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A deep-learning model for evaluating and predicting the impact of lockdown policies on COVID-19 cases
arXiv - CS - Social and Information Networks Pub Date : 2020-09-11 , DOI: arxiv-2009.05481 Ahmed Ben Said, Abdelkarim Erradi, Hussein Aly, Abdelmonem Mohamed
arXiv - CS - Social and Information Networks Pub Date : 2020-09-11 , DOI: arxiv-2009.05481 Ahmed Ben Said, Abdelkarim Erradi, Hussein Aly, Abdelmonem Mohamed
To reduce the impact of COVID-19 pandemic most countries have implemented
several counter-measures to control the virus spread including school and
border closing, shutting down public transport and workplace and restrictions
on gathering. In this research work, we propose a deep-learning prediction
model for evaluating and predicting the impact of various lockdown policies on
daily COVID-19 cases. This is achieved by first clustering countries having
similar lockdown policies, then training a prediction model based on the daily
cases of the countries in each cluster along with the data describing their
lockdown policies. Once the model is trained, it can used to evaluate several
scenarios associated to lockdown policies and investigate their impact on the
predicted COVID cases. Our evaluation experiments, conducted on Qatar as a use
case, shows that the proposed approach achieved competitive prediction
accuracy. Additionally, our findings highlighted that lifting restrictions
particularly on schools and border opening would result in significant increase
in the number of cases during the study period.
更新日期：2020-09-14
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