A Prospective Method for Generating COVID-19 Dynamics
Generating dynamic operators are constructed here from the cumulative case function to recover all state dynamics of a Susceptible–Exposed–Infectious–Recovered (SEIR) model for COVID-19 transmission. In this study, recorded and unrecorded EIRs and a time-dependent infection rate are taken into accou...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-06-01
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| Series: | Computation |
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| Online Access: | https://www.mdpi.com/2079-3197/10/7/107 |
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| author | Kamal Khairudin Sukandar Andy Leonardo Louismono Metra Volisa Rudy Kusdiantara Muhammad Fakhruddin Nuning Nuraini Edy Soewono |
| author_facet | Kamal Khairudin Sukandar Andy Leonardo Louismono Metra Volisa Rudy Kusdiantara Muhammad Fakhruddin Nuning Nuraini Edy Soewono |
| author_sort | Kamal Khairudin Sukandar |
| collection | DOAJ |
| description | Generating dynamic operators are constructed here from the cumulative case function to recover all state dynamics of a Susceptible–Exposed–Infectious–Recovered (SEIR) model for COVID-19 transmission. In this study, recorded and unrecorded EIRs and a time-dependent infection rate are taken into account to accommodate immeasurable control and intervention processes. Generating dynamic operators are built and implemented on the cumulative cases. All infection processes, which are hidden in this cumulative function, can be recovered entirely by implementing the generating operators. Direct implementation of the operators on the cumulative function gives all recorded state dynamics. Further, the unrecorded daily infection rate is estimated from the ratio between IFR and CFR. The remaining dynamics of unrecorded states are directly obtained from the generating operators. The simulations are conducted using infection data provided by Worldometers from ten selected countries. It is shown that the higher number of daily PCR tests contributed directly to reducing the effective reproduction ratio. The simulations of all state dynamics, infection rates, and effective reproduction ratios for several countries in the first and second waves of transmissions are presented. This method directly measures daily transmission indicators, which can be effectively used for the day-to-day control of the epidemic. |
| first_indexed | 2024-03-09T03:33:17Z |
| format | Article |
| id | doaj.art-2e5dd1d910e64415924fb86ae148c21e |
| institution | Directory Open Access Journal |
| issn | 2079-3197 |
| language | English |
| last_indexed | 2024-03-09T03:33:17Z |
| publishDate | 2022-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Computation |
| spelling | doaj.art-2e5dd1d910e64415924fb86ae148c21e2023-12-03T14:51:50ZengMDPI AGComputation2079-31972022-06-0110710710.3390/computation10070107A Prospective Method for Generating COVID-19 DynamicsKamal Khairudin Sukandar0Andy Leonardo Louismono1Metra Volisa2Rudy Kusdiantara3Muhammad Fakhruddin4Nuning Nuraini5Edy Soewono6Faculty of Mathematics and Natural Sciences, Institut Teknologi Bandung, Bandung 40132, IndonesiaFaculty of Mathematics and Natural Sciences, Institut Teknologi Bandung, Bandung 40132, IndonesiaFaculty of Mathematics and Natural Sciences, Institut Teknologi Bandung, Bandung 40132, IndonesiaFaculty of Mathematics and Natural Sciences, Institut Teknologi Bandung, Bandung 40132, IndonesiaDepartment of Mathematics, Faculty of Military Mathematics and Natural Sciences, The Republic of Indonesia Defense University, IPSC Area, Sentul, Bogor 16810, IndonesiaFaculty of Mathematics and Natural Sciences, Institut Teknologi Bandung, Bandung 40132, IndonesiaFaculty of Mathematics and Natural Sciences, Institut Teknologi Bandung, Bandung 40132, IndonesiaGenerating dynamic operators are constructed here from the cumulative case function to recover all state dynamics of a Susceptible–Exposed–Infectious–Recovered (SEIR) model for COVID-19 transmission. In this study, recorded and unrecorded EIRs and a time-dependent infection rate are taken into account to accommodate immeasurable control and intervention processes. Generating dynamic operators are built and implemented on the cumulative cases. All infection processes, which are hidden in this cumulative function, can be recovered entirely by implementing the generating operators. Direct implementation of the operators on the cumulative function gives all recorded state dynamics. Further, the unrecorded daily infection rate is estimated from the ratio between IFR and CFR. The remaining dynamics of unrecorded states are directly obtained from the generating operators. The simulations are conducted using infection data provided by Worldometers from ten selected countries. It is shown that the higher number of daily PCR tests contributed directly to reducing the effective reproduction ratio. The simulations of all state dynamics, infection rates, and effective reproduction ratios for several countries in the first and second waves of transmissions are presented. This method directly measures daily transmission indicators, which can be effectively used for the day-to-day control of the epidemic.https://www.mdpi.com/2079-3197/10/7/107COVID-19SEIR modelsdynamics generatorunrecorded infectionsRichard’s curve |
| spellingShingle | Kamal Khairudin Sukandar Andy Leonardo Louismono Metra Volisa Rudy Kusdiantara Muhammad Fakhruddin Nuning Nuraini Edy Soewono A Prospective Method for Generating COVID-19 Dynamics Computation COVID-19 SEIR models dynamics generator unrecorded infections Richard’s curve |
| title | A Prospective Method for Generating COVID-19 Dynamics |
| title_full | A Prospective Method for Generating COVID-19 Dynamics |
| title_fullStr | A Prospective Method for Generating COVID-19 Dynamics |
| title_full_unstemmed | A Prospective Method for Generating COVID-19 Dynamics |
| title_short | A Prospective Method for Generating COVID-19 Dynamics |
| title_sort | prospective method for generating covid 19 dynamics |
| topic | COVID-19 SEIR models dynamics generator unrecorded infections Richard’s curve |
| url | https://www.mdpi.com/2079-3197/10/7/107 |
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