Predicting the spread of COVID-19 in China with human mobility data
29th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, SIGSPATIAL 2021
; : 240-243, 2021.
Article
in English
| Scopus | ID: covidwho-1528576
ABSTRACT
The coronavirus disease 2019 (COVID-19) break-out in late December 2019 has spread rapidly worldwide. Existing studies have shown that there is a significant correlation between large-scale human movements and the spread of the epidemic. However, there is a lack of quantification of these correlations, and it is still challenging to predict the spread of the epidemic at early stage. In this paper, we address this issue by conducting a statistical analysis on the spatio-temporal relationship between human mobility and the epidemic spread. Specifically, we proposed an improved SEIR model to adapt to the COVID-19 epidemic, so that we can predict the spread of the epidemic at the early stage using human mobility data and the early confirmed cases. We evaluated our model in various provinces and cities in China, and the results are superior to various baselines, verifying the effectiveness of the method. © 2021 ACM.
Full text:
Available
Collection:
Databases of international organizations
Database:
Scopus
Type of study:
Prognostic study
Language:
English
Journal:
29th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, SIGSPATIAL 2021
Year:
2021
Document Type:
Article
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