Prediction of COVID-19 Outbreak in India adopting Bhilwara Model of Containment

Bhati, Amit; Jagetiya, Anurag

doi:10.1109/icces48766.2020.9138060

Cited by 10 publications

(5 citation statements)

References 12 publications

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“…Exceptions, missing information, and huge changes in your time series are not an issue for Prophet. The Prophet technique furnishes clients with various choices for calibrating [12,13] and changing estimates.…”

Section: Hierarchical Algorithm -Clustering Countries and Statesmentioning

confidence: 99%

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Prediction and Distribution of Disease Using Hybrid Clustering Algorithm in Big Data

Vinston Raja R

2023

IJRITCC

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COVID disease plague of 2019 (COVID19) has made an overall health related crisis with a very high gamble of spreading and influencing the whole planet. In essentially every nation, new cases have been accounted. To identify all countries expanding number of tests, the manual clustering of COVID-19 and clinical infection information tests becomes tedious and requires profoundly talented work. As of late, a few calculations have been utilized for clustering clinical datasets deterministically; nonetheless, these definitions have not been powerful in gathering and investigating clinical infections. To rank and score more than 200 nations as indicated by COVID-19 cases and casualty in 2020 and contrast the outcomes with existing pandemic weakness forecast models and results produced by standard Data clustering scoring methods. Information clustering is a course of orchestrating comparative information into gatherings. A clustering algorithm bundles an informational collection into a few several clusters such an extent that the similitude inside a gathering is better compared to among clusters. This paper propose new Hybrid clustering algorithm KMHC in view of K-Means and Hierarchical Clustering calculation. This calculation KMHC, First isolated into every nation gatherings or fragments in light of the COVID patients count, Secondly grouping models has been made across the nations on the planet and across the states in India, and the presentation investigation is analyzed. This paper likewise center figures the future COVID count for India. By utilizing this outcome, set of nations which are having higher COVID count can be effortlessly pictured and the proper moves will be made to diminish the count.

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Section: Hierarchical Algorithm -Clustering Countries and Statesmentioning

confidence: 99%

“…It is an iterative slope climbing calculation and the arrangement acquired relies upon the underlying grouping. Albeit the K-implies calculation had been effectively applied to numerous viable grouping issues, it has been shown that the calculation might neglect to merge to a worldwide least under specific circumstances [13].…”

Section: Introductionmentioning

confidence: 99%

Prediction and Distribution of Disease Using Hybrid Clustering Algorithm in Big Data

Vinston Raja R

2023

IJRITCC

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“…Several other kinds of research have also been done for forecasting the spread of COVID-19 in India [7] by extending the SIR model to SIRD (Susceptible-Infectious-Recovered-Death) model [15] [16]. Another research adopted the Bhilwara model of containment and used SEIR (Susceptible-Exposed-Infectious-Removed) Model for analyzing the disease spread [17]. This paper also aims to study the spread of COVID-19 patterns in India using ARIMA and Prophet Models for forecasting.…”

Section: Related Workmentioning

confidence: 99%

Analyzing and Forecasting COVID-19 Outbreak in India

Chordia¹,

Pawar²

2021

2021 11th International Conference on Cloud Computing, Data Science &Amp; Engineering (Confluence)

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The unprecedented outbreak of the COVID-19 virus has infected more than 50 million people all over the world in less than a year. More than 1 million people have lost their lives due to the ongoing pandemic. The pandemic struck India on January 30, 2020, when the first positive case of COVID-19 was identified in Kerala. Today, India is one of the most adversely affected countries in the world. Hence, it is of utmost importance to analyze the trends in India and use the adopted knowledge to forecast the future course of outcomes. Along with the overall trend analysis in India, this study also takes into account 5 most affected states of the country: Maharashtra, Andhra Pradesh, Tamil Nadu, Karnataka and Uttar Pradesh as the subjects of the research. ARIMA and Prophet time series forecasting models have been used to make three types of predictions: confirmed cases, deaths and recovered cases in India as well as in the adopted states. The effectiveness of the forecasting models is evaluated based on metrics such as Root Mean Squared Error, Mean Absolute Error, Mean Absolute Percentage Error and Coefficient of Determination. The results suggest that the adopted models are promising mechanisms for forecasting COVID-19 trends. Our study also suggests that ARIMA model performs better than Prophet Model at this task of forecasting the outbreak. The forecasts can be useful in increasing the preparedness level of government authorities, health facilities and hospitals to combat against massive spread of the virus.

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“…Examples of handcrafted mathematical models include Polynomials [10] and Logistic growth [6]. Compartmental models from epidemiology were also used including SEIR [1] and SIR [3]. More complex statistical models, such as the Gaussian Process Regression [7] and ARMA [8], have also been successfully applied to forecasting COVID-19.…”

Section: Related Workmentioning

confidence: 99%

The effects of regularisation on RNN models for time series forecasting: Covid-19 as an example

Carpenter¹,

Luo²,

Wang³

2021

Preprint

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Many research papers that propose models to predict the course of the COVID-19 pandemic either use handcrafted statistical models or large neural networks. Even though large neural networks are more powerful than simpler statistical models, they are especially hard to train on small datasets. This paper not only presents a model with grater flexibility than the other proposed neural networks, but also presents a model that is effective on smaller datasets. To improve performance on small data, six regularisation methods were tested. The results show that the GRU combined with 20% Dropout achieved the lowest RMSE scores. The main finding was that models with less access to data relied more on the regulariser. Applying Dropout to a GRU model trained on only 28 days of data reduced the RMSE by 23%.

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Prediction of COVID-19 Outbreak in India adopting Bhilwara Model of Containment

Cited by 10 publications

References 12 publications

Prediction and Distribution of Disease Using Hybrid Clustering Algorithm in Big Data

Prediction and Distribution of Disease Using Hybrid Clustering Algorithm in Big Data

Analyzing and Forecasting COVID-19 Outbreak in India

The effects of regularisation on RNN models for time series forecasting: Covid-19 as an example

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