Risk assessment for precise intervention of COVID-19 epidemic based on available big data and spatio-temporal simulation method: Empirical evidence from different public places in Guangzhou, China.
Appl Geogr
; 143: 102702, 2022 Jun.
Artigo
em Inglês
| MEDLINE | ID: covidwho-1797188
ABSTRACT
Risk assessment of the intra-city spatio-temporal spreading of COVID-19 is important for providing location-based precise intervention measures, especially when the epidemic occurred in the densely populated and high mobile public places. The individual-based simulation has been proven to be an effective method for the risk assessment. However, the acquisition of individual-level mobility data is limited. This study used publicly available datasets to approximate dynamic intra-city travel flows by a spatio-temporal gravity model. On this basis, an individual-based epidemic model integrating agent-based model with the susceptible-exposed-infectious-removed (SEIR) model was proposed and the intra-city spatio-temporal spreading process of COVID-19 in eleven public places in Guangzhou China were explored. The results indicated that the accuracy of dynamic intra-city travel flows estimated by available big data and gravity model is acceptable. The spatio-temporal simulation method well presented the process of COVID-19 epidemic. Four kinds of spatial-temporal transmission patterns were identified and the pattern was highly dependent on the urban spatial structure and location. It indicated that location-based precise intervention measures should be implemented according to different regions. The approach of this research can be used by policy-makers to make rapid and accurate risk assessments and to implement intervention measures ahead of epidemic outbreaks.
Texto completo:
Disponível
Coleções:
Bases de dados internacionais
Base de dados:
MEDLINE
Tipo de estudo:
Estudo experimental
/
Estudo prognóstico
Idioma:
Inglês
Revista:
Appl Geogr
Ano de publicação:
2022
Tipo de documento:
Artigo
País de afiliação:
J.apgeog.2022.102702
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