Yan Ma, Shiva Raj Mishra, Xi-Kun Han, Dong-Shan Zhu
{"title":"COVID-19高水平应对时间与日发病高峰时间之间的关系:对148个国家政府严格程度指数的分析","authors":"Yan Ma, Shiva Raj Mishra, Xi-Kun Han, Dong-Shan Zhu","doi":"10.1186/s40249-021-00880-x","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>The transmission dynamics and severity of coronavirus disease 2019 (COVID-19) pandemic is different across countries or regions. Differences in governments' policy responses may explain some of these differences. We aimed to compare worldwide government responses to the spread of COVID-19, to examine the relationship between response level, response timing and the epidemic trajectory.</p><p><strong>Methods: </strong>Free publicly-accessible data collected by the Coronavirus Government Response Tracker (OxCGRT) were used. Nine sub-indicators reflecting government response from 148 countries were collected systematically from January 1 to May 1, 2020. The sub-indicators were scored and were aggregated into a common Stringency Index (SI, a value between 0 and 100) that reflects the overall stringency of the government's response in a daily basis. Group-based trajectory modelling method was used to identify trajectories of SI. Multivariable linear regression models were used to analyse the association between time to reach a high-level SI and time to the peak number of daily new cases.</p><p><strong>Results: </strong>Our results identified four trajectories of response in the spread of COVID-19 based on when the response was initiated: before January 13, from January 13 to February 12, from February 12 to March 11, and the last stage-from March 11 (the day WHO declared a pandemic of COVID-19) on going. Governments' responses were upgraded with further spread of COVID-19 but varied substantially across countries. After the adjustment of SI level, geographical region and initiation stages, each day earlier to a high SI level (SI > 80) from the start of response was associated with 0.44 (standard error: 0.08, P < 0.001, R<sup>2</sup> = 0.65) days earlier to the peak number of daily new case. Also, each day earlier to a high SI level from the date of first reported case was associated with 0.65 (standard error: 0.08, P < 0.001, R<sup>2</sup> = 0.42) days earlier to the peak number of daily new case.</p><p><strong>Conclusions: </strong>Early start of a high-level response to COVID-19 is associated with early arrival of the peak number of daily new cases. This may help to reduce the delays in flattening the epidemic curve to the low spread level.</p>","PeriodicalId":13587,"journal":{"name":"Infectious Diseases of Poverty","volume":"10 1","pages":"96"},"PeriodicalIF":4.8000,"publicationDate":"2021-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1186/s40249-021-00880-x","citationCount":"25","resultStr":"{\"title\":\"The relationship between time to a high COVID-19 response level and timing of peak daily incidence: an analysis of governments' Stringency Index from 148 countries.\",\"authors\":\"Yan Ma, Shiva Raj Mishra, Xi-Kun Han, Dong-Shan Zhu\",\"doi\":\"10.1186/s40249-021-00880-x\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>The transmission dynamics and severity of coronavirus disease 2019 (COVID-19) pandemic is different across countries or regions. Differences in governments' policy responses may explain some of these differences. We aimed to compare worldwide government responses to the spread of COVID-19, to examine the relationship between response level, response timing and the epidemic trajectory.</p><p><strong>Methods: </strong>Free publicly-accessible data collected by the Coronavirus Government Response Tracker (OxCGRT) were used. Nine sub-indicators reflecting government response from 148 countries were collected systematically from January 1 to May 1, 2020. The sub-indicators were scored and were aggregated into a common Stringency Index (SI, a value between 0 and 100) that reflects the overall stringency of the government's response in a daily basis. Group-based trajectory modelling method was used to identify trajectories of SI. Multivariable linear regression models were used to analyse the association between time to reach a high-level SI and time to the peak number of daily new cases.</p><p><strong>Results: </strong>Our results identified four trajectories of response in the spread of COVID-19 based on when the response was initiated: before January 13, from January 13 to February 12, from February 12 to March 11, and the last stage-from March 11 (the day WHO declared a pandemic of COVID-19) on going. Governments' responses were upgraded with further spread of COVID-19 but varied substantially across countries. After the adjustment of SI level, geographical region and initiation stages, each day earlier to a high SI level (SI > 80) from the start of response was associated with 0.44 (standard error: 0.08, P < 0.001, R<sup>2</sup> = 0.65) days earlier to the peak number of daily new case. Also, each day earlier to a high SI level from the date of first reported case was associated with 0.65 (standard error: 0.08, P < 0.001, R<sup>2</sup> = 0.42) days earlier to the peak number of daily new case.</p><p><strong>Conclusions: </strong>Early start of a high-level response to COVID-19 is associated with early arrival of the peak number of daily new cases. 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The relationship between time to a high COVID-19 response level and timing of peak daily incidence: an analysis of governments' Stringency Index from 148 countries.
Background: The transmission dynamics and severity of coronavirus disease 2019 (COVID-19) pandemic is different across countries or regions. Differences in governments' policy responses may explain some of these differences. We aimed to compare worldwide government responses to the spread of COVID-19, to examine the relationship between response level, response timing and the epidemic trajectory.
Methods: Free publicly-accessible data collected by the Coronavirus Government Response Tracker (OxCGRT) were used. Nine sub-indicators reflecting government response from 148 countries were collected systematically from January 1 to May 1, 2020. The sub-indicators were scored and were aggregated into a common Stringency Index (SI, a value between 0 and 100) that reflects the overall stringency of the government's response in a daily basis. Group-based trajectory modelling method was used to identify trajectories of SI. Multivariable linear regression models were used to analyse the association between time to reach a high-level SI and time to the peak number of daily new cases.
Results: Our results identified four trajectories of response in the spread of COVID-19 based on when the response was initiated: before January 13, from January 13 to February 12, from February 12 to March 11, and the last stage-from March 11 (the day WHO declared a pandemic of COVID-19) on going. Governments' responses were upgraded with further spread of COVID-19 but varied substantially across countries. After the adjustment of SI level, geographical region and initiation stages, each day earlier to a high SI level (SI > 80) from the start of response was associated with 0.44 (standard error: 0.08, P < 0.001, R2 = 0.65) days earlier to the peak number of daily new case. Also, each day earlier to a high SI level from the date of first reported case was associated with 0.65 (standard error: 0.08, P < 0.001, R2 = 0.42) days earlier to the peak number of daily new case.
Conclusions: Early start of a high-level response to COVID-19 is associated with early arrival of the peak number of daily new cases. This may help to reduce the delays in flattening the epidemic curve to the low spread level.
期刊介绍：
Infectious Diseases of Poverty is a peer-reviewed, open access journal that focuses on essential public health questions related to infectious diseases of poverty. It covers a wide range of topics and methods, including the biology of pathogens and vectors, diagnosis and detection, treatment and case management, epidemiology and modeling, zoonotic hosts and animal reservoirs, control strategies and implementation, new technologies, and their application.
The journal also explores the impact of transdisciplinary or multisectoral approaches on health systems, ecohealth, environmental management, and innovative technologies. It aims to provide a platform for the exchange of research and ideas that can contribute to the improvement of public health in resource-limited settings.
In summary, Infectious Diseases of Poverty aims to address the urgent challenges posed by infectious diseases in impoverished populations. By publishing high-quality research in various areas, the journal seeks to advance our understanding of these diseases and contribute to the development of effective strategies for prevention, diagnosis, and treatment.
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