A Particle-Based COVID-19 Simulator With Contact Tracing and Testing
Goal: The COVID-19 pandemic has emerged as the most severe public health crisis in over a century. As of January 2021, there are more than 100 million cases and 2.1 million deaths. For informed decision making, reliable statistical data and capable simulation tools are needed. Our goal is to develop...
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| Veröffentlicht in: | IEEE open journal of engineering in medicine and biology 2021, Vol.2, p.111-117 |
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| container_title | IEEE open journal of engineering in medicine and biology |
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| creator | Kuzdeuov, Askat Karabay, Aknur Baimukashev, Daulet Ibragimov, Bauyrzhan Varol, Huseyin Atakan |
| description | Goal: The COVID-19 pandemic has emerged as the most severe public health crisis in over a century. As of January 2021, there are more than 100 million cases and 2.1 million deaths. For informed decision making, reliable statistical data and capable simulation tools are needed. Our goal is to develop an epidemic simulator that can model the effects of random population testing and contact tracing. Methods: Our simulator models individuals as particles with the position, velocity, and epidemic status states on a 2D map and runs an SEIR epidemic model with contact tracing and testing modules. The simulator is available on GitHub under the MIT license. Results: The results show that the synergistic use of contact tracing and massive testing is effective in suppressing the epidemic (the number of deaths was reduced by 72%). Conclusions: The Particle-based COVID-19 simulator enables the modeling of intervention measures, random testing, and contact tracing, for epidemic mitigation and suppression. |
| doi_str_mv | 10.1109/OJEMB.2021.3064506 |
| format | Article |
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As of January 2021, there are more than 100 million cases and 2.1 million deaths. For informed decision making, reliable statistical data and capable simulation tools are needed. Our goal is to develop an epidemic simulator that can model the effects of random population testing and contact tracing. Methods: Our simulator models individuals as particles with the position, velocity, and epidemic status states on a 2D map and runs an SEIR epidemic model with contact tracing and testing modules. The simulator is available on GitHub under the MIT license. Results: The results show that the synergistic use of contact tracing and massive testing is effective in suppressing the epidemic (the number of deaths was reduced by 72%). Conclusions: The Particle-based COVID-19 simulator enables the modeling of intervention measures, random testing, and contact tracing, for epidemic mitigation and suppression.</description><identifier>ISSN: 2644-1276</identifier><identifier>EISSN: 2644-1276</identifier><identifier>DOI: 10.1109/OJEMB.2021.3064506</identifier><identifier>PMID: 34786559</identifier><identifier>CODEN: IOJEA7</identifier><language>eng</language><publisher>New York: IEEE</publisher><subject>Biological system modeling ; Contact ; Contact tracing ; Coronaviruses ; COVID-19 ; Decision making ; Epidemic models ; epidemic simulator ; Epidemics ; Fatalities ; Mitigation ; Pandemics ; Particle measurements ; particle-based simulation ; Public health ; random testing ; Simulation ; Sociology ; Statistics ; Testing ; Two dimensional models</subject><ispartof>IEEE open journal of engineering in medicine and biology, 2021, Vol.2, p.111-117</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. 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Conclusions: The Particle-based COVID-19 simulator enables the modeling of intervention measures, random testing, and contact tracing, for epidemic mitigation and suppression.</description><subject>Biological system modeling</subject><subject>Contact</subject><subject>Contact tracing</subject><subject>Coronaviruses</subject><subject>COVID-19</subject><subject>Decision making</subject><subject>Epidemic models</subject><subject>epidemic simulator</subject><subject>Epidemics</subject><subject>Fatalities</subject><subject>Mitigation</subject><subject>Pandemics</subject><subject>Particle measurements</subject><subject>particle-based simulation</subject><subject>Public health</subject><subject>random testing</subject><subject>Simulation</subject><subject>Sociology</subject><subject>Statistics</subject><subject>Testing</subject><subject>Two dimensional models</subject><issn>2644-1276</issn><issn>2644-1276</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>ESBDL</sourceid><sourceid>RIE</sourceid><sourceid>DOA</sourceid><recordid>eNpdkc1uEzEUhS0EolXpC8BmJDZsJvj6fzZIbdpCUFGQCLC0HM916mgybj0zSLx93SaqKCtf2ed8ts8h5C3QGQBtPi6_Xn47nzHKYMapEpKqF-SYKSFqYFq9_Gc-IqfDsKWUMgkAzLwmR1xoo6RsjsnFWfXd5TH6DutzN2BbzZe_Fhc1NNWPuJs6N6Zc_Y7jTTVP_ej8WK2y87HfVK5vqxUOY5nfkFfBdQOeHtYT8vPqcjX_Ul8vPy_mZ9e1F9So2jEHnEntBecBJZciBIHomVSCKc99y7wPMrjANUg0QQiBLvi1Fl4jBH5CFntum9zW3ua4c_mvTS7ax42UN_bwFwstMGyMxhakQB5cozldU1qubbVZQ2F92rNup_UOW4_9mF33DPr8pI83dpP-WCNL2JwXwIcDIKe7qQRhd3Hw2HWuxzQNlsnGSK41qCJ9_590m6bcl6iKioNUTDJRVGyv8jkNQ8bw9Big9qFz-9i5fejcHjovpnd7U0TEJ0PDNTNK8XsPVqQd</recordid><startdate>2021</startdate><enddate>2021</enddate><creator>Kuzdeuov, Askat</creator><creator>Karabay, Aknur</creator><creator>Baimukashev, Daulet</creator><creator>Ibragimov, Bauyrzhan</creator><creator>Varol, Huseyin Atakan</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. 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| subjects | Biological system modeling Contact Contact tracing Coronaviruses COVID-19 Decision making Epidemic models epidemic simulator Epidemics Fatalities Mitigation Pandemics Particle measurements particle-based simulation Public health random testing Simulation Sociology Statistics Testing Two dimensional models |
| title | A Particle-Based COVID-19 Simulator With Contact Tracing and Testing |
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