Assessing Interventions against Coronavirus Disease 2019 (COVID-19) in Osaka, Japan: A Modeling Study
Estimation of the effective reproduction number, <i>R</i>(<i>t</i>), of coronavirus disease (COVID-19) in real-time is a continuing challenge. <i>R</i>(<i>t</i>) reflects the epidemic dynamics based on readily available illness onset data, and is usefu...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-03-01
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| Series: | Journal of Clinical Medicine |
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| Online Access: | https://www.mdpi.com/2077-0383/10/6/1256 |
| _version_ | 1797540917821833216 |
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| author | Ko Nakajo Hiroshi Nishiura |
| author_facet | Ko Nakajo Hiroshi Nishiura |
| author_sort | Ko Nakajo |
| collection | DOAJ |
| description | Estimation of the effective reproduction number, <i>R</i>(<i>t</i>), of coronavirus disease (COVID-19) in real-time is a continuing challenge. <i>R</i>(<i>t</i>) reflects the epidemic dynamics based on readily available illness onset data, and is useful for the planning and implementation of public health and social measures. In the present study, we proposed a method for computing the <i>R</i>(<i>t</i>) of COVID-19, and applied this method to the epidemic in Osaka prefecture from February to September 2020. We estimated <i>R</i>(<i>t</i>) as a function of the time of infection using the date of illness onset. The epidemic in Osaka came under control around 2 April during the first wave, and 26 July during the second wave. <i>R</i>(<i>t</i>) did not decline drastically following any single intervention. However, when multiple interventions were combined, the relative reductions in <i>R</i>(<i>t</i>) during the first and second waves were 70% and 51%, respectively. Although the second wave was brought under control without declaring a state of emergency, our model comparison indicated that relying on a single intervention would not be sufficient to reduce <i>R</i>(<i>t</i>) < 1. The outcome of the COVID-19 pandemic continues to rely on political leadership to swiftly design and implement combined interventions capable of broadly and appropriately reducing contacts. |
| first_indexed | 2024-03-10T13:07:54Z |
| format | Article |
| id | doaj.art-90273a77e970409dbb0896ce866755c0 |
| institution | Directory Open Access Journal |
| issn | 2077-0383 |
| language | English |
| last_indexed | 2024-03-10T13:07:54Z |
| publishDate | 2021-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Clinical Medicine |
| spelling | doaj.art-90273a77e970409dbb0896ce866755c02023-11-21T10:59:50ZengMDPI AGJournal of Clinical Medicine2077-03832021-03-01106125610.3390/jcm10061256Assessing Interventions against Coronavirus Disease 2019 (COVID-19) in Osaka, Japan: A Modeling StudyKo Nakajo0Hiroshi Nishiura1Kyoto University School of Public Health, Yoshida-Konoe-cho, Sakyo-ku, Kyoto 606-8501, JapanKyoto University School of Public Health, Yoshida-Konoe-cho, Sakyo-ku, Kyoto 606-8501, JapanEstimation of the effective reproduction number, <i>R</i>(<i>t</i>), of coronavirus disease (COVID-19) in real-time is a continuing challenge. <i>R</i>(<i>t</i>) reflects the epidemic dynamics based on readily available illness onset data, and is useful for the planning and implementation of public health and social measures. In the present study, we proposed a method for computing the <i>R</i>(<i>t</i>) of COVID-19, and applied this method to the epidemic in Osaka prefecture from February to September 2020. We estimated <i>R</i>(<i>t</i>) as a function of the time of infection using the date of illness onset. The epidemic in Osaka came under control around 2 April during the first wave, and 26 July during the second wave. <i>R</i>(<i>t</i>) did not decline drastically following any single intervention. However, when multiple interventions were combined, the relative reductions in <i>R</i>(<i>t</i>) during the first and second waves were 70% and 51%, respectively. Although the second wave was brought under control without declaring a state of emergency, our model comparison indicated that relying on a single intervention would not be sufficient to reduce <i>R</i>(<i>t</i>) < 1. The outcome of the COVID-19 pandemic continues to rely on political leadership to swiftly design and implement combined interventions capable of broadly and appropriately reducing contacts.https://www.mdpi.com/2077-0383/10/6/1256epidemiologysevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)coronavirus disease 2019 (COVID-19)public healthcontrolmathematical model |
| spellingShingle | Ko Nakajo Hiroshi Nishiura Assessing Interventions against Coronavirus Disease 2019 (COVID-19) in Osaka, Japan: A Modeling Study Journal of Clinical Medicine epidemiology severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) coronavirus disease 2019 (COVID-19) public health control mathematical model |
| title | Assessing Interventions against Coronavirus Disease 2019 (COVID-19) in Osaka, Japan: A Modeling Study |
| title_full | Assessing Interventions against Coronavirus Disease 2019 (COVID-19) in Osaka, Japan: A Modeling Study |
| title_fullStr | Assessing Interventions against Coronavirus Disease 2019 (COVID-19) in Osaka, Japan: A Modeling Study |
| title_full_unstemmed | Assessing Interventions against Coronavirus Disease 2019 (COVID-19) in Osaka, Japan: A Modeling Study |
| title_short | Assessing Interventions against Coronavirus Disease 2019 (COVID-19) in Osaka, Japan: A Modeling Study |
| title_sort | assessing interventions against coronavirus disease 2019 covid 19 in osaka japan a modeling study |
| topic | epidemiology severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) coronavirus disease 2019 (COVID-19) public health control mathematical model |
| url | https://www.mdpi.com/2077-0383/10/6/1256 |
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