researchers and policymakers for predicting purpose and for better decision making in different fields of its applications.
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Affiliation(s)
- Mintodê Nicodème Atchadé
- National Higher School of Mathematics Genius and Modelization, National University of Sciences, Technologies, Engineering and Mathematics, Abomey, Benin,University of Abomey-Calavi/International Chair in Mathematical Physics and Applications (ICMPA: UNESCO-Chair), 072 BP 50 Cotonou, Benin,Corresponding author at: National Higher School of Mathematics Genius and Modelization, National University of Sciences, Technologies, Engineering and Mathematics, Abomey, Benin.
| | - Paul Tchanati P.
- National Higher School of Mathematics Genius and Modelization, National University of Sciences, Technologies, Engineering and Mathematics, Abomey, Benin
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21
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Sardar I, Akbar MA, Leiva V, Alsanad A, Mishra P. Machine learning and automatic ARIMA/Prophet models-based forecasting of COVID-19: methodology, evaluation, and case study in SAARC countries. STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT : RESEARCH JOURNAL 2022; 37:345-359. [PMID: 36217358 PMCID: PMC9533996 DOI: 10.1007/s00477-022-02307-x] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Accepted: 08/26/2022] [Indexed: 06/16/2023]
Abstract
Machine learning (ML) has proved to be a prominent study field while solving complex real-world problems. The whole globe has suffered and continues suffering from Coronavirus disease 2019 (COVID-19), and its projections need to be forecasted. In this article, we propose and derive an autoregressive modeling framework based on ML and statistical methods to predict confirmed cases of COVID-19 in the South Asian Association for Regional Cooperation (SAARC) countries. Automatic forecasting models based on autoregressive integrated moving average (ARIMA) and Prophet time series structures, as well as extreme gradient boosting, generalized linear model elastic net (GLMNet), and random forest ML techniques, are introduced and applied to COVID-19 data from the SAARC countries. Different forecasting models are compared by means of selection criteria. By using evaluation metrics, the best and suitable models are selected. Results prove that the ARIMA model is found to be suitable and ideal for forecasting confirmed infected cases of COVID-19 in these countries. For the confirmed cases in Afghanistan, Bangladesh, India, Maldives, and Sri Lanka, the ARIMA model is superior to the other models. In Bhutan, the Prophet time series model is appropriate for predicting such cases. The GLMNet model is more accurate than other time-series models for Nepal and Pakistan. The random forest model is excluded from forecasting because of its poor fit.
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Affiliation(s)
- Iqra Sardar
- Department of Mathematics and Statistics, International Islamic University Islamabad, Islamabad, Pakistan
| | | | - Víctor Leiva
- School of Industrial Engineering, Pontificia Universidad Católica de Valparaíso, Valparaíso, Chile
| | - Ahmed Alsanad
- STC’s Artificial Intelligence Chair, Department of Information Systems, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi Arabia
| | - Pradeep Mishra
- Department of Statistics, College of Agriculture, Powarkheda, India
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22
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Jain P, Sahu S. Prediction and forecasting of worldwide corona virus (COVID-19) outbreak using time series and machine learning. CONCURRENCY AND COMPUTATION : PRACTICE & EXPERIENCE 2022; 34:e7286. [PMID: 36247093 PMCID: PMC9539277 DOI: 10.1002/cpe.7286] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/24/2021] [Revised: 06/07/2022] [Accepted: 07/15/2022] [Indexed: 06/16/2023]
Abstract
How will the newly discovered coronavirus (COVID-19) affect the world and what will be its global impact? For answering this question, we will require a prediction of overall recoveries and fatalities, as well as a reliable prognosis of coronavirus cases. Predicting, however, requires an ample total of past data related to it. On any particular day, the prediction is unclear since events in the future rarely repeat themselves the way that they did in the past. Furthermore, forecasts and predictions are determined by the absolute interests, accuracy of the data, and prophesied variables. In addition, psychological factors play an enormous role in how people perceive and react to the danger from the disease and therefore the fear that it is going to affect them personally. This research paper advances an unbiased method for predicting the increase of the COVID-19 employing a simple, but powerful method to do so. Assumed that the data are accurate and reliable which the longer term will still follow an equivalent disease pattern, our projections intimate with a large association. Within the COVID-19 cases were documented, in contingency, there is a steady increase. The hazards are far away from symmetric, as underestimating a pandemic's spread and failing to do enough to prevent it is far a lot worse than overspending and being too cautious when it will not be needed. This paper illustrates the timeline of a live forecasting study with huge implied implications for devising and decision-making and gives unbiased predictions on COVID-19 confirmed cases, recovered cases, deaths, and ongoing cases are shown on a continental map using data science and machine learning (ML) approaches. Utilizing these ML-based techniques, the proposed system predicts the accurate COVID-19 cases and gives better performance.
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Affiliation(s)
- Priyank Jain
- Indian Institute of Information TechnologyBhopalMadhya PradeshIndia
| | - Shriya Sahu
- Atal Bihari Vajpayee UniversityBilaspurChhattisgarhIndia
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23
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Coronavirus (COVID-19): ARIMA-based Time-series Analysis to Forecast near Future and the Effect of School Reopening in India. JOURNAL OF HEALTH MANAGEMENT 2022. [DOI: 10.1177/09720634221109087] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
COVID-19, a novel coronavirus, is currently a major worldwide threat. It has infected more than a million people globally leading to hundred-thousands of deaths. In such grave circumstances, it is very important to predict future scenario to support prevention and recurrence of the disease, aid in healthcare service preparation and help in decision making process. Following that notion, a model has been developed for forecasting future COVID-19 cases in India. The time series analysis indicates that the cases will keep on increasing in India in the coming month as the peak has not been attained until now. A statistical analysis based on the effect of reopening of schools has also been performed. It is revealed that there will be a minor increase in the active cases when pre-/primary schools are opened. The present prediction models will assist the government and medical personnel in gaining insight and planning for forthcoming conditions.
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24
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DISCOVID: discovering patterns of COVID-19 infection from recovered patients: a case study in Saudi Arabia. INTERNATIONAL JOURNAL OF INFORMATION TECHNOLOGY : AN OFFICIAL JOURNAL OF BHARATI VIDYAPEETH'S INSTITUTE OF COMPUTER APPLICATIONS AND MANAGEMENT 2022; 14:2825-2838. [PMID: 35812263 PMCID: PMC9251043 DOI: 10.1007/s41870-022-00973-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/05/2022] [Accepted: 04/25/2022] [Indexed: 12/13/2022]
Abstract
A respiratory syndrome COVID-19 pandemic has become a serious global concern. Still, a large number of people have been daily infected worldwide. Discovering COVID-19 infection patterns is significant for health providers towards understanding the infection factors. Current COVID-19 research works have not been attempted to discover the infection patterns, yet. In this paper, we employ an Association Rules Apriori (ARA) algorithm to discover the infection patterns from COVID-19 recovered patients’ data. A non-clinical COVID-19 dataset is introduced and analyzed. A sample of recovered patients’ data is manually collected in Saudi Arabia. Our manual computation and experimental results show strong associative rules with high confidence scores among males, weight above 70 kilograms, height above 160 centimeters, and fever patterns. These patterns are the strongest infection patterns discovered from COVID-19 recovered patients’ data.
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25
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Jin W, Dong S, Yu C, Luo Q. A data-driven hybrid ensemble AI model for COVID-19 infection forecast using multiple neural networks and reinforced learning. Comput Biol Med 2022; 146:105560. [PMID: 35551008 PMCID: PMC9042415 DOI: 10.1016/j.compbiomed.2022.105560] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2022] [Revised: 03/18/2022] [Accepted: 04/06/2022] [Indexed: 01/31/2023]
Abstract
The COVID-19 outbreak poses a huge challenge to international public health. Reliable forecast of the number of cases is of great significance to the planning of health resources and the investigation and evaluation of the epidemic situation. The data-driven machine learning models can adapt to complex changes in the epidemic situation without relying on correct physical dynamics modeling, which are sensitive and accurate in predicting the development of the epidemic. In this paper, an ensemble hybrid model based on Temporal Convolutional Networks (TCN), Gated Recurrent Unit (GRU), Deep Belief Networks (DBN), Q-learning, and Support Vector Machine (SVM) models, namely TCN-GRU-DBN-Q-SVM model, is proposed to achieve the forecasting of COVID-19 infections. Three widely-used predictors, TCN, GRU, and DBN are used as elements of the hybrid model ensembled by the weights provided by reinforcement learning method. Furthermore, an error predictor built by SVM, is trained with validation set, and the final prediction result could be obtained by combining the TCN-GRU-DBN-Q model with the SVM error predictor. In order to investigate the forecasting performance of the proposed hybrid model, several comparison models (TCN-GRU-DBN-Q, LSTM, N-BEATS, ANFIS, VMD-BP, WT-RVFL, and ARIMA models) are selected. The experimental results show that: (1) the prediction effect of the TCN-GRU-DBN-Q-SVM model on COVID-19 infection is satisfactory, which has been verified in three national infection data from the UK, India, and the US, and the proposed model has good generalization ability; (2) in the proposed hybrid model, SVM can efficiently predict the possible error of the predicted series given by TCN-GRU-DBN-Q components; (3) the integrated weights based on Q-learning can be adaptively adjusted according to the characteristics of the data in the forecasting tasks in different countries and multiple situations, which ensures the accuracy, robustness and generalization of the proposed model.
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Affiliation(s)
- Weiqiu Jin
- Shanghai Lung Cancer Center, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, 200030, PR China,School of Medicine, Shanghai Jiao Tong University, Shanghai, 200025, PR China
| | - Shuqing Dong
- School of Traffic and Transportation Engineering, Central South University, Hunan, 410075, PR China
| | - Chengqing Yu
- School of Traffic and Transportation Engineering, Central South University, Hunan, 410075, PR China
| | - Qingquan Luo
- Shanghai Lung Cancer Center, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, 200030, PR China,School of Medicine, Shanghai Jiao Tong University, Shanghai, 200025, PR China,Corresponding author. Shanghai Lung Cancer Center, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, 200030, PR China
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26
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Ormeño P, Márquez G, Guerrero-Nancuante C, Taramasco C. Detection of COVID-19 Patients Using Machine Learning Techniques: A Nationwide Chilean Study. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:ijerph19138058. [PMID: 35805713 PMCID: PMC9265284 DOI: 10.3390/ijerph19138058] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/07/2022] [Revised: 06/23/2022] [Accepted: 06/23/2022] [Indexed: 12/04/2022]
Abstract
Epivigila is a Chilean integrated epidemiological surveillance system with more than 17,000,000 Chilean patient records, making it an essential and unique source of information for the quantitative and qualitative analysis of the COVID-19 pandemic in Chile. Nevertheless, given the extensive volume of data controlled by Epivigila, it is difficult for health professionals to classify vast volumes of data to determine which symptoms and comorbidities are related to infected patients. This paper aims to compare machine learning techniques (such as support-vector machine, decision tree and random forest techniques) to determine whether a patient has COVID-19 or not based on the symptoms and comorbidities reported by Epivigila. From the group of patients with COVID-19, we selected a sample of 10% confirmed patients to execute and evaluate the techniques. We used precision, recall, accuracy, F1-score, and AUC to compare the techniques. The results suggest that the support-vector machine performs better than decision tree and random forest regarding the recall, accuracy, F1-score, and AUC. Machine learning techniques help process and classify large volumes of data more efficiently and effectively, speeding up healthcare decision making.
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Affiliation(s)
- Pablo Ormeño
- Escuela de Ingenieria y Negocios, Universidad de Viña del Mar, Viña del Mar 2520000, Chile
- Correspondence: (P.O.); (G.M.); (C.G.-N.); (C.T.)
| | - Gastón Márquez
- Departamento de Electrónica e Informática, Universidad Técnica Federico Santa María, Millennium Nucleus on Sociomedicine, Concepción 4030000, Chile
- Correspondence: (P.O.); (G.M.); (C.G.-N.); (C.T.)
| | - Camilo Guerrero-Nancuante
- Escuela de Enfermería, Universidad de Valparaíso, Valparaíso 2500000, Chile
- Correspondence: (P.O.); (G.M.); (C.G.-N.); (C.T.)
| | - Carla Taramasco
- Facultad de Ingeniería, Universidad Andrés Bello, Millennium Nucleus on Sociomedicine, Viña del Mar 2520000, Chile
- Correspondence: (P.O.); (G.M.); (C.G.-N.); (C.T.)
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Larabi-Marie-Sainte S, Alhalawani S, Shaheen S, Almustafa KM, Saba T, Khan FN, Rehman A. Forecasting COVID19 parameters using time-series: KSA, USA, Spain, and Brazil comparative case study. Heliyon 2022; 8:e09578. [PMID: 35694424 PMCID: PMC9162784 DOI: 10.1016/j.heliyon.2022.e09578] [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: 11/30/2021] [Revised: 01/15/2022] [Accepted: 05/23/2022] [Indexed: 12/03/2022] Open
Abstract
Many countries are suffering from the COVID19 pandemic. The number of confirmed cases, recovered, and deaths are of concern to the countries having a high number of infected patients. Forecasting these parameters is a crucial way to control the spread of the disease and struggle with the pandemic. This study aimed at forecasting the number of cases and deaths in KSA using time-series and well-known statistical forecasting techniques including Exponential Smoothing and Linear Regression. The study is extended to forecast the number of cases in the main countries such that the US, Spain, and Brazil (having a large number of contamination) to validate the proposed models (Drift, SES, Holt, and ETS). The forecast results were validated using four evaluation measures. The results showed that the proposed ETS (resp. Drift) model is efficient to forecast the number of cases (resp. deaths). The comparison study, using the number of cases in KSA, showed that ETS (with RMSE reaching 18.44) outperforms the state-of-the art studies (with RMSE equal to 107.54). The proposed forecasting model can be used as a benchmark to tackle this pandemic in any country.
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Affiliation(s)
- Souad Larabi-Marie-Sainte
- Department of Computer Science, College of Computer and Information Sciences, Prince Sultan University, Riyadh 11586, Saudi Arabia
| | - Sawsan Alhalawani
- Department of Computer Science, College of Computer and Information Sciences, Prince Sultan University, Riyadh 11586, Saudi Arabia
| | - Sara Shaheen
- Department of Computer Science, College of Computer and Information Sciences, Prince Sultan University, Riyadh 11586, Saudi Arabia
| | - Khaled Mohamad Almustafa
- Department of Information Sciences, College of Computer and Information Sciences, Prince Sultan University, Riyadh 11586, Saudi Arabia
| | - Tanzila Saba
- Artificial Intelligence Data Analytics (AIDA) Lab, College of Computer and Information Sciences, Prince Sultan University, Riyadh 12435, Saudi Arabia
| | - Fatima Nayer Khan
- Department of Information Sciences, College of Computer and Information Sciences, Prince Sultan University, Riyadh 11586, Saudi Arabia
| | - Amjad Rehman
- Artificial Intelligence Data Analytics (AIDA) Lab, College of Computer and Information Sciences, Prince Sultan University, Riyadh 12435, Saudi Arabia
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Tulshyan V, Sharma D, Mittal M. An Eye on the Future of COVID-19: Prediction of Likely Positive Cases and Fatality in India over a 30-Day Horizon Using the Prophet Model. Disaster Med Public Health Prep 2022; 16:980-986. [PMID: 33203489 PMCID: PMC7985632 DOI: 10.1017/dmp.2020.444] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2020] [Revised: 11/06/2020] [Accepted: 11/11/2020] [Indexed: 12/23/2022]
Abstract
OBJECTIVE The coronavirus disease (COVID-19) pandemic was initiated in Wuhan Province of mainland China in December 2019 and has spread over the world. This study analyzes the effects of COVID-19 based on likely positive cases and fatality in India during and after the lockdown period from March 24, 2020, to May 24, 2020. METHODS Python has been used as the main programming language for data analysis and forecasting using the Prophet model, a time series analysis model. The data set has been preprocessed by grouping together the days for total numbers of cases and deaths on few selected dates and removing missing values present in some states. RESULTS The Prophet model performs better in terms of precision on the real data. Prediction depicts that, during the lockdown, the total cases were rising but in a controlled manner with an accuracy of 87%. After the relaxation of lockdown rules, the predictions have shown an obstreperous situation with an accuracy of 60%. CONCLUSION The resilience could have been better if the lockdown with strict norms was continued without much relaxation. The situation after lockdown has been found to be uncertain as observed by the experimental study conducted in this work.
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Affiliation(s)
- Vatsal Tulshyan
- Department of Computer Science and Engineering, Amity School of Engineering and Technology, Amity University, Noida, India
| | - Dolly Sharma
- Department of Computer Science and Engineering, Amity School of Engineering and Technology, Amity University, Noida, India
| | - Mamta Mittal
- Department of Computer Science and Engineering, G. B. Pant Government Engineering College, Okhla, New Delhi, India
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Abstract
Complex phenomena have some common characteristics, such as nonlinearity, complexity, and uncertainty. In these phenomena, components typically interact with each other and a part of the system may affect other parts or vice versa. Accordingly, the human brain, the Earth’s global climate, the spreading of viruses, the economic organizations, and some engineering systems such as the transportation systems and power grids can be categorized into these phenomena. Since both analytical approaches and AI methods have some specific characteristics in solving complex problems, a combination of these techniques can lead to new hybrid methods with considerable performance. This is why several types of research have recently been conducted to benefit from these combinations to predict the spreading of COVID-19 and its dynamic behavior. In this review, 80 peer-reviewed articles, book chapters, conference proceedings, and preprints with a focus on employing hybrid methods for forecasting the spreading of COVID-19 published in 2020 have been aggregated and reviewed. These documents have been extracted from Google Scholar and many of them have been indexed on the Web of Science. Since there were many publications on this topic, the most relevant and effective techniques, including statistical models and deep learning (DL) or machine learning (ML) approach, have been surveyed in this research. The main aim of this research is to describe, summarize, and categorize these effective techniques considering their restrictions to be used as trustable references for scientists, researchers, and readers to make an intelligent choice to use the best possible method for their academic needs. Nevertheless, considering the fact that many of these techniques have been used for the first time and need more evaluations, we recommend none of them as an ideal way to be used in their project. Our study has shown that these methods can hold the robustness and reliability of statistical methods and the power of computation of DL ones.
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Mydukuri RV, Kallam S, Patan R, Al‐Turjman F, Ramachandran M. Deming least square regressed feature selection and Gaussian neuro-fuzzy multi-layered data classifier for early COVID prediction. EXPERT SYSTEMS 2022; 39:e12694. [PMID: 34230740 PMCID: PMC8250320 DOI: 10.1111/exsy.12694] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/29/2021] [Accepted: 02/10/2021] [Indexed: 05/31/2023]
Abstract
Coronavirus disease (COVID-19) is a harmful disease caused by the new SARS-CoV-2 virus. COVID-19 disease comprises symptoms such as cold, cough, fever, and difficulty in breathing. COVID-19 has affected many countries and their spread in the world has put humanity at risk. Due to the increasing number of cases and their stress on administration as well as health professionals, different prediction techniques were introduced to predict the coronavirus disease existence in patients. However, the accuracy was not improved, and time consumption was not minimized during the disease prediction. To address these problems, least square regressive Gaussian neuro-fuzzy multi-layered data classification (LSRGNFM-LDC) technique is introduced in this article. LSRGNFM-LDC technique performs efficient COVID prediction with better accuracy and lesser time consumption through feature selection and classification. The preprocessing is used to eliminate the unwanted data in input features. Preprocessing is applied to reduce the time complexity. Next, Deming Least Square Regressive Feature Selection process is carried out for selecting the most relevant features through identifying the line of best fit. After the feature selection process, Gaussian neuro-fuzzy classifier in LSRGNFM-LDC technique performs the data classification process with help of fuzzy if-then rules for performing prediction process. Finally, the fuzzy if-then rule classifies the patient data as lower risk level, medium risk level and higher risk level with higher accuracy and lesser time consumption. Experimental evaluation is performed by Novel Corona Virus 2019 Dataset using different metrics like prediction accuracy, prediction time, and error rate. The result shows that LSRGNFM-LDC technique improves the accuracy and minimizes the time consumption as well as error rate than existing works during COVID prediction.
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Affiliation(s)
- Rathnamma V Mydukuri
- Department of Computer Science and EngineeringKSRM College Of Engineering (A)KadapaAndhra PradeshIndia
| | - Suresh Kallam
- Department of Computer Science & EngineeringSree Vidyanikethan Engineering CollegeTirupatiAndhra PradeshIndia
| | - Rizwan Patan
- Department of Computer Science & EngineeringVelagapudi Ramakrishna Siddhartha Engineering CollegeVijayawadaAndhra PradeshIndia
| | - Fadi Al‐Turjman
- Research Center for AI and IoT, Artificial Intelligence Engineering DepartmentNear East UniversityMersinTurkey
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Mir MH, Jamwal S, Mehbodniya A, Garg T, Iqbal U, Samori IA. IoT-Enabled Framework for Early Detection and Prediction of COVID-19 Suspects by Leveraging Machine Learning in Cloud. JOURNAL OF HEALTHCARE ENGINEERING 2022; 2022:7713939. [PMID: 35432824 PMCID: PMC9006083 DOI: 10.1155/2022/7713939] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/13/2022] [Revised: 02/12/2022] [Accepted: 03/14/2022] [Indexed: 01/08/2023]
Abstract
COVID-19 is the repugnant but the most searched word since its outbreak in November 2019 across the globe. The world has to battle with it until an effective solution is developed. Due to the advancement in mobile and sensor technology, it is possible to come up with Internet of things-based healthcare systems. These novel healthcare systems can be proactive and preventive rather than traditional reactive healthcare systems. This article proposes a real-time IoT-enabled framework for the detection and prediction of COVID-19 suspects in early stages, by collecting symptomatic data and analyzing the nature of the virus in a better manner. The framework computes the presence of COVID-19 virus by mining the health parameters collected in real time from sensors and other IoT devices. The framework is comprised of four main components: user system or data collection center, data analytic center, diagnostic system, and cloud system. To point out and detect the COVID-19 suspected in real time, this work proposes the five machine learning techniques, namely support vector machine (SVM), decision tree, naïve Bayes, logistic regression, and neural network. In our proposed framework, the real and primary dataset collected from SKIMS, Srinagar, is used to validate our work. The experiment on the primary dataset was conducted using different machine learning techniques on selected symptoms. The efficiency of algorithms is calculated by computing the results of performance metrics such as accuracy, precision, recall, F1 score, root-mean-square error, and area under the curve score. The employed machine learning techniques have shown the accuracy of above 95% on the primary symptomatic data. Based on the experiment conducted, the proposed framework would be effective in the early identification and prediction of COVID-19 suspect realizing the nature of the disease in better way.
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Affiliation(s)
- Mahmood Hussain Mir
- Department of Computer Sciences, Baba Ghulam Shah Badshah University, Rajouri, Jammu and Kashmir 185234, India
| | - Sanjay Jamwal
- Department of Computer Sciences, Baba Ghulam Shah Badshah University, Rajouri, Jammu and Kashmir 185234, India
| | - Abolfazl Mehbodniya
- Department of Electronics and Communication Engineering, Kuwait College of Science and Technology (KCST), Doha Area, 7th Ring Road, Kuwait
| | - Tanya Garg
- Thapar Institute of Engineering and Technology, Patiala, Punjab, India
| | - Ummer Iqbal
- National Institute of Technology Srinagar, Srinagar, J&K, India
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Using the SARIMA Model to Forecast the Fourth Global Wave of Cumulative Deaths from COVID-19: Evidence from 12 Hard-Hit Big Countries. ECONOMETRICS 2022. [DOI: 10.3390/econometrics10020018] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
The COVID-19 pandemic is a serious threat to all of us. It has caused an unprecedented shock to the world’s economy, and it has interrupted the lives and livelihood of millions of people. In the last two years, a large body of literature has attempted to forecast the main dimensions of the COVID-19 outbreak using a wide set of models. In this paper, I forecast the short- to mid-term cumulative deaths from COVID-19 in 12 hard-hit big countries around the world as of 20 August 2021. The data used in the analysis were extracted from the Our World in Data COVID-19 dataset. Both non-seasonal and seasonal autoregressive integrated moving averages (ARIMA and SARIMA) were estimated. The analysis showed that: (i) ARIMA/SARIMA forecasts were sufficiently accurate in both the training and test set by always outperforming the simple alternative forecasting techniques chosen as benchmarks (Mean, Naïve, and Seasonal Naïve); (ii) SARIMA models outperformed ARIMA models in 47 out 48 metrics (in forecasting future values), i.e., on 97.9% of all the considered forecast accuracy measures (mean absolute error [MAE], mean absolute percentage error [MAPE], mean absolute scaled error [MASE], and the root mean squared error [RMSE]), suggesting a clear seasonal pattern in the data; and (iii) the forecasted values from SARIMA models fitted very well the observed (real-time) data for the period 21 August 2021–19 September 2021 for almost all the countries analyzed. This article shows that SARIMA can be safely used for both the short- and medium-term predictions of COVID-19 deaths. Thus, this approach can help government authorities to monitor and manage the huge pressure that COVID-19 is exerting on national healthcare systems.
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Aruleba RT, Adekiya TA, Ayawei N, Obaido G, Aruleba K, Mienye ID, Aruleba I, Ogbuokiri B. COVID-19 Diagnosis: A Review of Rapid Antigen, RT-PCR and Artificial Intelligence Methods. Bioengineering (Basel) 2022; 9:153. [PMID: 35447713 PMCID: PMC9024895 DOI: 10.3390/bioengineering9040153] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2022] [Revised: 03/22/2022] [Accepted: 03/23/2022] [Indexed: 12/15/2022] Open
Abstract
As of 27 December 2021, SARS-CoV-2 has infected over 278 million persons and caused 5.3 million deaths. Since the outbreak of COVID-19, different methods, from medical to artificial intelligence, have been used for its detection, diagnosis, and surveillance. Meanwhile, fast and efficient point-of-care (POC) testing and self-testing kits have become necessary in the fight against COVID-19 and to assist healthcare personnel and governments curb the spread of the virus. This paper presents a review of the various types of COVID-19 detection methods, diagnostic technologies, and surveillance approaches that have been used or proposed. The review provided in this article should be beneficial to researchers in this field and health policymakers at large.
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Affiliation(s)
- Raphael Taiwo Aruleba
- Department of Molecular and Cell Biology, Faculty of Science, University of Cape Town, Cape Town 7701, South Africa;
| | - Tayo Alex Adekiya
- Department of Pharmacy and Pharmacology, School of Therapeutic Science, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, 7 York Road, Parktown 2193, South Africa;
| | - Nimibofa Ayawei
- Department of Chemistry, Bayelsa Medical University, Yenagoa PMB 178, Bayelsa State, Nigeria;
| | - George Obaido
- Department of Computer Science and Engineering, University of California, San Diego, La Jolla, CA 92093-0404, USA
| | - Kehinde Aruleba
- School of Computing and Mathematical Sciences, University of Leicester, Leicester LE1 7RH, UK
| | - Ibomoiye Domor Mienye
- Department of Electrical and Electronic Engineering Science, University of Johannesburg, Johannesburg 2006, South Africa; (I.D.M.); (I.A.)
| | - Idowu Aruleba
- Department of Electrical and Electronic Engineering Science, University of Johannesburg, Johannesburg 2006, South Africa; (I.D.M.); (I.A.)
| | - Blessing Ogbuokiri
- Department of Mathematics and Statistics, York University, Toronto, ON M3J 1P3, Canada;
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Petropoulos F, Makridakis S, Stylianou N. COVID-19: Forecasting confirmed cases and deaths with a simple time series model. INTERNATIONAL JOURNAL OF FORECASTING 2022; 38:439-452. [PMID: 33311822 PMCID: PMC7717777 DOI: 10.1016/j.ijforecast.2020.11.010] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/25/2023]
Abstract
Forecasting the outcome of outbreaks as early and as accurately as possible is crucial for decision-making and policy implementations. A significant challenge faced by forecasters is that not all outbreaks and epidemics turn into pandemics, making the prediction of their severity difficult. At the same time, the decisions made to enforce lockdowns and other mitigating interventions versus their socioeconomic consequences are not only hard to make, but also highly uncertain. The majority of modeling approaches to outbreaks, epidemics, and pandemics take an epidemiological approach that considers biological and disease processes. In this paper, we accept the limitations of forecasting to predict the long-term trajectory of an outbreak, and instead, we propose a statistical, time series approach to modelling and predicting the short-term behavior of COVID-19. Our model assumes a multiplicative trend, aiming to capture the continuation of the two variables we predict (global confirmed cases and deaths) as well as their uncertainty. We present the timeline of producing and evaluating 10-day-ahead forecasts over a period of four months. Our simple model offers competitive forecast accuracy and estimates of uncertainty that are useful and practically relevant.
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Affiliation(s)
| | - Spyros Makridakis
- Institute for the Future (IFF), University of Nicosia, Nicosia, Cyprus
| | - Neophytos Stylianou
- International Institute for Compassionate Care, Cyprus
- School of Management, University of Bath, UK
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Guadiana-Alvarez JL, Hussain F, Morales-Menendez R, Rojas-Flores E, García-Zendejas A, Escobar CA, Ramírez-Mendoza RA, Wang J. Prognosis patients with COVID-19 using deep learning. BMC Med Inform Decis Mak 2022; 22:78. [PMID: 35346166 PMCID: PMC8959787 DOI: 10.1186/s12911-022-01820-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2021] [Accepted: 03/14/2022] [Indexed: 11/18/2022] Open
Abstract
Background The coronavirus (COVID-19) is a novel pandemic and recently we do not have enough knowledge about the virus behaviour and key performance indicators (KPIs) to assess the mortality risk forecast. However, using a lot of complex and expensive biomarkers could be impossible for many low budget hospitals. Timely identification of the risk of mortality of COVID-19 patients (RMCPs) is essential to improve hospitals' management systems and resource allocation standards. Methods For the mortality risk prediction, this research work proposes a COVID-19 mortality risk calculator based on a deep learning (DL) model and based on a dataset provided by the HM Hospitals Madrid, Spain. A pre-processing strategy for unbalanced classes and feature selection is proposed. To evaluate the proposed methods, an over-sampling Synthetic Minority TEchnique (SMOTE) and data imputation approaches are introduced which is based on the K-nearest neighbour. Results A total of 1,503 seriously ill COVID-19 patients having a median age of 70 years old are comprised in the research work, with 927 (61.7%) males and 576 (38.3%) females. A total of 48 features are considered to evaluate the proposed method, and the following results are achieved. It includes the following values i.e., area under the curve (AUC) 0.93, F2 score 0.93, recall 1.00, accuracy, 0.95, precision 0.91, specificity 0.9279 and maximum probability of correct decision (MPCD) 0.93. Conclusion The results show that the proposed method is significantly best for the mortality risk prediction of patients with COVID-19 infection. The MPCD score shows that the proposed DL outperforms on every dataset when evaluating even with an over-sampling technique. The benefits of the data imputation algorithm for unavailable biomarker data are also evaluated. Based on the results, the proposed scheme could be an appropriate tool for critically ill Covid-19 patients to assess the risk of mortality and prognosis.
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Alyasseri ZAA, Al‐Betar MA, Doush IA, Awadallah MA, Abasi AK, Makhadmeh SN, Alomari OA, Abdulkareem KH, Adam A, Damasevicius R, Mohammed MA, Zitar RA. Review on COVID-19 diagnosis models based on machine learning and deep learning approaches. EXPERT SYSTEMS 2022; 39:e12759. [PMID: 34511689 PMCID: PMC8420483 DOI: 10.1111/exsy.12759] [Citation(s) in RCA: 58] [Impact Index Per Article: 29.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/29/2021] [Revised: 05/17/2021] [Accepted: 06/07/2021] [Indexed: 05/02/2023]
Abstract
COVID-19 is the disease evoked by a new breed of coronavirus called the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Recently, COVID-19 has become a pandemic by infecting more than 152 million people in over 216 countries and territories. The exponential increase in the number of infections has rendered traditional diagnosis techniques inefficient. Therefore, many researchers have developed several intelligent techniques, such as deep learning (DL) and machine learning (ML), which can assist the healthcare sector in providing quick and precise COVID-19 diagnosis. Therefore, this paper provides a comprehensive review of the most recent DL and ML techniques for COVID-19 diagnosis. The studies are published from December 2019 until April 2021. In general, this paper includes more than 200 studies that have been carefully selected from several publishers, such as IEEE, Springer and Elsevier. We classify the research tracks into two categories: DL and ML and present COVID-19 public datasets established and extracted from different countries. The measures used to evaluate diagnosis methods are comparatively analysed and proper discussion is provided. In conclusion, for COVID-19 diagnosing and outbreak prediction, SVM is the most widely used machine learning mechanism, and CNN is the most widely used deep learning mechanism. Accuracy, sensitivity, and specificity are the most widely used measurements in previous studies. Finally, this review paper will guide the research community on the upcoming development of machine learning for COVID-19 and inspire their works for future development. This review paper will guide the research community on the upcoming development of ML and DL for COVID-19 and inspire their works for future development.
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Affiliation(s)
- Zaid Abdi Alkareem Alyasseri
- Center for Artificial Intelligence Technology, Faculty of Information Science and TechnologyUniversiti Kebangsaan MalaysiaBangiMalaysia
- ECE Department‐Faculty of EngineeringUniversity of KufaNajafIraq
| | - Mohammed Azmi Al‐Betar
- Artificial Intelligence Research Center (AIRC)Ajman UniversityAjmanUnited Arab Emirates
- Department of Information TechnologyAl‐Huson University College, Al‐Balqa Applied UniversityIrbidJordan
| | - Iyad Abu Doush
- Computing Department, College of Engineering and Applied SciencesAmerican University of KuwaitSalmiyaKuwait
- Computer Science DepartmentYarmouk UniversityIrbidJordan
| | - Mohammed A. Awadallah
- Artificial Intelligence Research Center (AIRC)Ajman UniversityAjmanUnited Arab Emirates
- Department of Computer ScienceAl‐Aqsa UniversityGazaPalestine
| | - Ammar Kamal Abasi
- Artificial Intelligence Research Center (AIRC)Ajman UniversityAjmanUnited Arab Emirates
- School of Computer SciencesUniversiti Sains MalaysiaPenangMalaysia
| | - Sharif Naser Makhadmeh
- Artificial Intelligence Research Center (AIRC)Ajman UniversityAjmanUnited Arab Emirates
- Faculty of Information TechnologyMiddle East UniversityAmmanJordan
| | | | | | - Afzan Adam
- Center for Artificial Intelligence Technology, Faculty of Information Science and TechnologyUniversiti Kebangsaan MalaysiaBangiMalaysia
| | | | - Mazin Abed Mohammed
- College of Computer Science and Information TechnologyUniversity of AnbarAnbarIraq
| | - Raed Abu Zitar
- Sorbonne Center of Artificial IntelligenceSorbonne University‐Abu DhabiAbu DhabiUnited Arab Emirates
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Grekousis G, Feng Z, Marakakis I, Lu Y, Wang R. Ranking the importance of demographic, socioeconomic, and underlying health factors on US COVID-19 deaths: A geographical random forest approach. Health Place 2022; 74:102744. [PMID: 35114614 PMCID: PMC8801594 DOI: 10.1016/j.healthplace.2022.102744] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/03/2021] [Revised: 12/31/2021] [Accepted: 01/20/2022] [Indexed: 12/22/2022]
Abstract
A growing number of studies show that the uneven spatial distribution of COVID-19 deaths is related to demographic and socioeconomic disparities across space. However, most studies fail to assess the relative importance of each factor to COVID-19 death rate and, more importantly, how this importance varies spatially. Here, we assess the variables that are more important locally using Geographical Random Forest (GRF), a local non-linear regression method. Through GRF, we estimated the non-linear relationships between the COVID-19 death rate and 29 socioeconomic and health-related factors during the first year of the pandemic in the USA (county level). GRF outputs are compared to global (Random Forest and OLS) and local (Geographically Weighted Regression) models. Results show that GRF outperforms all models and that the importance of variables highly varies by location. For example, lack of health insurance is the most important factor in one-third (34.86%) of the US counties. Most of these counties are (concentrated mainly in the Midwest region and South region). On the other hand, no leisure-time physical activity is the most important primary factor for 19.86% of the US counties. These counties are found in California, Oregon, Washington, and parts of the South region. Understanding the location-based characteristics and spatial patterns of socioeconomic and health factors linked to COVID-19 deaths is paramount for policy designing and decision making. In this way, interventions can be designed and implemented based on the most important factors locally, avoiding thus general guidelines addressed for the entire nation.
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Affiliation(s)
- George Grekousis
- School of Geography and Planning, Department of Urban and Regional Planning, Sun Yat-Sen University, Xingang Xi Road, Guangzhou, 510275, China; Guangdong Key Laboratory for Urbanization and Geo-simulation, China; Guangdong Provincial Engineering Research Center for Public Security and Disaster, China.
| | - Zhixin Feng
- School of Geography and Planning, Department of Urban and Regional Planning, Sun Yat-Sen University, Xingang Xi Road, Guangzhou, 510275, China.
| | - Ioannis Marakakis
- Department of Geography and Regional Planning, School of Rural & Surveying Engineering, National Technical University of Athens (NTUA), 15780, Zografou Campus, Greece.
| | - Yi Lu
- Department of Architecture and Civil Engineering, City University of Hong Kong, Hong Kong, China; City University of Hong Kong Shenzhen Research Institute, Shenzhen, China.
| | - Ruoyu Wang
- Institute of Geography, School of GeoSciences, University of Edinburgh, Edinburgh, UK.
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Singh A, Jindal V, Sandhu R, Chang V. A scalable framework for smart COVID surveillance in the workplace using Deep Neural Networks and cloud computing. EXPERT SYSTEMS 2022; 39:e12704. [PMID: 34177036 PMCID: PMC8209860 DOI: 10.1111/exsy.12704] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/05/2021] [Revised: 03/02/2021] [Accepted: 03/30/2021] [Indexed: 06/13/2023]
Abstract
A smart and scalable system is required to schedule various machine learning applications to control pandemics like COVID-19 using computing infrastructure provided by cloud and fog computing. This paper proposes a framework that considers the use case of smart office surveillance to monitor workplaces for detecting possible violations of COVID effectively. The proposed framework uses deep neural networks, fog computing and cloud computing to develop a scalable and time-sensitive infrastructure that can detect two major violations: wearing a mask and maintaining a minimum distance of 6 feet between employees in the office environment. The proposed framework is developed with the vision to integrate multiple machine learning applications and handle the computing infrastructures for pandemic applications. The proposed framework can be used by application developers for the rapid development of new applications based on the requirements and do not worry about scheduling. The proposed framework is tested for two independent applications and performed better than the traditional cloud environment in terms of latency and response time. The work done in this paper tries to bridge the gap between machine learning applications and their computing infrastructure for COVID-19.
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Affiliation(s)
- Ajay Singh
- Department of Computer Science and Engineering and Information TechnologyJaypee University of Information TechnologySolanIndia
| | - Vaibhav Jindal
- Department of Computer Science and Engineering and Information TechnologyJaypee University of Information TechnologySolanIndia
| | - Rajinder Sandhu
- Department of Computer Science and Engineering and Information TechnologyJaypee University of Information TechnologySolanIndia
| | - Victor Chang
- Artificial Intelligence and Information Systems Research Group, School Computing, Engineering and Digital TechnologiesTeesside UniversityMiddlesbroughUK
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Kalezhi J, Chibuluma M, Chembe C, Chama V, Lungo F, Kunda D. Modelling Covid-19 infections in Zambia using data mining techniques. RESULTS IN ENGINEERING 2022; 13:100363. [PMID: 35317385 PMCID: PMC8813672 DOI: 10.1016/j.rineng.2022.100363] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/29/2021] [Revised: 01/08/2022] [Accepted: 02/01/2022] [Indexed: 06/14/2023]
Abstract
The outbreak of Covid-19 pandemic has been declared a global health crisis by the World Health Organization since its emergence. Several researchers have proposed a number of techniques to understand how the pandemic affects the populations. Reported among these techniques are data mining models which have been successfully applied in a wide range of situations before the advent of Covid-19 pandemic. In this work, the researchers have applied a number of existing data mining methods (classifiers) available in the Waikato Environment for Knowledge Analysis (WEKA) machine learning library. WEKA was used to gain a better understanding on how the epidemic spread within Zambia. The classifiers used are J48 decision tree, Multilayer Perceptron and Naïve Bayes among others. The predictions of these techniques are compared against simpler classifiers and those reported in related works.
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Affiliation(s)
- Josephat Kalezhi
- Department of Computer Engineering, Copperbelt University, Kitwe, Zambia
| | - Mathews Chibuluma
- Department of Information Technology/Systems, Copperbelt University, Kitwe, Zambia
| | | | - Victoria Chama
- Department of Computer Science and Information Technology, Mulungushi University, Kabwe, Zambia
| | - Francis Lungo
- School of Social Sciences, Mulungushi University, Kabwe, Zambia
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40
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Performance evaluation of regression models for COVID-19: A statistical and predictive perspective. AIN SHAMS ENGINEERING JOURNAL 2022; 13. [PMCID: PMC8423812 DOI: 10.1016/j.asej.2021.08.016] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/16/2023]
Abstract
Research is very important in the pandemic situation of COVID-19 to deliver a speedy solution to this problem. COVID-19 has presented governments, corporations and ordinary citizens around the world with technology playing an essential role to tackle the crisis. Moderate and flexible innovation arrangements that can speed up progress towards giving critical well-being ability are proved hourly. Knowledge with the aid of creativity must be obtained, accepted and analysed in a short time frame. In this example, the machine learning model has a major role to play in predicting the number of next positive COVID-19 cases to come. For government departments to take effective and strengthened future COVID-19 planning and innovation. The ongoing global pandemic of COVID-19 has been non-linear and dynamic. Due to the especially perplexing nature of the COVID-19 episode and its diversity from country to country, this study recommends machine learning as a convincing means to demonstrate flare-up. In this linear regression, polynomial regression, ridge regression, polynomial ridgeregression, support vector regression models, the COVID-19 data set from multiple on-line tools have been evaluated. During the work process comprehensive experiments were performed and each test was evaluated with the parameters mean square error (MSE), medium absolute error (MAE), root mean square error (RMSE) and R2 score. This study also offers a path for future research using regression models based on machine learning. Precise validation and data analysis can contribute to strategies for healing and disease prevention at an early stage. A systematic comprehensive strategy is a new philosophy in which statistical data for government agencies and community can be forecast.
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41
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A Decision-Level Fusion Method for COVID-19 Patient Health Prediction. BIG DATA RESEARCH 2022; 27:100287. [PMCID: PMC8574072 DOI: 10.1016/j.bdr.2021.100287] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/26/2020] [Revised: 08/11/2021] [Accepted: 10/28/2021] [Indexed: 06/16/2023]
Abstract
With the continuous attempts to develop effective machine learning methods, information fusion approaches play an important role in integrating data from multiple sources and improving these methods' performance. Among the different fusion techniques, decision-level fusion has unique advantages to fuse the decisions of various classifiers and getting an effective outcome. In this paper, we propose a decision-level fusion method that combines three well-calibrated ensemble classifiers, namely, a random forest (RF), gradient boosting (GB), and extreme gradient boosting (XGB) methods. It is used to predict the COVID-19 patient health for early monitoring and efficient treatment. A soft voting technique is used to generate the final decision result from the predictions of these calibrated classifiers. The method uses the COVID-19 patient's health information, travel demographic, and geographical data to predict the possible outcome of the COVID-19 case, recovered, or death. A different set of experiments is conducted on a public novel Corona Virus 2019 dataset using a different ratio of test sets. The experimental results show that the proposed fusion method achieved an accuracy of 97.24% and an F1-score of 0.97, which is higher than the current related work that has an accuracy of 94% and an F1-score 0.86, on 20% test set taken from the dataset.
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42
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Temperature Forecasting Correction Based on Operational GRAPES-3km Model Using Machine Learning Methods. ATMOSPHERE 2022. [DOI: 10.3390/atmos13020362] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/10/2022]
Abstract
Postprocess correction is essential to improving the model forecasting result, in which machine learning methods play more and more important roles. In this study, three machine learning (ML) methods of Linear Regression, LSTM-FCN and LightGBM were used to carry out the correction of temperature forecasting of an operational high-resolution model GRAPES-3km. The input parameters include 2 m temperature, relative humidity, local pressure and wind speed forecasting and observation data in Shaanxi province of China from 1 January 2019 to 31 December 2020. The dataset from September 2018 was used for model evaluation using the metrics of root mean square error (RMSE), average absolute error (MAE) and coefficient of determination (R2). All three machine learning methods perform very well in correcting the temperature forecast of GRAPES-3km model. The RMSE decreased by 33%, 32% and 40%, respectively, the MAE decreased by 33%, 34% and 41%, respectively, the R2 increased by 21.4%, 21.5% and 25.2%, respectively. Among the three methods, LightGBM performed the best with the forecast accuracy rate reaching above 84%.
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43
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Bhattacharyya A, Chakraborty T, Rai SN. Stochastic forecasting of COVID-19 daily new cases across countries with a novel hybrid time series model. NONLINEAR DYNAMICS 2022; 107:3025-3040. [PMID: 35039713 PMCID: PMC8754528 DOI: 10.1007/s11071-021-07099-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/25/2021] [Accepted: 11/20/2021] [Indexed: 06/14/2023]
Abstract
An unprecedented outbreak of the novel coronavirus (COVID-19) in the form of peculiar pneumonia has spread globally since its first case in Wuhan province, China, in December 2019. Soon after, the infected cases and mortality increased rapidly. The future of the pandemic's progress was uncertain, and thus, predicting it became crucial for public health researchers. These predictions help the effective allocation of health-care resources, stockpiling, and help in strategic planning for clinicians, government authorities, and public health policymakers after understanding the extent of the effect. The main objective of this paper is to develop a hybrid forecasting model that can generate real-time out-of-sample forecasts of COVID-19 outbreaks for five profoundly affected countries, namely the USA, Brazil, India, the UK, and Canada. A novel hybrid approach based on the Theta method and autoregressive neural network (ARNN) model, named Theta-ARNN (TARNN) model, is developed. Daily new cases of COVID-19 are nonlinear, non-stationary, and volatile; thus, a single specific model cannot be ideal for future prediction of the pandemic. However, the newly introduced hybrid forecasting model with an acceptable prediction error rate can help healthcare and government for effective planning and resource allocation. The proposed method outperforms traditional univariate and hybrid forecasting models for the test datasets on an average.
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Affiliation(s)
- Arinjita Bhattacharyya
- Department of Bioinformatics and Biostatistics, University of Louisville, Louisville, KY USA
| | - Tanujit Chakraborty
- Department of Science and Engineering, Sorbonne University Abu Dhabi, Abu Dhabi, UAE
| | - Shesh N. Rai
- Department of Bioinformatics and Biostatistics, University of Louisville, Louisville, KY USA
- Biostatistics and Bioinformatics Facility, JG Brown Cancer Center, University of Louisville, Louisville, KY USA
- The Christina Lee Brown Envirome Institute, University of Louisville, Louisville, KY USA
- University of Louisville Alcohol Research Center, University of Louisville, Louisville, KY USA
- University of Louisville Hepatobiology & Toxicology Center, University of Louisville, Louisville, KY USA
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44
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Forecasting the Severity of COVID-19 Pandemic Amidst the Emerging SARS-CoV-2 Variants: Adoption of ARIMA Model. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2022; 2022:3163854. [PMID: 35069779 PMCID: PMC8776442 DOI: 10.1155/2022/3163854] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Revised: 11/14/2021] [Accepted: 12/06/2021] [Indexed: 12/15/2022]
Abstract
Currently, the global report of COVID-19 cases is around 110 million, and more than 2.43 million related death cases as of February 18, 2021. Viruses continuously change through mutation; hence, different virus of SARS-CoV-2 has been reported globally. The United Kingdom (UK), South Africa, Brazil, and Nigeria are the countries from which these emerged variants have been notified and now spreading globally. Therefore, these countries have been selected as a research sample for the present study. The datasets analyzed in this study spanned from March 1, 2020, to January 31, 2021, and were obtained from the World Health Organization website. The study used the Autoregressive Integrated Moving Average (ARIMA) model to forecast coronavirus incidence in the UK, South Africa, Brazil, and Nigeria. ARIMA models with minimum Akaike Information Criterion Correction (AICc) and statistically significant parameters were chosen as the best models in this research. Accordingly, for the new confirmed cases, ARIMA (3,1,14), ARIMA (0,1,11), ARIMA (1,0,10), and ARIMA (1,1,14) models were chosen for the UK, South Africa, Brazil, and Nigeria, respectively. Also, the model specification for the confirmed death cases was ARIMA (3,0,4), ARIMA (0,1,4), ARIMA (1,0,7), and ARIMA (Brown); models were selected for the UK, South Africa, Brazil, and Nigeria, respectively. The results of the ARIMA model forecasting showed that if the required measures are not taken by the respective governments and health practitioners in the days to come, the magnitude of the coronavirus pandemic is expected to increase in the study's selected countries.
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45
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Rajab K, Kamalov F, Cherukuri AK. Forecasting COVID-19: Vector Autoregression-Based Model. ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2022; 47:6851-6860. [PMID: 35004125 PMCID: PMC8722659 DOI: 10.1007/s13369-021-06526-2] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/12/2021] [Accepted: 10/28/2021] [Indexed: 11/28/2022]
Abstract
Forecasting the spread of COVID-19 infection is an important aspect of public health management. In this paper, we propose an approach to forecasting the spread of the pandemic based on the vector autoregressive model. Concretely, we combine the time series for the number of new cases and the number of new deaths to obtain a joint forecasting model. We apply the proposed model to forecast the number of new cases and deaths in the UAE, Saudi Arabia, and Kuwait. Test results based on out-of-sample forecast show that the proposed model achieves a high level of accuracy that is superior to many existing methods. Concretely, our model achieves mean absolute percentage error (MAPE) of 0.35%, 2.03%, and 3.75% in predicting the number of daily new cases for the three countries, respectively. Furthermore, interpolating our predictions to forecast the cumulative number of cases, we obtain MAPE of 0.0017%, 0.002%, and 0.024%, respectively. The strong performance of the proposed approach indicates that it could be a valuable tool in managing the pandemic.
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46
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Ziyadidegan S, Razavi M, Pesarakli H, Javid AH, Erraguntla M. Factors affecting the COVID-19 risk in the US counties: an innovative approach by combining unsupervised and supervised learning. STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT : RESEARCH JOURNAL 2022; 36:1469-1484. [PMID: 35035282 PMCID: PMC8747889 DOI: 10.1007/s00477-021-02148-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 11/26/2021] [Indexed: 05/07/2023]
Abstract
The COVID-19 disease spreads swiftly, and nearly three months after the first positive case was confirmed in China, Coronavirus started to spread all over the United States. Some states and counties reported high number of positive cases and deaths, while some reported lower COVID-19 related cases and death. In this paper, the factors that could affect the risk of COVID-19 infection and death were analyzed in county level. An innovative method by using K-means clustering and several classification models is utilized to determine the most critical factors. Results showed that longitudinal coordinate and population density, latitudinal coordinate, percentage of non-white people, percentage of uninsured people, percent of people below poverty, percentage of Elderly people, number of ICU beds per 10,000 people, percentage of smokers were the most significant attributes.
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Affiliation(s)
- Samira Ziyadidegan
- Department of Industrial and Systems Engineering, Texas A&M University, College Station, TX 77843 USA
| | - Moein Razavi
- Department of Computer Science and Engineering, Texas A&M University, College Station, TX 77843 USA
| | - Homa Pesarakli
- Department of Architecture, Texas A&M University, College Station, TX 77843 USA
| | - Amir Hossein Javid
- Department of Statistics, Oklahoma State University, Stillwater, OK 74074 USA
| | - Madhav Erraguntla
- Department of Industrial and Systems Engineering, Texas A&M University, College Station, TX 77843 USA
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47
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Chahar S, Roy PK. COVID-19: A Comprehensive Review of Learning Models. ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING : STATE OF THE ART REVIEWS 2022; 29:1915-1940. [PMID: 34566404 PMCID: PMC8449694 DOI: 10.1007/s11831-021-09641-3] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/13/2021] [Accepted: 08/31/2021] [Indexed: 05/17/2023]
Abstract
Coronavirus disease is communicable and inhibits the infected person's immune system. It belongs to the Coronaviridae family and has affected 213 nations and territories so far. Many kinds of studies are being carried out to filter advice and provide oversight to monitor this outbreak. A comparative and brief review was carried out in this paper on research concerning the early identification of symptoms, estimation of the end of the pandemic, and examination of user-generated conversations. Chest X-ray images, abdominal computed tomography scan, tweets shared on social media are several of the datasets used by researchers. Using machine learning and deep learning methods such as K-means clustering, Random Forest, Convolutional Neural Network, Long Short-Term Memory, Auto-Encoder, and Regression approaches, the above-mentioned datasets are processed. The studies on COVID-19 with machine learning and deep learning models with their results and limitations are outlined in this article. The challenges with open future research directions are discussed at the end.
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Affiliation(s)
- Shivam Chahar
- School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, TN India
| | - Pradeep Kumar Roy
- Department of Computer Science and Engineering, Indian Institute of Information Technology, Surat, Gujarat India
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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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49
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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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50
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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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