Policy and Law Assessment of COVID-19 Based on Smooth Transition Autoregressive Model
As of the end of October 2020, the cumulative number of confirmed cases of COVID-19 has exceeded 45 million and the cumulative number of deaths has exceeded 1.1 million all over the world. Faced with the fatal pandemic, countries around the world have taken various prevention and control measures. O...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2021-01-01
|
| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2021/6659117 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832550489494913024 |
|---|---|
| author | Jieqi Lei Xuyuan Wang Yiming Zhang Lian Zhu Lin Zhang |
| author_facet | Jieqi Lei Xuyuan Wang Yiming Zhang Lian Zhu Lin Zhang |
| author_sort | Jieqi Lei |
| collection | DOAJ |
| description | As of the end of October 2020, the cumulative number of confirmed cases of COVID-19 has exceeded 45 million and the cumulative number of deaths has exceeded 1.1 million all over the world. Faced with the fatal pandemic, countries around the world have taken various prevention and control measures. One of the important issues in epidemic prevention and control is the assessment of the prevention and control effectiveness. Changes in the time series of daily new confirmed cases can reflect the impact of policies in certain regions. In this paper, a smooth transition autoregressive (STAR) model is applied to investigate the intrinsic changes during the epidemic in certain countries and regions. In order to quantitatively evaluate the influence of the epidemic control measures, the sequence is fitted to the STAR model; then, comparisons between the dates of transition points and those of releasing certain policies are applied. Our model well fits the data. Moreover, the nonlinear smooth function within the STAR model reveals that the implementation of prevention and control policies is effective in some regions with different speeds. However, the ineffectiveness is also revealed and the threat of a second wave had already emerged. |
| format | Article |
| id | doaj-art-65e9453fb107421cb666e0b4591bbf32 |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-65e9453fb107421cb666e0b4591bbf322025-02-03T06:06:34ZengWileyComplexity1076-27871099-05262021-01-01202110.1155/2021/66591176659117Policy and Law Assessment of COVID-19 Based on Smooth Transition Autoregressive ModelJieqi Lei0Xuyuan Wang1Yiming Zhang2Lian Zhu3Lin Zhang4School of Humanities, Beijing University of Posts and Telecommunications, Beijing 100876, ChinaSchool of Science, Beijing University of Posts and Telecommunications, Beijing 100876, ChinaInternational School, Beijing University of Posts and Telecommunications, Beijing 1000876, ChinaSchool of Journalism and Communication, Shanghai International Studies University, Shanghai 200083, ChinaSchool of Science, Beijing University of Posts and Telecommunications, Beijing 100876, ChinaAs of the end of October 2020, the cumulative number of confirmed cases of COVID-19 has exceeded 45 million and the cumulative number of deaths has exceeded 1.1 million all over the world. Faced with the fatal pandemic, countries around the world have taken various prevention and control measures. One of the important issues in epidemic prevention and control is the assessment of the prevention and control effectiveness. Changes in the time series of daily new confirmed cases can reflect the impact of policies in certain regions. In this paper, a smooth transition autoregressive (STAR) model is applied to investigate the intrinsic changes during the epidemic in certain countries and regions. In order to quantitatively evaluate the influence of the epidemic control measures, the sequence is fitted to the STAR model; then, comparisons between the dates of transition points and those of releasing certain policies are applied. Our model well fits the data. Moreover, the nonlinear smooth function within the STAR model reveals that the implementation of prevention and control policies is effective in some regions with different speeds. However, the ineffectiveness is also revealed and the threat of a second wave had already emerged.http://dx.doi.org/10.1155/2021/6659117 |
| spellingShingle | Jieqi Lei Xuyuan Wang Yiming Zhang Lian Zhu Lin Zhang Policy and Law Assessment of COVID-19 Based on Smooth Transition Autoregressive Model Complexity |
| title | Policy and Law Assessment of COVID-19 Based on Smooth Transition Autoregressive Model |
| title_full | Policy and Law Assessment of COVID-19 Based on Smooth Transition Autoregressive Model |
| title_fullStr | Policy and Law Assessment of COVID-19 Based on Smooth Transition Autoregressive Model |
| title_full_unstemmed | Policy and Law Assessment of COVID-19 Based on Smooth Transition Autoregressive Model |
| title_short | Policy and Law Assessment of COVID-19 Based on Smooth Transition Autoregressive Model |
| title_sort | policy and law assessment of covid 19 based on smooth transition autoregressive model |
| url | http://dx.doi.org/10.1155/2021/6659117 |
| work_keys_str_mv | AT jieqilei policyandlawassessmentofcovid19basedonsmoothtransitionautoregressivemodel AT xuyuanwang policyandlawassessmentofcovid19basedonsmoothtransitionautoregressivemodel AT yimingzhang policyandlawassessmentofcovid19basedonsmoothtransitionautoregressivemodel AT lianzhu policyandlawassessmentofcovid19basedonsmoothtransitionautoregressivemodel AT linzhang policyandlawassessmentofcovid19basedonsmoothtransitionautoregressivemodel |