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Measuring the worldwide spread of COVID-19 using a comprehensive modeling method.
Zhou, Xiang; Ma, Xudong; Gao, Sifa; Ma, Yingying; Gao, Jianwei; Jiang, Huizhen; Zhu, Weiguo; Hong, Na; Long, Yun; Su, Longxiang.
Afiliação
  • Zhou X; Department of Critical Care Medicine, State Key Laboratory for Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, 100730, China.
  • Ma X; Department of Medical Administration, National Health Commission of the People's Republic of China, Beijing, 100044, China.
  • Gao S; Department of Medical Administration, National Health Commission of the People's Republic of China, Beijing, 100044, China.
  • Ma Y; Digital Health China Technologies Co. Ltd, Beijing, 100080, China.
  • Gao J; Digital Health China Technologies Co. Ltd, Beijing, 100080, China.
  • Jiang H; Department of Information Management, Peking Union Medical College Hospital, Peking Union Medical College & Chinese Academy of Medical Sciences, Beijing, 100730, China.
  • Zhu W; Department of Information Management, Peking Union Medical College Hospital, Peking Union Medical College & Chinese Academy of Medical Sciences, Beijing, 100730, China.
  • Hong N; Digital Health China Technologies Co. Ltd, Beijing, 100080, China. h_na@163.com.
  • Long Y; Department of Critical Care Medicine, State Key Laboratory for Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, 100730, China. ly_icu@aliyun.com.
  • Su L; Department of Critical Care Medicine, State Key Laboratory for Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, 100730, China. sulongxiang@vip.163.com.
BMC Med Inform Decis Mak ; 21(Suppl 9): 384, 2023 09 15.
Article em En | MEDLINE | ID: mdl-37715170
ABSTRACT

BACKGROUND:

With the global spread of COVID-19, detecting high-risk countries/regions timely and dynamically is essential; therefore, we sought to develop automatic, quantitative and scalable analysis methods to observe and estimate COVID-19 spread worldwide and further generate reliable and timely decision-making support for public health management using a comprehensive modeling method based on multiple mathematical models.

METHODS:

We collected global COVID-19 epidemic data reported from January 23 to September 30, 2020, to observe and estimate its possible spread trends. Countries were divided into three outbreak levels high, middle, and low. Trends analysis was performed by calculating the growth rate, and then country grouping was implemented using group-based trajectory modeling on the three levels. Individual countries from each group were also chosen to further disclose the outbreak situations using two predicting models the logistic growth model and the SEIR model.

RESULTS:

All 187 observed countries' trajectory subgroups were identified using two grouping strategies with and without population consideration. By measuring epidemic trends and predicting the epidemic size and peak of individual countries, our study found that the logistic growth model generally estimated a smaller epidemic size than the SEIR model. According to SEIR modeling, confirmed cases in each country would take an average of 9-12 months to reach the outbreak peak from the day the first case occurred. Additionally, the average number of cases at the peak time will reach approximately 10-20% of the countries' populations, and the countries with high trends and a high predicted size must pay special attention and implement public health interventions in a timely manner.

CONCLUSIONS:

We demonstrated comprehensive observations and predictions of the COVID-19 outbreak in 187 countries using a comprehensive modeling method. The methods proposed in this study can measure COVID-19 development from multiple perspectives and are generalizable to other epidemic diseases. Furthermore, the methods also provide reliable and timely decision-making support for public health management.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Epidemias / COVID-19 Tipo de estudo: Prognostic_studies / Risk_factors_studies Limite: Humans Idioma: En Ano de publicação: 2023 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Epidemias / COVID-19 Tipo de estudo: Prognostic_studies / Risk_factors_studies Limite: Humans Idioma: En Ano de publicação: 2023 Tipo de documento: Article