ncov_with_mobility's Introduction

ncov_with_mobility

The code and data for "Predicting the spread of COVID-19 in China with human mobility data"

Bibtex:

@inproceedings{10.1145/3474717.3483952,
author = {Wu, Shangbin and Fan, Xiaoliang and Chen, Longbiao and Cheng, Ming and Wang, Cheng},
title = {Predicting the Spread of COVID-19 in China with Human Mobility Data},
year = {2021},
isbn = {9781450386647},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3474717.3483952},
doi = {10.1145/3474717.3483952},
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.},
booktitle = {Proceedings of the 29th International Conference on Advances in Geographic Information Systems},
pages = {240–243},
numpages = {4},
keywords = {human mobility, SEIR, epidemic, COVID-19},
location = {Beijing, China},
series = {SIGSPATIAL '21}
}

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