Monitoring Novel Corona Virus (COVID-19) Infections in India by Cluster Analysis

Kumar, Sanjay

doi:10.1007/s40745-020-00289-7

Cited by 63 publications

(48 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The hierarchical analysis of clusters was performed to display relations using the observations gathered from the three types of cases of Coronavirus 2019 of Indian states and UTs. The cluster analysis categorized 27 states and 5 UTs into six clusters (I-VI) of affected cases with COVID-19 [26].…”

Section: Literature Reviewmentioning

confidence: 99%

The Role of Machine Learning to Fight COVID-19

Ouf¹,

Hamza²

2021

IJIES

View full text Add to dashboard Cite

Objectives: this paper aimed to first, exploring the relationship between multiple physical measurements. Second, predicting the treatment course of hospitalized patients with COVID-19 disease from physical measurements. Third, investigating the primary symptoms and the average duration of each symptom's disappearance. Fourth, provide the physicians with the prediction model to help them determine the best combination of drugs for Covid-19 patients'. Methods: this paper first, apply correlation analysis on the dataset to identify the relationships between the dataset attributes and selecting model features more efficiently to improve the accuracy results. Second, implements seven machine learning algorithms with four cross-validation techniques to predict the most appropriate treatment course for COVID-19 hospitalized patients. Two treatment courses were identified; course-LR was patients treated with Lopinavir-ritonavir and course LR + AR were patients treated with Lopinavir-ritonavir combined with arbidol for antiviral treatment. Results: by applying correlation analysis between dataset attributes, we found that there is no relationship between the presence of chronic diseases or the patient' age and the Covid-19 clinical classification. The prediction model results show that 10 fold cross-validation with Naïve Bayes and neural network achieving the highest accuracy of 85.71%. Conclusion: this paper has exploited correlation analysis and machine learning based approaches to identify relevant attributes in the COVID-19 patients' dataset and predicting the most appropriate treatment course.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

The Role of Machine Learning to Fight COVID-19

Ouf¹,

Hamza²

2021

IJIES

View full text Add to dashboard Cite

show abstract

“…The applications of inverted distributions have been discussed with many researchers, and the reader can refer to Abd AL-Fattah et al [ 2 ], Barco et al [ 3 ], Hassan and Abd-Allah [ 4 ], Hassan and Mohamed [ 5 ], Muhammed [ 6 ], Chesneau et al [ 7 ], Usman and ul Haq [ 8 ], Eferhonore et al [ 9 ] among others. The modeling of COVID-19 data have been discussed with many researchers, and the reader can refer to Kumar [ 10 ], Khakharia et al [ 11 ], Li et al [ 12 ], Liu et al [ 13 ], Wang [ 14 ], Lalmuanawma et al [ 15 ] and Bullock et al [ 16 ].…”

Section: Introductionmentioning

confidence: 99%

The Odd Weibull Inverse Topp–Leone Distribution with Applications to COVID-19 Data

Almetwally

2021

Ann. Data. Sci.

View full text Add to dashboard Cite

This paper aims at defining an optimal statistical model for the COVID-19 distribution in the United Kingdom, and Canada. A combining the inverted Topp–Leone distribution and the odd Weibull family introduces a new lifetime distribution with a three-parameter to formulate the odd Weibull inverted Topp–Leone (OWITL) distribution. As a simple linear representation, hazard rate function, and moment function, this new distribution has several nice properties. To estimate the unknown parameters of OWITL distribution, maximum likelihood, least-square, weighted least-squares, maximum product spacing, Cramér–von Mises estimators, and Anderson–Darling estimation methods are used. To evaluate the use of estimation techniques, a numerical outcome of the Monte Carlo simulation is obtained.

show abstract

“…This in turn will help to reduce number of infected and deceased individuals. A similar clustering approach is used in a study carried out by [ 7 ].…”

Section: Introductionmentioning

confidence: 99%

An analysis of COVID-19 clusters in India

et al. 2021

View full text Add to dashboard Cite

Background In this study we cluster the districts of India in terms of the spread of COVID-19 and related variables such as population density and the number of specialty hospitals. Simulation using a compartment model is used to provide insight into differences in response to public health interventions. Two case studies of interest from Nizamuddin and Dharavi provide contrasting pictures of the success in curbing spread. Methods A cluster analysis of the worst affected districts in India provides insight about the similarities between them. The effects of public health interventions in flattening the curve in their respective states is studied using the individual contact SEIQHRF model, a stochastic individual compartment model which simulates disease prevalence in the susceptible, infected, recovered and fatal compartments. Results The clustering of hotspot districts provide homogeneous groups that can be discriminated in terms of number of cases and related covariates. The cluster analysis reveal that the distribution of number of COVID-19 hospitals in the districts does not correlate with the distribution of confirmed COVID-19 cases. From the SEIQHRF model for Nizamuddin we observe in the second phase the number of infected individuals had seen a multitudinous increase in the states where Nizamuddin attendees returned, increasing the risk of the disease spread. However, the simulations reveal that implementing administrative interventions, flatten the curve. In Dharavi, through tracing, tracking, testing and treating, massive breakout of COVID-19 was brought under control. Conclusions The cluster analysis performed on the districts reveal homogeneous groups of districts that can be ranked based on the burden placed on the healthcare system in terms of number of confirmed cases, population density and number of hospitals dedicated to COVID-19 treatment. The study rounds up with two important case studies on Nizamuddin basti and Dharavi to illustrate the growth curve of COVID-19 in two very densely populated regions in India. In the case of Nizamuddin, the study showed that there was a manifold increase in the risk of infection. In contrast it is seen that there was a rapid decline in the number of cases in Dharavi within a span of about one month.

show abstract

Monitoring Novel Corona Virus (COVID-19) Infections in India by Cluster Analysis

Cited by 63 publications

References 11 publications

The Role of Machine Learning to Fight COVID-19

The Role of Machine Learning to Fight COVID-19

The Odd Weibull Inverse Topp–Leone Distribution with Applications to COVID-19 Data

An analysis of COVID-19 clusters in India

Contact Info

Product

Resources

About