COVID-19 spread control policies based early dynamics forecasting using deep learning algorithm-Reference-Cited by-同舟云学术

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COVID-19 spread control policies based early dynamics forecasting using deep learning algorithm

Published:2023-02 Issue: Volume:167 Page:112984
ISSN:0960-0779
Container-title:Chaos, Solitons & Fractals
language:en
Short-container-title:Chaos, Solitons & Fractals

Author:

Ali Furqan,Ullah Farman,Khan Junaid Iqbal,Khan Jebran,Sardar Abdul Wasay,Lee Sungchang

Publisher

Elsevier BV

Subject

General Mathematics,General Physics and Astronomy,Statistical and Nonlinear Physics,Applied Mathematics

Reference55 articles.

1. Wearable iots and geo-fencing based framework for COVID-19 remote patient health monitoring and quarantine management to control the pandemic;Ullah;Electronics,2021

2. South Korea: coronavirus cases by test result | Statista,2021

3. Responding to community spread of COVID-19: interim guidance, 7 March 2020;Organization,2020

4. Prediction of COVID-19 active cases using exponential and non-linear growth models;Mahanty;Expert Syst,2022

5. Corona COVID-19 spread-a nonlinear modeling and simulation;Harb;Comput Electr Eng,2020

Cited by 3 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Impact of social distancing on disease transmission risk in the context of a pandemic;Physical Review E;2023-11-09

2. Collective infectivity of the pandemic over time and association with vaccine coverage and economic development;Chaos, Solitons & Fractals;2023-11

3. Forecasting Time Series Covid 19 Using Deep Learning Methods: South America Comparative Case Study;2023-08-22

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