On automatic calibration of the SIRD epidemiological model for COVID-19 data in Poland
We propose a novel methodology for estimating the epidemiological parameters of a modified SIRD model (acronym of Susceptible, Infected, Recovered and Deceased individuals) and perform a short-term forecast of SARS-CoV-2 virus spread. We mainly focus on forecasting number of deceased. The procedure...
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| creator | Błaszczyk, Piotr Klimczak, Konrad Mahdi, Adam Oprocha, Piotr Potorski, Paweł Przybyłowicz, Paweł Sobieraj, Michał |
| description | We propose a novel methodology for estimating the epidemiological parameters
of a modified SIRD model (acronym of Susceptible, Infected, Recovered and
Deceased individuals) and perform a short-term forecast of SARS-CoV-2 virus
spread. We mainly focus on forecasting number of deceased. The procedure was
tested on reported data for Poland. For some short-time intervals we performed
numerical test investigating stability of parameter estimates in the proposed
approach. Numerical experiments confirm the effectiveness of short-term
forecasts (up to 2 weeks) and stability of the method. To improve their
performance (i.e. computation time) GPU architecture was used in computations. |
| doi_str_mv | 10.48550/arxiv.2204.12346 |
| format | Article |
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of a modified SIRD model (acronym of Susceptible, Infected, Recovered and
Deceased individuals) and perform a short-term forecast of SARS-CoV-2 virus
spread. We mainly focus on forecasting number of deceased. The procedure was
tested on reported data for Poland. For some short-time intervals we performed
numerical test investigating stability of parameter estimates in the proposed
approach. Numerical experiments confirm the effectiveness of short-term
forecasts (up to 2 weeks) and stability of the method. To improve their
performance (i.e. computation time) GPU architecture was used in computations.</description><identifier>DOI: 10.48550/arxiv.2204.12346</identifier><language>eng</language><subject>Statistics - Machine Learning ; Statistics - Methodology</subject><creationdate>2022-04</creationdate><rights>http://arxiv.org/licenses/nonexclusive-distrib/1.0</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>228,230,776,881</link.rule.ids><linktorsrc>$$Uhttps://arxiv.org/abs/2204.12346$$EView_record_in_Cornell_University$$FView_record_in_$$GCornell_University$$Hfree_for_read</linktorsrc><backlink>$$Uhttps://doi.org/10.48550/arXiv.2204.12346$$DView paper in arXiv$$Hfree_for_read</backlink></links><search><creatorcontrib>Błaszczyk, Piotr</creatorcontrib><creatorcontrib>Klimczak, Konrad</creatorcontrib><creatorcontrib>Mahdi, Adam</creatorcontrib><creatorcontrib>Oprocha, Piotr</creatorcontrib><creatorcontrib>Potorski, Paweł</creatorcontrib><creatorcontrib>Przybyłowicz, Paweł</creatorcontrib><creatorcontrib>Sobieraj, Michał</creatorcontrib><title>On automatic calibration of the SIRD epidemiological model for COVID-19 data in Poland</title><description>We propose a novel methodology for estimating the epidemiological parameters
of a modified SIRD model (acronym of Susceptible, Infected, Recovered and
Deceased individuals) and perform a short-term forecast of SARS-CoV-2 virus
spread. We mainly focus on forecasting number of deceased. The procedure was
tested on reported data for Poland. For some short-time intervals we performed
numerical test investigating stability of parameter estimates in the proposed
approach. Numerical experiments confirm the effectiveness of short-term
forecasts (up to 2 weeks) and stability of the method. To improve their
performance (i.e. computation time) GPU architecture was used in computations.</description><subject>Statistics - Machine Learning</subject><subject>Statistics - Methodology</subject><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>GOX</sourceid><recordid>eNotj8tKxDAYhbNxIaMP4Mr_BVpzb7KUjpfCQEWH2Za_TaKBthliFX176-jicM7i48BHyBWjpTRK0RvMX_Gz5JzKknEh9Tk5tDPgx5ImXOIAA46xz-tMM6QAy5uHl-Z5C_4YnZ9iGtNrXBmYkvMjhJShbg_NtmAWHC4IcYanNOLsLshZwPHdX_73huzv7_b1Y7FrH5r6dlegrnThtOBrDHXCSKcqZpUYTNBIPVVMWhQ2BNs7Rrl3FVqNkjPFuOuD6dEysSHXf7cnse6Y44T5u_sV7E6C4gcYnEmg</recordid><startdate>20220426</startdate><enddate>20220426</enddate><creator>Błaszczyk, Piotr</creator><creator>Klimczak, Konrad</creator><creator>Mahdi, Adam</creator><creator>Oprocha, Piotr</creator><creator>Potorski, Paweł</creator><creator>Przybyłowicz, Paweł</creator><creator>Sobieraj, Michał</creator><scope>EPD</scope><scope>GOX</scope></search><sort><creationdate>20220426</creationdate><title>On automatic calibration of the SIRD epidemiological model for COVID-19 data in Poland</title><author>Błaszczyk, Piotr ; Klimczak, Konrad ; Mahdi, Adam ; Oprocha, Piotr ; Potorski, Paweł ; Przybyłowicz, Paweł ; Sobieraj, Michał</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-a676-d632d6380d384d571953c8f6a0e05149a39ff9bd102ed7a96a421512dbf8ba913</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Statistics - Machine Learning</topic><topic>Statistics - Methodology</topic><toplevel>online_resources</toplevel><creatorcontrib>Błaszczyk, Piotr</creatorcontrib><creatorcontrib>Klimczak, Konrad</creatorcontrib><creatorcontrib>Mahdi, Adam</creatorcontrib><creatorcontrib>Oprocha, Piotr</creatorcontrib><creatorcontrib>Potorski, Paweł</creatorcontrib><creatorcontrib>Przybyłowicz, Paweł</creatorcontrib><creatorcontrib>Sobieraj, Michał</creatorcontrib><collection>arXiv Statistics</collection><collection>arXiv.org</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Błaszczyk, Piotr</au><au>Klimczak, Konrad</au><au>Mahdi, Adam</au><au>Oprocha, Piotr</au><au>Potorski, Paweł</au><au>Przybyłowicz, Paweł</au><au>Sobieraj, Michał</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>On automatic calibration of the SIRD epidemiological model for COVID-19 data in Poland</atitle><date>2022-04-26</date><risdate>2022</risdate><abstract>We propose a novel methodology for estimating the epidemiological parameters
of a modified SIRD model (acronym of Susceptible, Infected, Recovered and
Deceased individuals) and perform a short-term forecast of SARS-CoV-2 virus
spread. We mainly focus on forecasting number of deceased. The procedure was
tested on reported data for Poland. For some short-time intervals we performed
numerical test investigating stability of parameter estimates in the proposed
approach. Numerical experiments confirm the effectiveness of short-term
forecasts (up to 2 weeks) and stability of the method. To improve their
performance (i.e. computation time) GPU architecture was used in computations.</abstract><doi>10.48550/arxiv.2204.12346</doi><oa>free_for_read</oa></addata></record> |
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| subjects | Statistics - Machine Learning Statistics - Methodology |
| title | On automatic calibration of the SIRD epidemiological model for COVID-19 data in Poland |
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