tetano
Editor, Senior Moderator
JMIR Public Health Surveill. 2020 May 12. doi: 10.2196/18638. [Epub ahead of print]
An Evaluation Model of COVID-19 Spread Control and Prevention: Effectiveness Analysis Based on Immigration Population Data in China.
Huang Q[SUP]1[/SUP], Kang YS[SUP]2[/SUP].
Author information
Abstract
BACKGROUND:
Compared to the peak data in early February, 2020, the spread of Coronavirus (COVID-19) has been drastically slowed down and come under control in China, as reported by the end of February 2020. Meanwhile, the outcomes of control and prevention of COVID-19 varied among different regions (i.e. provinces and municipalities) in China; moreover, COVID-19 became a global pandemic and the spread of disease accelerated among other countries outside China.
OBJECTIVE:
This study aimed to establish valid models which will evaluate the effectiveness of COVID-19 control and prevention among various regions in China. These models also targeted regions with problems in control and prevention by issuing immediate warnings.
METHODS:
We built a mathematical model: the Epidemic Risk Time Series Model, based on which, we analyzed two sets of data, including the daily number of COVID-19 incidence (i.e., newly-diagnosed cases) as well as the daily immigration population size.
RESULTS:
Based on the model's evaluation result, some regions, such as Shanghai and Zhejiang, were successful in COVID-19 control and prevention; whereas other regions yielded poor performance, such as Heilongjiang. The evaluation result was highly correlated with R0 value, and the result was evaluated within a timely manner at the beginning of disease outbreak.
CONCLUSIONS:
The Epidemic Risk Time Series Model was designed to evaluate the effectiveness of COVID-19 epidemic control and prevention among different regions in China, based on an analysis of immigration population data. Compared to other methods, such as R0, this model was able to issue early warnings more promptly. This model can be generalized and applied to other countries regarding evaluations of COVID-19 control and prevention.
CLINICALTRIAL:
PMID:32396132DOI:10.2196/18638
An Evaluation Model of COVID-19 Spread Control and Prevention: Effectiveness Analysis Based on Immigration Population Data in China.
Huang Q[SUP]1[/SUP], Kang YS[SUP]2[/SUP].
Author information
Abstract
BACKGROUND:
Compared to the peak data in early February, 2020, the spread of Coronavirus (COVID-19) has been drastically slowed down and come under control in China, as reported by the end of February 2020. Meanwhile, the outcomes of control and prevention of COVID-19 varied among different regions (i.e. provinces and municipalities) in China; moreover, COVID-19 became a global pandemic and the spread of disease accelerated among other countries outside China.
OBJECTIVE:
This study aimed to establish valid models which will evaluate the effectiveness of COVID-19 control and prevention among various regions in China. These models also targeted regions with problems in control and prevention by issuing immediate warnings.
METHODS:
We built a mathematical model: the Epidemic Risk Time Series Model, based on which, we analyzed two sets of data, including the daily number of COVID-19 incidence (i.e., newly-diagnosed cases) as well as the daily immigration population size.
RESULTS:
Based on the model's evaluation result, some regions, such as Shanghai and Zhejiang, were successful in COVID-19 control and prevention; whereas other regions yielded poor performance, such as Heilongjiang. The evaluation result was highly correlated with R0 value, and the result was evaluated within a timely manner at the beginning of disease outbreak.
CONCLUSIONS:
The Epidemic Risk Time Series Model was designed to evaluate the effectiveness of COVID-19 epidemic control and prevention among different regions in China, based on an analysis of immigration population data. Compared to other methods, such as R0, this model was able to issue early warnings more promptly. This model can be generalized and applied to other countries regarding evaluations of COVID-19 control and prevention.
CLINICALTRIAL:
PMID:32396132DOI:10.2196/18638