Sigmoid models for Covid-19 pandemic

Sigmoid models for Covid-19 pandemic

Several static models for description of the Covid-19 pandemic are considered, analyzed and compared. The best models relative to this pandemic (generalized fractional-power model and generalized inverse tangent model) are new. These and other non-symmetric models are user friendly and show a good a...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Hauptverfasser: Konstantinov, M., Konstantinov, K., Konstantinov, S.
Format: Tagungsbericht
Sprache:eng
Schlagworte:
Coronaviruses
COVID-19
Generalized inverse
Pandemics
Static models
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
container_end_page
container_issue 1
container_start_page
container_title
container_volume 2333
creator Konstantinov, M.
Konstantinov, K.
Konstantinov, S.
description Several static models for description of the Covid-19 pandemic are considered, analyzed and compared. The best models relative to this pandemic (generalized fractional-power model and generalized inverse tangent model) are new. These and other non-symmetric models are user friendly and show a good agreement with the official data for the pandemic in Europe. The best models give less than 1% error for a 20-day prediction period and less than 1.5% error for a 40-day prediction period. Recommendations for the use of the models are made. The results obtained may be used in the analysis of possible new waves of Covid-19 in different countries, in Europe and worldwide. Such waves seem more and more likely.
doi_str_mv 10.1063/5.0041728
format Conference Proceeding
fullrecord <record><control><sourceid>proquest_scita</sourceid><recordid>TN_cdi_proquest_journals_2498706326</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2498706326</sourcerecordid><originalsourceid>FETCH-LOGICAL-p253t-71d2eaa45e3af42445beb85285fa04c83e58bdf5213c685fcc46726bda88e5263</originalsourceid><addsrcrecordid>eNotkE1LxDAYhIMoWFcP_oOCnoTs5utN0qMUv2DBwyp4C2mSSpZtU5uu4L-3unsaGB5mmEHompIlJZKvYEmIoIrpE1RQAIqVpPIUFYRUAjPBP87RRc5bQlillC7Q7SZ-din6sks-7HLZprGs03f0mFblYHsfuugu0VlrdzlcHXWB3h8f3upnvH59eqnv13hgwCesqGfBWgGB21YwIaAJjQamobVEOM0D6Ma3wCh3cjadE1Ix2XirdQAm-QLdHHKHMX3tQ57MNu3Hfq40TFRazQP_qbsDlV2c7BRTb4Yxdnb8MZSYvxcMmOML_BdPgUww</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>conference_proceeding</recordtype><pqid>2498706326</pqid></control><display><type>conference_proceeding</type><title>Sigmoid models for Covid-19 pandemic</title><source>AIP Journals Complete</source><creator>Konstantinov, M. ; Konstantinov, K. ; Konstantinov, S.</creator><contributor>Venkov, George ; Pasheva, Vesela ; Popivanov, Nedyu</contributor><creatorcontrib>Konstantinov, M. ; Konstantinov, K. ; Konstantinov, S. ; Venkov, George ; Pasheva, Vesela ; Popivanov, Nedyu</creatorcontrib><description>Several static models for description of the Covid-19 pandemic are considered, analyzed and compared. The best models relative to this pandemic (generalized fractional-power model and generalized inverse tangent model) are new. These and other non-symmetric models are user friendly and show a good agreement with the official data for the pandemic in Europe. The best models give less than 1% error for a 20-day prediction period and less than 1.5% error for a 40-day prediction period. Recommendations for the use of the models are made. The results obtained may be used in the analysis of possible new waves of Covid-19 in different countries, in Europe and worldwide. Such waves seem more and more likely.</description><identifier>ISSN: 0094-243X</identifier><identifier>EISSN: 1551-7616</identifier><identifier>DOI: 10.1063/5.0041728</identifier><identifier>CODEN: APCPCS</identifier><language>eng</language><publisher>Melville: American Institute of Physics</publisher><subject>Coronaviruses ; COVID-19 ; Generalized inverse ; Pandemics ; Static models</subject><ispartof>AIP conference proceedings, 2021, Vol.2333 (1)</ispartof><rights>Author(s)</rights><rights>2021 Author(s). Published by AIP Publishing.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://pubs.aip.org/acp/article-lookup/doi/10.1063/5.0041728$$EHTML$$P50$$Gscitation$$H</linktohtml><link.rule.ids>309,310,314,778,782,787,788,792,4500,23913,23914,25123,27907,27908,76135</link.rule.ids></links><search><contributor>Venkov, George</contributor><contributor>Pasheva, Vesela</contributor><contributor>Popivanov, Nedyu</contributor><creatorcontrib>Konstantinov, M.</creatorcontrib><creatorcontrib>Konstantinov, K.</creatorcontrib><creatorcontrib>Konstantinov, S.</creatorcontrib><title>Sigmoid models for Covid-19 pandemic</title><title>AIP conference proceedings</title><description>Several static models for description of the Covid-19 pandemic are considered, analyzed and compared. The best models relative to this pandemic (generalized fractional-power model and generalized inverse tangent model) are new. These and other non-symmetric models are user friendly and show a good agreement with the official data for the pandemic in Europe. The best models give less than 1% error for a 20-day prediction period and less than 1.5% error for a 40-day prediction period. Recommendations for the use of the models are made. The results obtained may be used in the analysis of possible new waves of Covid-19 in different countries, in Europe and worldwide. Such waves seem more and more likely.</description><subject>Coronaviruses</subject><subject>COVID-19</subject><subject>Generalized inverse</subject><subject>Pandemics</subject><subject>Static models</subject><issn>0094-243X</issn><issn>1551-7616</issn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2021</creationdate><recordtype>conference_proceeding</recordtype><recordid>eNotkE1LxDAYhIMoWFcP_oOCnoTs5utN0qMUv2DBwyp4C2mSSpZtU5uu4L-3unsaGB5mmEHompIlJZKvYEmIoIrpE1RQAIqVpPIUFYRUAjPBP87RRc5bQlillC7Q7SZ-din6sks-7HLZprGs03f0mFblYHsfuugu0VlrdzlcHXWB3h8f3upnvH59eqnv13hgwCesqGfBWgGB21YwIaAJjQamobVEOM0D6Ma3wCh3cjadE1Ix2XirdQAm-QLdHHKHMX3tQ57MNu3Hfq40TFRazQP_qbsDlV2c7BRTb4Yxdnb8MZSYvxcMmOML_BdPgUww</recordid><startdate>20210308</startdate><enddate>20210308</enddate><creator>Konstantinov, M.</creator><creator>Konstantinov, K.</creator><creator>Konstantinov, S.</creator><general>American Institute of Physics</general><scope>8FD</scope><scope>H8D</scope><scope>L7M</scope></search><sort><creationdate>20210308</creationdate><title>Sigmoid models for Covid-19 pandemic</title><author>Konstantinov, M. ; Konstantinov, K. ; Konstantinov, S.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-p253t-71d2eaa45e3af42445beb85285fa04c83e58bdf5213c685fcc46726bda88e5263</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Coronaviruses</topic><topic>COVID-19</topic><topic>Generalized inverse</topic><topic>Pandemics</topic><topic>Static models</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Konstantinov, M.</creatorcontrib><creatorcontrib>Konstantinov, K.</creatorcontrib><creatorcontrib>Konstantinov, S.</creatorcontrib><collection>Technology Research Database</collection><collection>Aerospace Database</collection><collection>Advanced Technologies Database with Aerospace</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Konstantinov, M.</au><au>Konstantinov, K.</au><au>Konstantinov, S.</au><au>Venkov, George</au><au>Pasheva, Vesela</au><au>Popivanov, Nedyu</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Sigmoid models for Covid-19 pandemic</atitle><btitle>AIP conference proceedings</btitle><date>2021-03-08</date><risdate>2021</risdate><volume>2333</volume><issue>1</issue><issn>0094-243X</issn><eissn>1551-7616</eissn><coden>APCPCS</coden><abstract>Several static models for description of the Covid-19 pandemic are considered, analyzed and compared. The best models relative to this pandemic (generalized fractional-power model and generalized inverse tangent model) are new. These and other non-symmetric models are user friendly and show a good agreement with the official data for the pandemic in Europe. The best models give less than 1% error for a 20-day prediction period and less than 1.5% error for a 40-day prediction period. Recommendations for the use of the models are made. The results obtained may be used in the analysis of possible new waves of Covid-19 in different countries, in Europe and worldwide. Such waves seem more and more likely.</abstract><cop>Melville</cop><pub>American Institute of Physics</pub><doi>10.1063/5.0041728</doi><tpages>1</tpages><oa>free_for_read</oa></addata></record>
fulltext fulltext
identifier ISSN: 0094-243X
ispartof AIP conference proceedings, 2021, Vol.2333 (1)
issn 0094-243X
1551-7616
language eng
recordid cdi_proquest_journals_2498706326
source AIP Journals Complete
subjects Coronaviruses
COVID-19
Generalized inverse
Pandemics
Static models
title Sigmoid models for Covid-19 pandemic
url https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-16T11%3A20%3A29IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_scita&rft_val_fmt=info:ofi/fmt:kev:mtx:book&rft.genre=proceeding&rft.atitle=Sigmoid%20models%20for%20Covid-19%20pandemic&rft.btitle=AIP%20conference%20proceedings&rft.au=Konstantinov,%20M.&rft.date=2021-03-08&rft.volume=2333&rft.issue=1&rft.issn=0094-243X&rft.eissn=1551-7616&rft.coden=APCPCS&rft_id=info:doi/10.1063/5.0041728&rft_dat=%3Cproquest_scita%3E2498706326%3C/proquest_scita%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2498706326&rft_id=info:pmid/&rfr_iscdi=true