Outbreak Prediction of COVID-19 for Dense and Populated Countries Using Machine Learning

Khakharia, Aman; Shah, Vruddhi; Jain, Sankalp; Shah, Jash; Tiwari, Amanshu; Daphal, Prathamesh; Warang, Mahesh; Mehendale, Ninad

doi:10.1007/s40745-020-00314-9

Cited by 56 publications

(43 citation statements)

References 16 publications

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“…A set of models using 9 different machine learning algorithms for predicting the rise in new cases, having an average accuracy of 87.9 ± 3.9%, was developed for 10 high population and high-density countries. The highest accuracy of 99.93% was achieved for Ethiopia using ARMA averaged over the next 5 days (Khakharia et al 2020). Not every machine learning algorithm could give a very high accuracy for predicting the cases for each country.…”

Section: Compared With Existing Covid Prediction Modelsmentioning

confidence: 99%

Prediction of the confirmed cases and deaths of global COVID-19 using artificial intelligence

Guo

2021

Environ Sci Pollut Res

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Section: Compared With Existing Covid Prediction Modelsmentioning

confidence: 99%

Prediction of the confirmed cases and deaths of global COVID-19 using artificial intelligence

Guo

2021

Environ Sci Pollut Res

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“…Kumar [ 33 ] applied cluster analysis to study and improve the monitoring of SARS-CoV-2 infections in India, providing insights on clusters of affected Indian states and union territories. Besides aiming to improve the management of available resources, Khakharia et al [ 34 ] developed outbreak classification models for COVID-19 using data sets with information about patients who live in India, Bangladesh, the Democratic Republic of Congo, Pakistan, China, Philippines, Germany, Indonesia, Ethiopia, and Nigeria. Vaid et al [ 35 ] implemented and validated models (eg, XGBoost) to predict mortality and critical events using electronic health records of patients who tested positive for COVID-19 in New York City.…”

Section: Discussionmentioning

confidence: 99%

Classification Models for COVID-19 Test Prioritization in Brazil: Machine Learning Approach

Santana¹,

Silveira²,

Sobrinho³

et al. 2021

J Med Internet Res

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Background Controlling the COVID-19 outbreak in Brazil is a challenge due to the population’s size and urban density, inefficient maintenance of social distancing and testing strategies, and limited availability of testing resources. Objective The purpose of this study is to effectively prioritize patients who are symptomatic for testing to assist early COVID-19 detection in Brazil, addressing problems related to inefficient testing and control strategies. Methods Raw data from 55,676 Brazilians were preprocessed, and the chi-square test was used to confirm the relevance of the following features: gender, health professional, fever, sore throat, dyspnea, olfactory disorders, cough, coryza, taste disorders, and headache. Classification models were implemented relying on preprocessed data sets; supervised learning; and the algorithms multilayer perceptron (MLP), gradient boosting machine (GBM), decision tree (DT), random forest (RF), extreme gradient boosting (XGBoost), k-nearest neighbors (KNN), support vector machine (SVM), and logistic regression (LR). The models’ performances were analyzed using 10-fold cross-validation, classification metrics, and the Friedman and Nemenyi statistical tests. The permutation feature importance method was applied for ranking the features used by the classification models with the highest performances. Results Gender, fever, and dyspnea were among the highest-ranked features used by the classification models. The comparative analysis presents MLP, GBM, DT, RF, XGBoost, and SVM as the highest performance models with similar results. KNN and LR were outperformed by the other algorithms. Applying the easy interpretability as an additional comparison criterion, the DT was considered the most suitable model. Conclusions The DT classification model can effectively (with a mean accuracy≥89.12%) assist COVID-19 test prioritization in Brazil. The model can be applied to recommend the prioritizing of a patient who is symptomatic for COVID-19 testing.

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“…Among ML techniques, artificial neural network (ANN) outperformed adaptive neuro-fuzzy inference system (ANFIS) [ 10 ]. 9 different ML algorithms were employed to estimate the new cases of COVID-19 outbreak in 10 densely populated countries worldwide to find the best-fitted model for each country [ 11 ]. The autoregressive integrated moving average (ARIMA) and least square support vector machine (LS-SVM) models were employed to predict the confirmed cases of COVID-19 in the five countries of the world.…”

Section: Literature Reviewmentioning

confidence: 99%

A numerical approach to maximize the number of testing of COVID-19 using conditional cluster sampling method

Zoha

Ghosh

Arif-Ul-Islam

et al. 2021

Informatics in Medicine Unlocked

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The COVID-19 pandemic is the defining health crisis of the world in 2020 and the world economy is affected as well. Bangladesh is also one of the impacted countries, which needs to conduct sufficient tests to identify patients and accordingly adopt measures to limit the massive outbreak of this viral infection. But due to economic drawbacks and also unavailability of testing equipment, Bangladesh is lagging critically behind in test numbers. This study shows a pool testing method named Conditional Cluster Sampling (CCS) that utilizes soft computing and data analysis techniques to reduce the expense of total testing equipment. The proposed method also demonstrates its effectiveness compared to the traditional individual testing method. Firstly, according to patients’ symptoms and severity of their conditions, they are classified into four classes- Minor, Moderate, Major, Critical. After that Random Forest Classifier (RFC) is used to predict the class. Then random sampling is done from each class according to CCS. Finally, using Monte Carlo Simulation (MCS) for 100 cycles, the effectiveness of CCS is demonstrated for different probability levels of infection. It is shown that the CCS method can save up to 22% of the test kits that can save a huge amount of money as well as testing time.

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Outbreak Prediction of COVID-19 for Dense and Populated Countries Using Machine Learning

Cited by 56 publications

References 16 publications

Prediction of the confirmed cases and deaths of global COVID-19 using artificial intelligence

Prediction of the confirmed cases and deaths of global COVID-19 using artificial intelligence

Classification Models for COVID-19 Test Prioritization in Brazil: Machine Learning Approach

A numerical approach to maximize the number of testing of COVID-19 using conditional cluster sampling method

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