An agent based model for assessing transmission dynamics and health systems burden for COVID-19
Coronavirus disease of 2019 (COVID-19) pandemic has caused over 230 million infections with more than 4 million deaths worldwide. Researches have been using various mathematical and simulation techniques to estimate the future trends of the pandemic to help the policymakers and healthcare fraternity...
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| Main Authors: | , , , , , , |
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| Format: | EJournal Article |
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Institute of Advanced Engineering and Science,
2021-12-01.
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| Online Access: | Get fulltext |
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| LEADER | 02710 am a22003613u 4500 | ||
|---|---|---|---|
| 001 | ijeecs24664_15838 | ||
| 042 | |a dc | ||
| 100 | 1 | 0 | |a S., Narassima M. |e author |
| 100 | 1 | 0 | |e contributor |
| 700 | 1 | 0 | |a P., Anbuudayasankar S. |e author |
| 700 | 1 | 0 | |a Jammy, Guru Rajesh |e author |
| 700 | 1 | 0 | |a Pant, Rashmi |e author |
| 700 | 1 | 0 | |a Choudhury, Lincoln |e author |
| 700 | 1 | 0 | |a Ramakrishnan, Aadharsh |e author |
| 700 | 1 | 0 | |a John, Denny |e author |
| 245 | 0 | 0 | |a An agent based model for assessing transmission dynamics and health systems burden for COVID-19 |
| 260 | |b Institute of Advanced Engineering and Science, |c 2021-12-01. | ||
| 500 | |a https://ijeecs.iaescore.com/index.php/IJEECS/article/view/24664 | ||
| 520 | |a Coronavirus disease of 2019 (COVID-19) pandemic has caused over 230 million infections with more than 4 million deaths worldwide. Researches have been using various mathematical and simulation techniques to estimate the future trends of the pandemic to help the policymakers and healthcare fraternity. Agent-based models (ABM) could provide accurate projections than the compartmental models that have been largely used. The present study involves a simulation of ABM using a synthetic population from India to analyze the effects of interventions on the spread of the disease. A disease model with various states representing the possible progression of the disease was developed and simulated using AnyLogic. The results indicated that imposing stricter non-pharmaceutical interventions (NPI) lowered the peak values of infections, the proportion of critical patients, and the deceased. Stricter interventions offer a larger time window for the healthcare fraternity to enhance preparedness. The findings of this research could act as a start-point to understand the benefits of ABM-based models for projecting infectious diseases and analyzing the effects of NPI imposed. | ||
| 540 | |a Copyright (c) 2021 Institute of Advanced Engineering and Science | ||
| 540 | |a http://creativecommons.org/licenses/by-nc/4.0 | ||
| 546 | |a eng | ||
| 690 | |a SARS-CoV-2; Simulation; Disease Modelling; Simulation | ||
| 690 | |a Agent based model; Coronavirus; COVID-19; SARS-CoV-2; Simulation; | ||
| 655 | 7 | |a info:eu-repo/semantics/article |2 local | |
| 655 | 7 | |a info:eu-repo/semantics/publishedVersion |2 local | |
| 655 | 7 | |2 local | |
| 786 | 0 | |n Indonesian Journal of Electrical Engineering and Computer Science; Vol 24, No 3: December 2021; 1735-1743 | |
| 786 | 0 | |n 2502-4760 | |
| 786 | 0 | |n 2502-4752 | |
| 786 | 0 | |n 10.11591/ijeecs.v24.i3 | |
| 787 | 0 | |n https://ijeecs.iaescore.com/index.php/IJEECS/article/view/24664/15838 | |
| 856 | 4 | 1 | |u https://ijeecs.iaescore.com/index.php/IJEECS/article/view/24664/15838 |z Get fulltext |