Modelling Logistic Growth Model for COVID-19 Pandemic in India

Jain, Minni; Bhati, Prajwal Kumar; Kataria, Pranav; Kumar, Rohit

doi:10.1109/icces48766.2020.9138049

Cited by 13 publications

(12 citation statements)

References 5 publications

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“…The models predict exponential and subexponential spread rate in the number of positive cases in India in April 2020. The findings proved that significant measures are required to control the transmission rate of the virus in the India [4].…”

Section: Literature Surveymentioning

confidence: 89%

Machine Learning Approaches for Analysis of Covid-19 Data in India: A Case of Pandemic

Muralikrishna¹

2020

IJATCSE

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Covid -19 has made the whole world upside down with spreading of virus faster in various countries. India cases started in the month of March which panic all the peoples, yet the mortality rate (1.8%) is much less than the other countries. It is believed with native immunity of Indians that surveyed. But thou a dreathful time for the health care centre where the doctors and nurses spent sleepless night treating the cases. The lockdown has made relaxation in spread of the virus. Yet few states showed very high cases with the living culture and spread of virus were due to community spread too. In this study of Covid-19, machine learning techniques were applied to the datasets of twelve states with twelve dates assumed. The results were very promising with SVM, Naïve and DT models with accuracy of 100%. F1-score, precision and recall obtained as 1.0 whereas KNN accuracy was very poor with 60%. The confusion matrix accuracy obtained was 0.0821. CNN prediction is better over LSTM and Hybrid -LSTM -CNN models. Hence, the results proved that implementing ML and DL techniques would help to analysis the cases faster and monitor the region or states in the future any pandemic attacks.

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Section: Literature Surveymentioning

confidence: 89%

Machine Learning Approaches for Analysis of Covid-19 Data in India: A Case of Pandemic

Muralikrishna¹

2020

IJATCSE

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“…In [9] Dhahi Alshammari, Nourah Alqahtani, Dabiah Alboaneen, Bernardi Pranggono, and Raja A lyaffer the authors utilizes the Logistic Growth model with high expectations contrasted with others.…”

Section: Existing Workmentioning

confidence: 99%

“…The primary preferred position of Conditional Random Field over Markov's shrouded models is its restrictive nature, which prompts the unwinding of the autonomous intuition needed by Hidden Marko Models to guarantee an unmistakable pattern. Conditional Random Fields evades the issue of name inclination, shortcomings distinguished by very good quality entropy models Markov and another contingent Markov Model dependent on focused [1,9,12]: MLP is an idea moved by the regular tangible framework, which estimates information like the brain. The basic part of this is the new structure of the information ready system.…”

Section: B Pre-processingmentioning

confidence: 99%

A Review on Indian State/City Covid-19 Cases Outbreak Forecast utilizing Machine Learning Models

Degadwala

Patel

Vyas³

2021

2021 6th International Conference on Inventive Computation Technologies (ICICT)

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S everal scene supposition models for COVID-19 are "be ing utilized by experts around the globe to settle on trained choices and keep up fitting control measures. Man-made awareness Machine Learning (ML) based choosing fragments have shown their significance to foresee perioperative results to improve the dynamic of things to come course of activities. The ML models have been utilized in different application spaces which required the obvious check and prioritization of undesirable parts for a danger. A few supposition techniques are in fact unavoidably used to oversee imagining issues. This evaluation shows the limitation of Machine Learning models to ascertain the amount of moving toward patients influenced by COVID-19 which is considered as a typical risk to humanity. S pecifically, four standard choosing models, for example, Linear apostatize, keep up vector machine, MLP, Decision Tree, Boosted Random Forest, Regression Tree, and Extra Tree have been utilized in this evaluation to figure the compromising elements of COVID-19. Three kinds of guesses are made by the aggregate of the models, for example, the number of starting late polluted cases after and before starter vexing, the number of passing's after and before groundwork vexing, and the number of recuperation after and before groundwork vexing. The outcomes made by the evaluation display a promising structure to utilize these systems for the" current situation of the COVID-19 pandemic.

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“…Konstruksi model pertumbuhan logistik dapat menunjukkan pertumbuhan dan penurunan jumlah kasus. Jika pada tahap awal kurva logistik konvergen maka pandemic dapat dikendalikan dan sebaliknya [6]. Pertumbuhan logistik menggunakan parameter yang diestimasi dengan metode non-linear least squares (NLS) untuk memodelkan dan menganalisis data time-series [7] Model logistik yang dipengaruhi oleh awal jumlah populasi, laju pertumbuhan, dan daya tampung (carrying capacity) sebagai ukuran populasi maksimum yang dapat ditampung oleh suatu lingkungan tertentu tanpa ada pertambahan atau penurunan ukuran populasi selama periode waktu relatif lama.…”

Section: Pendahuluanunclassified

Logistic Model Development for Prediction of Covid-19 Infected Individuals: Case Study in Central Java Province, Indonesia

Arditya

Widowati

Sutimin³

et al. 2021

JFMA

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In early 2020, covid-19 spread fast in the worldwide and cause the high death. The disease started from the Asian region which resulted in a viral pandemic in 2020. In order to anticipate the increasing of the cases, a strategy is needed to inhibit its transmission. The mathematical model approach is important tool for predicting of covid-19 spread in populations. In this paper we propose and analyze the dynamical behaviour of a developed logistic model by considering the effect of the contact patterns in reducing the covid-19 spread process. To verify the developed logistic model, numerical simulation was given with case study of covid-19 spread for patients under supervision in Central Java Province, Indonesia. Based on simulation results, it was found that physical distancing can reduce the growth of the covid-19 spread for patient under supervision. It can be seen from the number of covid-19 spread for patients under supervision with physical distancing intervention smaller compared to without physical distancing intervention.

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Modelling Logistic Growth Model for COVID-19 Pandemic in India

Cited by 13 publications

References 5 publications

Machine Learning Approaches for Analysis of Covid-19 Data in India: A Case of Pandemic

Machine Learning Approaches for Analysis of Covid-19 Data in India: A Case of Pandemic

A Review on Indian State/City Covid-19 Cases Outbreak Forecast utilizing Machine Learning Models

Logistic Model Development for Prediction of Covid-19 Infected Individuals: Case Study in Central Java Province, Indonesia

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