Submit to Sensors Review for Sensors Propose a Special Issue

Journal Menu

Journal Browser

► Journal Browser

Modeling COVID-19 with Artificial Intelligence and Machine/Statistical Learning Techniques from Sensor Data and other Potential Applications

Special Issue Editors
Special Issue Information
Keywords
Benefits of Publishing in a Special Issue
Published Papers

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Intelligent Sensors".

Deadline for manuscript submissions: closed (31 December 2021) | Viewed by 50740

Share This Special Issue

Special Issue Editor

Dr. Victor Leiva

E-Mail
Guest Editor

School of Industrial Engineering, Pontificia Universidad Católica de Valparaíso, Avenida Brasil 2241, Valparaíso 2362807, Chile
Interests: machine/statistical learning and modeling; clustering, PLS, and EM algorithm; artificial intelligence, big data, data mining, and data science; influence diagnostics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Interconnected sensors provide large volumes of data that are often valuable in different contexts. In today’s world of digital transformation, various types of sensors and networks reinforce the use of big data science and artificial intelligence. The pandemic of the coronavirus disease SARS-CoV-2 (COVID-19) is providing an avenue for various investigations to transit with the support of this type of data that are generated in the current interconnected world. These data are primarily unstructured and well defined within the context of big data, data science, machine learning, and artificial intelligence. Data from medical images, traceability of infected patients and outbreak areas, mobility in public transport, environmental monitoring, etc., usually georeferenced, are of great interest for the aforementioned investigations. These massive data are generated from various sources, ranging from IoT sensors to social media. For these types of observations, classical methods for structured data analysis are inadequate and insufficient to obtain information and discover relevant knowledge during the COVID-19 pandemic. Consequently, artificial intelligence techniques to process medical images and sentiment analysis to achieve social distancing are, among others, areas of great relevance. Although the focus of this Special Issue is machine learning and statistical modeling for facing the COVID-19 pandemic, we welcome contributions in artificial intelligence, classification, and unsupervised learning, as well as in the topics detailed below. We strongly encourage interdisciplinary works with real data.

This Special Issue invites submissions in, but not limited to, applied data science with potential applications in COVID-19 and emphasis on the following areas:

(i) Artificial intelligence;

(ii) Bayesian methods;

(iii) Big data, dimensionality high, and large-scale data analysis;

(iv) Deep and statistical learning;

(v) Machine learning;

(vi) Evolutionary-based, game-based, physics-based, and swarm-based algorithms, among others;

(vii) Multivariate analysis such as clustering, PCA, and PLS, among others;

(viii) Statistical modeling and its diagnostics.

Prof. Dr. Victor Leiva
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

Artificial intelligence
Artificial neural networks
Big data, big data analytics, and big data science
Biomedical intelligence and clinical data analytics
Bioinformatics, health informatics, and biocomputing
Coronavirus disease, COVID-19, and SARS-CoV-2
Data analytics, data mining, and expert systems
Decision support systems and knowledge discovery in databases
Deep learning, machine learning, and statistical learning
Digital transformation and digitization
Disease spread and social distancing
Image processing and medical imaging
Medical image processing and telemedicine
Monitoring/recognizing/forecasting of emotions and sentiment analysis
Multivariate analysis
Optimization algorithms
Predictive models and analytics using artificial intelligence
Sensor data, sensor networks, smart devices, and IoT applications
Smart city sensors and smart mobility
Statistical analysis/modeling and its diagnostics

Benefits of Publishing in a Special Issue

Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue polices can be found here.

Published Papers (11 papers)

Download All Papers

Order results

Result details

Show export options Show export options

Select all

Export citation of selected articles as:

Research

15 pages, 2416 KiB

Open AccessArticle

Remotely Monitoring COVID-19 Patient Health Condition Using Metaheuristics Convolute Networks from IoT-Based Wearable Device Health Data

by Mustafa Musa Jaber, Thamer Alameri, Mohammed Hasan Ali, Adi Alsyouf, Mohammad Al-Bsheish, Badr K. Aldhmadi, Sarah Yahya Ali, Sura Khalil Abd, Saif Mohammed Ali, Waleed Albaker and Mu’taman Jarrar

Sensors 2022, 22(3), 1205; https://doi.org/10.3390/s22031205 - 5 Feb 2022

Cited by 50 | Viewed by 11522

Abstract

Today, COVID-19-patient health monitoring and management are major public health challenges for technologies. This research monitored COVID-19 patients by using the Internet of Things. IoT-based collected real-time GPS helps alert the patient automatically to reduce risk factors. Wearable IoT devices are attached to the human body, interconnected with edge nodes, to investigate data for making health-condition decisions. This system uses the wearable IoT sensor, cloud, and web layers to explore the patient’s health condition remotely. Every layer has specific functionality in the COVID-19 symptoms’ monitoring process. The first layer collects the patient health information, which is transferred to the second layer that stores that data in the cloud. The network examines health data and alerts the patients, thus helping users take immediate actions. Finally, the web layer notifies family members to take appropriate steps. This optimized deep-learning model allows for the management and monitoring for further analysis. Full article

(This article belongs to the Special Issue Modeling COVID-19 with Artificial Intelligence and Machine/Statistical Learning Techniques from Sensor Data and other Potential Applications)

► Show Figures

Figure 1

19 pages, 1105 KiB

Open AccessArticle

Stance Analysis of Distance Education in the Kingdom of Saudi Arabia during the COVID-19 Pandemic Using Arabic Twitter Data

by Tahani Alqurashi

Sensors 2022, 22(3), 1006; https://doi.org/10.3390/s22031006 - 27 Jan 2022

Cited by 10 | Viewed by 3930

Abstract

The coronavirus has caused significant disruption to people’s everyday lives, altering how people live, work, and study. The Kingdom of Saudi Arabia (KSA) reacted very quickly to suppress the spread of the virus even before the first case of COVID-19 was confirmed in the country. In the education sector, all face-to-face activities at public and private schools and universities were suspended, as they switched from traditional to distance learning for the entire 2020 academic year. This study collected 1,846,285 tweets to analyze the public’s dynamic opinions towards distance education in the KSA during the 2020 academic year. Several classical machine-learning models and deep-learning models, including ensemble random forest (RF), support vector machine (SVM), adaptive boosting (AdaBoost), multinomial naïve Bayes (MNB), convolutional neural network (CNN), and long short-term memory (LSTM), were tested on this data, and the best-performing models were selected to analyze the public stance towards distance education. Additionally, I correlated my analysis with the major events that were announced by the Ministry of Education (MOE). I observed that people in the KSA took some time to react and express their stances at the start of the academic year. Regarding the news, I observed that any exam-related topic attracted high engagement. In-favor stances increased when news headlines covered the topic of exams compared to other topics. The results show that the primary Saudi public stance favored distance education during the 2020 academic year. Full article

(This article belongs to the Special Issue Modeling COVID-19 with Artificial Intelligence and Machine/Statistical Learning Techniques from Sensor Data and other Potential Applications)

► Show Figures

Figure 1

22 pages, 1285 KiB

Open AccessArticle

Predictive Machine Learning Models and Survival Analysis for COVID-19 Prognosis Based on Hematochemical Parameters

by Nicola Altini, Antonio Brunetti, Stefano Mazzoleni, Fabrizio Moncelli, Ilenia Zagaria, Berardino Prencipe, Erika Lorusso, Enrico Buonamico, Giovanna Elisiana Carpagnano, Davide Fiore Bavaro, Mariacristina Poliseno, Annalisa Saracino, Annalisa Schirinzi, Riccardo Laterza, Francesca Di Serio, Alessia D’Introno, Francesco Pesce and Vitoantonio Bevilacqua

Sensors 2021, 21(24), 8503; https://doi.org/10.3390/s21248503 - 20 Dec 2021

Cited by 10 | Viewed by 4207

Abstract

The coronavirus disease 2019 (COVID-19) pandemic has affected hundreds of millions of individuals and caused millions of deaths worldwide. Predicting the clinical course of the disease is of pivotal importance to manage patients. Several studies have found hematochemical alterations in COVID-19 patients, such as inflammatory markers. We retrospectively analyzed the anamnestic data and laboratory parameters of 303 patients diagnosed with COVID-19 who were admitted to the Polyclinic Hospital of Bari during the first phase of the COVID-19 global pandemic. After the pre-processing phase, we performed a survival analysis with Kaplan–Meier curves and Cox Regression, with the aim to discover the most unfavorable predictors. The target outcomes were mortality or admission to the intensive care unit (ICU). Different machine learning models were also compared to realize a robust classifier relying on a low number of strongly significant factors to estimate the risk of death or admission to ICU. From the survival analysis, it emerged that the most significant laboratory parameters for both outcomes was C-reactive protein min;

H R = 17.963

(95% CI 6.548–49.277, p < 0.001) for death,

H R = 1.789

(95% CI 1.000–3.200, p = 0.050) for admission to ICU. The second most important parameter was Erythrocytes max;

H R = 1.765

(95% CI 1.141–2.729, p < 0.05) for death,

H R = 1.481

(95% CI 0.895–2.452, p = 0.127) for admission to ICU. The best model for predicting the risk of death was the decision tree, which resulted in ROC-AUC of 89.66%, whereas the best model for predicting the admission to ICU was support vector machine, which had ROC-AUC of 95.07%. The hematochemical predictors identified in this study can be utilized as a strong prognostic signature to characterize the severity of the disease in COVID-19 patients. Full article

(This article belongs to the Special Issue Modeling COVID-19 with Artificial Intelligence and Machine/Statistical Learning Techniques from Sensor Data and other Potential Applications)

► Show Figures

Figure 1

19 pages, 4039 KiB

Open AccessArticle

Characterization of COVID-19’s Impact on Mobility and Short-Term Prediction of Public Transport Demand in a Mid-Size City in Spain

by Ana Belén Rodríguez González, Mark R. Wilby, Juan José Vinagre Díaz and Rubén Fernández Pozo

Sensors 2021, 21(19), 6574; https://doi.org/10.3390/s21196574 - 30 Sep 2021

Cited by 23 | Viewed by 2934

Abstract

COVID-19 has dramatically struck each section of our society: health, economy, employment, and mobility. This work presents a data-driven characterization of the impact of COVID-19 pandemic on public and private mobility in a mid-size city in Spain (Fuenlabrada). Our analysis used real data collected from the public transport smart card system and a Bluetooth traffic monitoring network, from February to September 2020, thus covering relevant phases of the pandemic. Our results show that, at the peak of the pandemic, public and private mobility dramatically decreased to

95 %

and

86 %

of their pre-COVID-19 values, after which the latter experienced a faster recovery. In addition, our analysis of daily patterns evidenced a clear change in the behavior of users towards mobility during the different phases of the pandemic. Based on these findings, we developed short-term predictors of future public transport demand to provide operators and mobility managers with accurate information to optimize their service and avoid crowded areas. Our prediction model achieved a high performance for pre- and post-state-of-alarm phases. Consequently, this work contributes to enlarging the knowledge about the impact of pandemic on mobility, providing a deep analysis about how it affected each transport mode in a mid-size city. Full article

(This article belongs to the Special Issue Modeling COVID-19 with Artificial Intelligence and Machine/Statistical Learning Techniques from Sensor Data and other Potential Applications)

► Show Figures

Figure 1

20 pages, 745 KiB

Open AccessArticle

Modeling Mortality Based on Pollution and Temperature Using a New Birnbaum–Saunders Autoregressive Moving Average Structure with Regressors and Related-Sensors Data

by Helton Saulo, Rubens Souza, Roberto Vila, Víctor Leiva and Robert G. Aykroyd

Sensors 2021, 21(19), 6518; https://doi.org/10.3390/s21196518 - 29 Sep 2021

Cited by 3 | Viewed by 1845

Abstract

Environmental agencies are interested in relating mortality to pollutants and possible environmental contributors such as temperature. The Gaussianity assumption is often violated when modeling this relationship due to asymmetry and then other regression models should be considered. The class of Birnbaum–Saunders models, especially their regression formulations, has received considerable attention in the statistical literature. These models have been applied successfully in different areas with an emphasis on engineering, environment, and medicine. A common simplification of these models is that statistical dependence is often not considered. In this paper, we propose and derive a time-dependent model based on a reparameterized Birnbaum–Saunders (RBS) asymmetric distribution that allows us to analyze data in terms of a time-varying conditional mean. In particular, it is a dynamic class of autoregressive moving average (ARMA) models with regressors and a conditional RBS distribution (RBSARMAX). By means of a Monte Carlo simulation study, the statistical performance of the new methodology is assessed, showing good results. The asymmetric RBSARMAX structure is applied to the modeling of mortality as a function of pollution and temperature over time with sensor-related data. This modeling provides strong evidence that the new ARMA formulation is a good alternative for dealing with temporal data, particularly related to mortality with regressors of environmental temperature and pollution. Full article

(This article belongs to the Special Issue Modeling COVID-19 with Artificial Intelligence and Machine/Statistical Learning Techniques from Sensor Data and other Potential Applications)

► Show Figures

Figure 1

17 pages, 9431 KiB

Open AccessArticle

Automated Triage System for Intensive Care Admissions during the COVID-19 Pandemic Using Hybrid XGBoost-AHP Approach

by Mohanad A. Deif, Ahmed A. A. Solyman, Mohammed H. Alsharif and Peerapong Uthansakul

Sensors 2021, 21(19), 6379; https://doi.org/10.3390/s21196379 - 24 Sep 2021

Cited by 31 | Viewed by 2833

Abstract

The sudden increase in patients with severe COVID-19 has obliged doctors to make admissions to intensive care units (ICUs) in health care practices where capacity is exceeded by the demand. To help with difficult triage decisions, we proposed an integration system Xtreme Gradient Boosting (XGBoost) classifier and Analytic Hierarchy Process (AHP) to assist health authorities in identifying patients’ priorities to be admitted into ICUs according to the findings of the biological laboratory investigation for patients with COVID-19. The Xtreme Gradient Boosting (XGBoost) classifier was used to decide whether or not they should admit patients into ICUs, before applying them to an AHP for admissions’ priority ranking for ICUs. The 38 commonly used clinical variables were considered and their contributions were determined by the Shapley’s Additive explanations (SHAP) approach. In this research, five types of classifier algorithms were compared: Support Vector Machine (SVM), Decision Tree (DT), K-Nearest Neighborhood (KNN), Random Forest (RF), and Artificial Neural Network (ANN), to evaluate the XGBoost performance, while the AHP system compared its results with a committee formed from experienced clinicians. The proposed (XGBoost) classifier achieved a high prediction accuracy as it could discriminate between patients with COVID-19 who need ICU admission and those who do not with accuracy, sensitivity, and specificity rates of 97%, 96%, and 96% respectively, while the AHP system results were close to experienced clinicians’ decisions for determining the priority of patients that need to be admitted to the ICU. Eventually, medical sectors can use the suggested framework to classify patients with COVID-19 who require ICU admission and prioritize them based on integrated AHP methodologies. Full article

(This article belongs to the Special Issue Modeling COVID-19 with Artificial Intelligence and Machine/Statistical Learning Techniques from Sensor Data and other Potential Applications)

► Show Figures

Figure 1

16 pages, 1028 KiB

Open AccessArticle

A New Approach to Predicting Cryptocurrency Returns Based on the Gold Prices with Support Vector Machines during the COVID-19 Pandemic Using Sensor-Related Data

by Esam Mahdi, Víctor Leiva, Saed Mara’Beh and Carlos Martin-Barreiro

Sensors 2021, 21(18), 6319; https://doi.org/10.3390/s21186319 - 21 Sep 2021

Cited by 29 | Viewed by 4964

Abstract

In a real-world situation produced under COVID-19 scenarios, predicting cryptocurrency returns accurately can be challenging. Such a prediction may be helpful to the daily economic and financial market. Unlike forecasting the cryptocurrency returns, we propose a new approach to predict whether the return classification would be in the first, second, third quartile, or any quantile of the gold price the next day. In this paper, we employ the support vector machine (SVM) algorithm for exploring the predictability of financial returns for the six major digital currencies selected from the list of top ten cryptocurrencies based on data collected through sensors. These currencies are Binance Coin, Bitcoin, Cardano, Dogecoin, Ethereum, and Ripple. Our study considers the pre-COVID-19 and ongoing COVID-19 periods. An algorithm that allows updated data analysis, based on the use of a sensor in the database, is also proposed. The results show strong evidence that the SVM is a robust technique for devising profitable trading strategies and can provide accurate results before and during the current pandemic. Our findings may be helpful for different stakeholders in understanding the cryptocurrency dynamics and in making better investment decisions, especially under adverse conditions and during times of uncertain environments such as in the COVID-19 pandemic. Full article

(This article belongs to the Special Issue Modeling COVID-19 with Artificial Intelligence and Machine/Statistical Learning Techniques from Sensor Data and other Potential Applications)

► Show Figures

Figure 1

20 pages, 10253 KiB

Open AccessArticle

Sentiment Analysis of Arabic Tweets Regarding Distance Learning in Saudi Arabia during the COVID-19 Pandemic

by Malak Aljabri, Sara Mhd. Bachar Chrouf, Norah A. Alzahrani, Leena Alghamdi, Reem Alfehaid, Reem Alqarawi, Jawaher Alhuthayfi and Nouf Alduhailan

Sensors 2021, 21(16), 5431; https://doi.org/10.3390/s21165431 - 11 Aug 2021

Cited by 44 | Viewed by 6642

Abstract

The COVID-19 pandemic has greatly impacted the normal life of people worldwide. One of the most noticeable impacts is the enforcement of social distancing to reduce the spread of the virus. The Ministry of Education in Saudi Arabia implemented social distancing measures by enforcing distance learning at all educational stages. This measure brought about new experiences and challenges to students, parents, and teachers. This research measures the acceptance rate of this way of learning by analysing people’s tweets regarding distance learning in Saudi Arabia. All the tweets analysed were written in Arabic and collected within the boundary of Saudi Arabia. They date back to the day that the distance learning announcement was made. The tweets were pre-processed, and labelled positive, or negative. Machine learning classifiers with different features and extraction techniques were then built to analyse the sentiment. The accuracy results for the different models were then compared. The best accuracy achieved (0.899) resulted from the Logistic regression classifier with unigram and Term Frequency-Inverse Document Frequency as a feature extraction approach. This model was then applied on a new unlabelled dataset and classified to different educational stages; results demonstrated generally positive opinions regarding distance learning for general education stages (kindergarten, intermediate, and high schools), and negative opinions for the university stage. Further analysis was applied to identify the main topics related to the positive and negative sentiment. This result can be used by the Ministry of Education to further improve the distance learning educational system. Full article

(This article belongs to the Special Issue Modeling COVID-19 with Artificial Intelligence and Machine/Statistical Learning Techniques from Sensor Data and other Potential Applications)

► Show Figures

Figure 1

17 pages, 487 KiB

Open AccessArticle

A Two-Stage Location Problem with Order Solved Using a Lagrangian Algorithm and Stochastic Programming for a Potential Use in COVID-19 Vaccination Based on Sensor-Related Data

by Xavier Cabezas, Sergio García, Carlos Martin-Barreiro, Erwin Delgado and Víctor Leiva

Sensors 2021, 21(16), 5352; https://doi.org/10.3390/s21165352 - 9 Aug 2021

Cited by 18 | Viewed by 3357

Abstract

Healthcare service centers must be sited in strategic locations that meet the immediate needs of patients. The current situation due to the COVID-19 pandemic makes this problem particularly relevant. Assume that each center corresponds to an assigned place for vaccination and that each center uses one or more vaccine brands/laboratories. Then, each patient could choose a center instead of another, because she/he may prefer the vaccine from a more reliable laboratory. This defines an order of preference that might depend on each patient who may not want to be vaccinated in a center where there are only her/his non-preferred vaccine brands. In countries where the vaccination process is considered successful, the order assigned by each patient to the vaccination centers is defined by incentives that local governments give to their population. These same incentives for foreign citizens are seen as a strategic decision to generate income from tourism. The simple plant/center location problem (SPLP) is a combinatorial approach that has been extensively studied. However, a less-known natural extension of it with order (SPLPO) has not been explored in the same depth. In this case, the size of the instances that can be solved is limited. The SPLPO considers an order of preference that patients have over a set of facilities to meet their demands. This order adds a new set of constraints in its formulation that increases the complexity of the problem to obtain an optimal solution. In this paper, we propose a new two-stage stochastic formulation for the SPLPO (2S-SPLPO) that mimics the mentioned pandemic situation, where the order of preference is treated as a random vector. We carry out computational experiments on simulated 2S-SPLPO instances to evaluate the performance of the new proposal. We apply an algorithm based on Lagrangian relaxation that has been shown to be efficient for large instances of the SPLPO. A potential application of this new algorithm to COVID-19 vaccination is discussed and explored based on sensor-related data. Two further algorithms are proposed to store the patient’s records in a data warehouse and generate 2S-SPLPO instances using sensors. Full article

(This article belongs to the Special Issue Modeling COVID-19 with Artificial Intelligence and Machine/Statistical Learning Techniques from Sensor Data and other Potential Applications)

► Show Figures

Figure 1

19 pages, 2829 KiB

Open AccessArticle

Lot-Size Models with Uncertain Demand Considering Its Skewness/Kurtosis and Stochastic Programming Applied to Hospital Pharmacy with Sensor-Related COVID-19 Data

by Fernando Rojas, Víctor Leiva, Mauricio Huerta and Carlos Martin-Barreiro

Sensors 2021, 21(15), 5198; https://doi.org/10.3390/s21155198 - 31 Jul 2021

Cited by 17 | Viewed by 3159

Abstract

Governments have been challenged to provide timely medical care to face the COVID-19 pandemic. Under this pandemic, the demand for pharmaceutical products has changed significantly. Some of these products are in high demand, while, for others, their demand falls sharply. These changes in the random demand patterns are connected with changes in the skewness (asymmetry) and kurtosis of their data distribution. Such changes are critical to determining optimal lots and inventory costs. The lot-size model helps to make decisions based on probabilistic demand when calculating the optimal costs of supply using two-stage stochastic programming. The objective of this study is to evaluate how the skewness and kurtosis of the distribution of demand data, collected through sensors, affect the modeling of inventories of hospital pharmacy products helpful to treat COVID-19. The use of stochastic programming allows us to obtain results under demand uncertainty that are closer to reality. We carry out a simulation study to evaluate the performance of our methodology under different demand scenarios with diverse degrees of skewness and kurtosis. A case study in the field of hospital pharmacy with sensor-related COVID-19 data is also provided. An algorithm that permits us to use sensors when submitting requests for supplying pharmaceutical products in the hospital treatment of COVID-19 is designed. We show that the coefficients of skewness and kurtosis impact the total costs of inventory that involve order, purchase, holding, and shortage. We conclude that the asymmetry and kurtosis of the demand statistical distribution do not seem to affect the first-stage lot-size decisions. However, demand patterns with high positive skewness are related to significant increases in expected inventories on hand and shortage, increasing the costs of second-stage decisions. Thus, demand distributions that are highly asymmetrical to the right and leptokurtic favor high total costs in probabilistic lot-size systems. Full article

(This article belongs to the Special Issue Modeling COVID-19 with Artificial Intelligence and Machine/Statistical Learning Techniques from Sensor Data and other Potential Applications)

► Show Figures

Figure 1

17 pages, 829 KiB

Open AccessArticle

Disjoint and Functional Principal Component Analysis for Infected Cases and Deaths Due to COVID-19 in South American Countries with Sensor-Related Data

by Carlos Martin-Barreiro, John A. Ramirez-Figueroa, Xavier Cabezas, Víctor Leiva and M. Purificación Galindo-Villardón

Sensors 2021, 21(12), 4094; https://doi.org/10.3390/s21124094 - 14 Jun 2021

Cited by 28 | Viewed by 3496

Abstract

In this paper, we group South American countries based on the number of infected cases and deaths due to COVID-19. The countries considered are: Argentina, Bolivia, Brazil, Chile, Colombia, Ecuador, Peru, Paraguay, Uruguay, and Venezuela. The data used are collected from a database of Johns Hopkins University, an institution that is dedicated to sensing and monitoring the evolution of the COVID-19 pandemic. A statistical analysis, based on principal components with modern and recent techniques, is conducted. Initially, utilizing the correlation matrix, standard components and varimax rotations are calculated. Then, by using disjoint components and functional components, the countries are grouped. An algorithm that allows us to keep the principal component analysis updated with a sensor in the data warehouse is designed. As reported in the conclusions, this grouping changes depending on the number of components considered, the type of principal component (standard, disjoint or functional) and the variable to be considered (infected cases or deaths). The results obtained are compared to the k-means technique. The COVID-19 cases and their deaths vary in the different countries due to diverse reasons, as reported in the conclusions. Full article

(This article belongs to the Special Issue Modeling COVID-19 with Artificial Intelligence and Machine/Statistical Learning Techniques from Sensor Data and other Potential Applications)

► Show Figures

Journal Menu

Journal Browser

Modeling COVID-19 with Artificial Intelligence and Machine/Statistical Learning Techniques from Sensor Data and other Potential Applications

Share This Special Issue

Special Issue Editor

Special Issue Information

Keywords

Benefits of Publishing in a Special Issue

Published Papers (11 papers)

Research

Further Information

Guidelines

MDPI Initiatives

Follow MDPI