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Exploring the influence of human mobility factors and spread prediction on early COVID-19 in the USA.
Zheng, Zhicheng; Xie, Zhixiang; Qin, Yaochen; Wang, Kun; Yu, Yan; Fu, Pinde.
Afiliación
  • Zheng Z; Key Laboratory of Geospatial Technology for Middle and Lower Yellow River Region / College of Environment and Planning Henan University, Jinming Road, Kaifeng, 475004, China.
  • Xie Z; College of Surveying and Geo-informatics, North China University of Water Resources and Electric Power, Zhengzhou, 450046, China.
  • Qin Y; Key Laboratory of Geospatial Technology for Middle and Lower Yellow River Region / College of Environment and Planning Henan University, Jinming Road, Kaifeng, 475004, China. qinyc@henu.edu.cn.
  • Wang K; Key Research Institute of Yellow River Civilization and Sustainable Development & Collaborative Innovation Center on Yellow River Civilization jointly built by Henan Province and Ministry of Education, Henan University, Kaifeng, 475001, China. qinyc@henu.edu.cn.
  • Yu Y; Key Laboratory of Geospatial Technology for Middle and Lower Yellow River Region / College of Environment and Planning Henan University, Jinming Road, Kaifeng, 475004, China.
  • Fu P; Key Laboratory of Geospatial Technology for Middle and Lower Yellow River Region / College of Environment and Planning Henan University, Jinming Road, Kaifeng, 475004, China.
BMC Public Health ; 21(1): 615, 2021 03 29.
Article en En | MEDLINE | ID: mdl-33781260
ABSTRACT

BACKGROUND:

COVID-19 is still spreading rapidly around the world. In this context, how to accurately predict the turning point, duration and final scale of the epidemic in different countries, regions or cities is key to enabling decision makers and public health departments to formulate intervention measures and deploy resources.

METHODS:

Based on COVID-19 surveillance data and human mobility data, this study predicts the epidemic trends of national and state regional administrative units in the United States from July 27, 2020, to January 22, 2021, by constructing a SIRD model considering the factors of "lockdown" and "riot".

RESULTS:

(1) The spread of the epidemic in the USA has the characteristics of geographical proximity. (2) During the lockdown period, there was a strong correlation between the number of COVID-19 infected cases and residents' activities in recreational areas such as parks. (3) The turning point (the point of time in which active infected cases peak) of the early epidemic in the USA was predicted to occur in September. (4) Among the 10 states experiencing the most severe epidemic, New York, New Jersey, Massachusetts, Texas, Illinois, Pennsylvania and California are all predicted to meet the turning point in a concentrated period from July to September, while the turning point in Georgia is forecast to occur in December. No turning points in Florida and Arizona were foreseen for the forecast period, with the number of infected cases still set to be growing rapidly.

CONCLUSIONS:

The model was found accurately to predict the future trend of the epidemic and can be applied to other countries. It is worth noting that in the early stage there is no vaccine or approved pharmaceutical intervention for this disease, making the fight against the pandemic reliant on non-pharmaceutical interventions. Therefore, reducing mobility, focusing on personal protection and increasing social distance remain still the most effective measures to date.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Pandemias / Migración Humana / COVID-19 Tipo de estudio: Prognostic_studies / Risk_factors_studies Límite: Humans País/Región como asunto: America do norte Idioma: En Revista: BMC Public Health Asunto de la revista: SAUDE PUBLICA Año: 2021 Tipo del documento: Article País de afiliación: China

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Pandemias / Migración Humana / COVID-19 Tipo de estudio: Prognostic_studies / Risk_factors_studies Límite: Humans País/Región como asunto: America do norte Idioma: En Revista: BMC Public Health Asunto de la revista: SAUDE PUBLICA Año: 2021 Tipo del documento: Article País de afiliación: China