Analysis and Prediction of COVID-19 Using SIR, SEIQR, and Machine Learning Models: Australia, Italy, and UK Cases

The novel coronavirus disease, also known as COVID-19, is a disease outbreak that was first identified in Wuhan, a Central Chinese city. In this report, a short analysis focusing on Australia, Italy, and UK is conducted. The analysis includes confirmed and recovered cases and deaths, the growth rate in Australia compared with that in Italy and UK, and the trend of the disease in different Australian regions. Mathematical approaches based on susceptible, infected, and recovered (SIR) cases and susceptible, exposed, infected, quarantined, and recovered (SEIQR) cases models are proposed to predict epidemiology in the above-mentioned countries. Since the performance of the classic forms of SIR and SEIQR depends on parameter settings, some optimization algorithms, namely Broyden–Fletcher–Goldfarb–Shanno (BFGS), conjugate gradients (CG), limited memory bound constrained BFGS (L-BFGS-B), and Nelder–Mead, are proposed to optimize the parameters and the predictive capabilities of the SIR and SEIQR models. The results of the optimized SIR and SEIQR models were compared with those of two well-known machine learning algorithms, i.e., the Prophet algorithm and logistic function. The results demonstrate the different behaviors of these algorithms in different countries as well as the better performance of the improved SIR and SEIQR models. Moreover, the Prophet algorithm was found to provide better prediction performance than the logistic function, as well as better prediction performance for Italy and UK cases than for Australian cases. Therefore, it seems that the Prophet algorithm is suitable for data with an increasing trend in the context of a pandemic. Optimization of SIR and SEIQR model parameters yielded a significant improvement in the prediction accuracy of the models. Despite the availability of several algorithms for trend predictions in this pandemic, there is no single algorithm that would be optimal for all cases.

Download Full-text

Analysis and Prediction of COVID-19 using SIR, SEIR, and Machine Learning Models: Australia, Italy, and UK Cases

10.21203/rs.3.rs-85513/v1 ◽

2020 ◽

Author(s):

Iman Rahimi ◽

Amir H Gandomi ◽

Fang Chen

Keyword(s):

Machine Learning ◽

United Kingdom ◽

Prediction Accuracy ◽

Logistic Function ◽

Learning Models ◽

The United Kingdom ◽

Increasing Trend ◽

Classic Form ◽

Novel Coronavirus ◽

Machine Learning Models

Abstract The novel Coronavirus disease, known as COVID-19, is an outbreak that started in Wuhan, one of the Central Chinese cities. In this report, a short analysis focusing on Australia, Italy, and the United Kingdom has been conducted. The analysis includes confirmed and recovered cases and deaths, the growth rate in Australia as compared with Italy and the United Kingdom, and the outbreak in different Australian cities. Mathematical approaches based on the susceptible, infected, and recovered case (SIR) and susceptible, exposed, infected, and recovered (SEIR) models were proposed to predict the epidemiology in the countries. Since the performance of the classic form of SIR and SEIR depends on parameter settings, some optimization algorithms, namely, the Broyden–Fletcher–Goldfarb–Shanno (BFGS), conjugate gradients (CG), L-BFGS-B, and Nelder-Mead are proposed to optimize the parameters of SIR and SEIR models and improve its predictive capabilities. The results of optimized SIR and SEIR models are compared with the Prophet algorithm and logistic function as two known ML algorithms. The results show that different algorithms display different behaviours in different countries. However, the improved version of the SIR and SEIR models have a better performance compared with other mentioned algorithms described in this study. Moreover, the Prophet algorithm works better for Italy and the United Kingdom cases than for Australian cases and Logistic function compared with Prophet algorithm has a better performance in these cases. It seems that Prophet algorithm is suitable for data with increasing trend in pandemic situations. Optimization of the SIR and SEIR models parameters has yielded a significant improvement in the prediction accuracy of the models. Although there are several algorithms for prediction of this Pandemic, there is no certain algorithm that would be the best one for all cases.

Download Full-text

Prediction of Healing Performance of Autogenous Healing Concrete Using Machine Learning

Materials ◽

10.3390/ma14154068 ◽

2021 ◽

Vol 14 (15) ◽

pp. 4068

Author(s):

Xu Huang ◽

Mirna Wasouf ◽

Jessada Sresakoolchai ◽

Sakdirat Kaewunruen

Keyword(s):

Machine Learning ◽

Search Algorithm ◽

Weather Conditions ◽

Prediction Performance ◽

Machine Learning Algorithms ◽

Coefficient Of Determination ◽

Gradient Boosting ◽

Support Vector ◽

Self Healing ◽

Artificial Neural Network Ann

Cracks typically develop in concrete due to shrinkage, loading actions, and weather conditions; and may occur anytime in its life span. Autogenous healing concrete is a type of self-healing concrete that can automatically heal cracks based on physical or chemical reactions in concrete matrix. It is imperative to investigate the healing performance that autogenous healing concrete possesses, to assess the extent of the cracking and to predict the extent of healing. In the research of self-healing concrete, testing the healing performance of concrete in a laboratory is costly, and a mass of instances may be needed to explore reliable concrete design. This study is thus the world’s first to establish six types of machine learning algorithms, which are capable of predicting the healing performance (HP) of self-healing concrete. These algorithms involve an artificial neural network (ANN), a k-nearest neighbours (kNN), a gradient boosting regression (GBR), a decision tree regression (DTR), a support vector regression (SVR) and a random forest (RF). Parameters of these algorithms are tuned utilising grid search algorithm (GSA) and genetic algorithm (GA). The prediction performance indicated by coefficient of determination (R2) and root mean square error (RMSE) measures of these algorithms are evaluated on the basis of 1417 data sets from the open literature. The results show that GSA-GBR performs higher prediction performance (R2GSA-GBR = 0.958) and stronger robustness (RMSEGSA-GBR = 0.202) than the other five types of algorithms employed to predict the healing performance of autogenous healing concrete. Therefore, reliable prediction accuracy of the healing performance and efficient assistance on the design of autogenous healing concrete can be achieved.

Download Full-text

Modified Decision Tree Technique for Ransomware Detection at Runtime through API Calls

Scientific Programming ◽

10.1155/2020/8845833 ◽

2020 ◽

Vol 2020 ◽

pp. 1-10

Author(s):

Faizan Ullah ◽

Qaisar Javaid ◽

Abdu Salam ◽

Masood Ahmad ◽

Nadeem Sarwar ◽

...

Keyword(s):

Machine Learning ◽

Random Forest ◽

Decision Tree ◽

Feature Vector ◽

Machine Learning Algorithms ◽

The Novel ◽

Proposed Model ◽

Testing Accuracy ◽

Financial Losses

Ransomware (RW) is a distinctive variety of malware that encrypts the files or locks the user’s system by keeping and taking their files hostage, which leads to huge financial losses to users. In this article, we propose a new model that extracts the novel features from the RW dataset and performs classification of the RW and benign files. The proposed model can detect a large number of RW from various families at runtime and scan the network, registry activities, and file system throughout the execution. API-call series was reutilized to represent the behavior-based features of RW. The technique extracts fourteen-feature vector at runtime and analyzes it by applying online machine learning algorithms to predict the RW. To validate the effectiveness and scalability, we test 78550 recent malign and benign RW and compare with the random forest and AdaBoost, and the testing accuracy is extended at 99.56%.

Download Full-text

PERFORMANCE COMPARISON OF MACHINE LEARNING ALGORITHMS FOR PREDICTIVE MAINTENANCE

Informatyka Automatyka Pomiary w Gospodarce i Ochronie Środowiska ◽

10.35784/iapgos.1834 ◽

2020 ◽

Vol 10 (3) ◽

pp. 32-35

Author(s):

Jakub Gęca

Keyword(s):

Machine Learning ◽

Learning Algorithms ◽

Performance Comparison ◽

Machine Learning Algorithms ◽

Predictive Maintenance ◽

Model Parameters ◽

Data Set ◽

Reduction Techniques ◽

Machine Reliability ◽

Dimensionality Reduction Techniques

The consequences of failures and unscheduled maintenance are the reasons why engineers have been trying to increase the reliability of industrial equipment for years. In modern solutions, predictive maintenance is a frequently used method. It allows to forecast failures and alert about their possibility. This paper presents a summary of the machine learning algorithms that can be used in predictive maintenance and comparison of their performance. The analysis was made on the basis of data set from Microsoft Azure AI Gallery. The paper presents a comprehensive approach to the issue including feature engineering, preprocessing, dimensionality reduction techniques, as well as tuning of model parameters in order to obtain the highest possible performance. The conducted research allowed to conclude that in the analysed case , the best algorithm achieved 99.92% accuracy out of over 122 thousand test data records. In conclusion, predictive maintenance based on machine learning represents the future of machine reliability in industry.

Download Full-text

Monitoring the Impact of Air Quality on the COVID-19 Fatalities in Delhi, India: Using Machine Learning Techniques

Disaster Medicine and Public Health Preparedness ◽

10.1017/dmp.2020.372 ◽

2020 ◽

pp. 1-8

Author(s):

Jasleen Kaur Sethi ◽

Mamta Mittal

Keyword(s):

Machine Learning ◽

Air Quality ◽

Air Pollutants ◽

Machine Learning Techniques ◽

Environmental Restoration ◽

The Novel ◽

Ozone Pollution ◽

Learning Techniques ◽

Novel Coronavirus ◽

The Impact

ABSTRACT Objective: The focus of this study is to monitor the effect of lockdown on the various air pollutants due to the coronavirus disease (COVID-19) pandemic and identify the ones that affect COVID-19 fatalities so that measures to control the pollution could be enforced. Methods: Various machine learning techniques: Decision Trees, Linear Regression, and Random Forest have been applied to correlate air pollutants and COVID-19 fatalities in Delhi. Furthermore, a comparison between the concentration of various air pollutants and the air quality index during the lockdown period and last two years, 2018 and 2019, has been presented. Results: From the experimental work, it has been observed that the pollutants ozone and toluene have increased during the lockdown period. It has also been deduced that the pollutants that may impact the mortalities due to COVID-19 are ozone, NH3, NO2, and PM10. Conclusions: The novel coronavirus has led to environmental restoration due to lockdown. However, there is a need to impose measures to control ozone pollution, as there has been a significant increase in its concentration and it also impacts the COVID-19 mortality rate.

Download Full-text

Prediction of Damage Accumulation Effect of Wood Structural Members under Long-Term Service: A Machine Learning Approach

Materials ◽

10.3390/ma12081243 ◽

2019 ◽

Vol 12 (8) ◽

pp. 1243

Author(s):

Zheng Li ◽

Duo Tao ◽

Mengwei Li ◽

Zhan Shu ◽

Songshi Jing ◽

...

Keyword(s):

Machine Learning ◽

Damage Accumulation ◽

Goodness Of Fit ◽

Machine Learning Algorithms ◽

Cycle Performance ◽

Model Parameters ◽

Timber Structures ◽

Structural Members ◽

Accumulation Effect

It is well known that wood structural members can stand a relatively heavy load in the short term but will gradually get weaker if the load is applied for a longer period. This phenomenon is caused by the damage accumulation effect in wood and should be appropriately considered during the design of timber structures. Although various formulation methods (also known as classical models) have been proposed to evaluate the damage accumulation effect in wood, the calibration of model parameters is very time-consuming. Our work proposes a novel method to deal with the damage accumulation effect in wood that involves the application of machine learning algorithms. The proposed algorithm considers a multi-objective optimization process with a combination of goodness-of-fit and complexity. Long-term experimental data of typical wood species are used for developing the machine learning based damage accumulation model. Compared with existing pre-formulated models, our model managed to reduce the complexity of the model structure and give sufficiently accurate and unbiased predictions. This study aims to provide a novel tool for evaluating the damage accumulation in wood structural members, and the proposed model can further support the life-cycle performance assessment of timber structures under long-term service scenarios.

Download Full-text

The potential of machine learning for weather index insurance

Natural Hazards and Earth System Science ◽

10.5194/nhess-21-2379-2021 ◽

2021 ◽

Vol 21 (8) ◽

pp. 2379-2405

Author(s):

Luigi Cesarini ◽

Rui Figueiredo ◽

Beatrice Monteleone ◽

Mario L. V. Martina

Keyword(s):

Machine Learning ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Support Vector ◽

Index Insurance ◽

Model Parameters ◽

Classification Problems ◽

Weather Index ◽

Weather Index Insurance ◽

Extreme Flood

Abstract. Weather index insurance is an innovative tool in risk transfer for disasters induced by natural hazards. This paper proposes a methodology that uses machine learning algorithms for the identification of extreme flood and drought events aimed at reducing the basis risk connected to this kind of insurance mechanism. The model types selected for this study were the neural network and the support vector machine, vastly adopted for classification problems, which were built exploring thousands of possible configurations based on the combination of different model parameters. The models were developed and tested in the Dominican Republic context, based on data from multiple sources covering a time period between 2000 and 2019. Using rainfall and soil moisture data, the machine learning algorithms provided a strong improvement when compared to logistic regression models, used as a baseline for both hazards. Furthermore, increasing the amount of information provided during the training of the models proved to be beneficial to the performances, increasing their classification accuracy and confirming the ability of these algorithms to exploit big data and their potential for application within index insurance products.

Download Full-text

Saturation Modeling of Gas Hydrate Using Machine Learning with X-Ray CT Images

Energies ◽

10.3390/en13195032 ◽

2020 ◽

Vol 13 (19) ◽

pp. 5032

Author(s):

Sungil Kim ◽

Kyungbook Lee ◽

Minhui Lee ◽

Taewoong Ahn ◽

Jaehyoung Lee ◽

...

Keyword(s):

Machine Learning ◽

Gas Hydrate ◽

Ct Images ◽

Prediction Performance ◽

Machine Learning Algorithms ◽

Support Vector ◽

Sand Sample ◽

X Ray ◽

Physical Conditions ◽

Water Gas

This study conducts saturation modeling in a gas hydrate (GH) sand sample with X-ray CT images using the following machine learning algorithms: random forest (RF), convolutional neural network (CNN), and support vector machine (SVM). The RF yields the best prediction performance for water, gas, and GH saturation in the samples among the three methods. The CNN and SVM also exhibit sufficient performances under the restricted conditions, but require improvements to their reliability and overall prediction performance. Furthermore, the RF yields the lowest mean square error and highest correlation coefficient between the original and predicted datasets. Although the GH CT images aid in approximately understanding how fluids act in a GH sample, difficulties were encountered in accurately understanding the behavior of GH in a GH sample during the experiments owing to limited physical conditions. Therefore, the proposed saturation modeling method can aid in understanding the behavior of GH in a GH sample in real-time with the use of an appropriate machine learning method. Furthermore, highly accurate descriptions of each saturation, obtained from the proposed method, lead to an accurate resource evaluation and well-guided optimal depressurization for a target GH field production.

Download Full-text

How Does the Novel Coronavirus Kill? A Machine Learning Approach

SSRN Electronic Journal ◽

10.2139/ssrn.3618304 ◽

2020 ◽

Author(s):

Logan Ryan ◽

Huaqin Pan ◽

Samson Mataraso ◽

Anna Lynn-Palevsky ◽

Emily Pellegrini ◽

...

Keyword(s):

Machine Learning ◽

Learning Approach ◽

The Novel ◽

Machine Learning Approach ◽

Novel Coronavirus

Download Full-text

Shape-based Machine Learning Models for the Potential Novel COVID-19 Protease Inhibitors Assisted by Molecular Dynamics Simulation

Current Topics in Medicinal Chemistry ◽

10.2174/1568026620666200704135327 ◽

2020 ◽

Vol 20 (24) ◽

pp. 2146-2167 ◽

Cited By ~ 1

Author(s):

Anuraj Nayarisseri ◽

Ravina Khandelwal ◽

Maddala Madhavi ◽

Chandrabose Selvaraj ◽

Umesh Panwar ◽

...

Keyword(s):

Machine Learning ◽

Molecular Dynamics ◽

Protease Inhibitors ◽

Dynamics Simulation ◽

The Novel ◽

Public Health Services ◽

Dynamic Simulations ◽

Disease Patterns ◽

Novel Coronavirus ◽

Dynamics Simulations

Background: The vast geographical expansion of novel coronavirus and an increasing number of COVID-19 affected cases have overwhelmed health and public health services. Artificial Intelligence (AI) and Machine Learning (ML) algorithms have extended their major role in tracking disease patterns, and in identifying possible treatments. Objective: This study aims to identify potential COVID-19 protease inhibitors through shape-based Machine Learning assisted by Molecular Docking and Molecular Dynamics simulations. Methods: 31 Repurposed compounds have been selected targeting the main coronavirus protease (6LU7) and a machine learning approach was employed to generate shape-based molecules starting from the 3D shape to the pharmacophoric features of their seed compound. Ligand-Receptor Docking was performed with Optimized Potential for Liquid Simulations (OPLS) algorithms to identify highaffinity compounds from the list of selected candidates for 6LU7, which were subjected to Molecular Dynamic Simulations followed by ADMET studies and other analyses. Results: Shape-based Machine learning reported remdesivir, valrubicin, aprepitant, and fulvestrant as the best therapeutic agents with the highest affinity for the target protein. Among the best shape-based compounds, a novel compound identified was not indexed in any chemical databases (PubChem, Zinc, or ChEMBL). Hence, the novel compound was named 'nCorv-EMBS'. Further, toxicity analysis showed nCorv-EMBS to be suitable for further consideration as the main protease inhibitor in COVID-19. Conclusion: Effective ACE-II, GAK, AAK1, and protease 3C blockers can serve as a novel therapeutic approach to block the binding and attachment of the main COVID-19 protease (PDB ID: 6LU7) to the host cell and thus inhibit the infection at AT2 receptors in the lung. The novel compound nCorv- EMBS herein proposed stands as a promising inhibitor to be evaluated further for COVID-19 treatment.

Download Full-text