COVID-EENet: Predicting Fine-Grained Impact of COVID-19 on Local Economies
Keywords:AI For Social Impact (AISI Track Papers Only), Humans And AI (HAI)
AbstractAssessing the impact of the COVID-19 crisis on economies is fundamental to tailor the responses of the governments to recover from the crisis. In this paper, we present a novel approach to assessing the economic impact with a large-scale credit card transaction dataset at a fine granularity. For this purpose, we develop a fine-grained economic-epidemiological modeling framework COVID-EENet, which is featured with a two-level deep neural network. In support of the fine-grained EEM, COVID-EENet learns the impact of nearby mass infection cases on the changes of local economies in each district. Through the experiments using the nationwide dataset, given a set of active mass infection cases, COVID-EENet is shown to precisely predict the sales changes in two or four weeks for each district and business category. Therefore, policymakers can be informed of the predictive impact to put in the most effective mitigation measures. Overall, we believe that our work opens a new perspective of using financial data to recover from the economic crisis. For public use in this urgent problem, we release the source code at https://github.com/kaist-dmlab/COVID-EENet.
How to Cite
Kim, D., Min, H., Nam, Y., Song, H., Yoon, S., Kim, M., & Lee, J.-G. (2022). COVID-EENet: Predicting Fine-Grained Impact of COVID-19 on Local Economies. Proceedings of the AAAI Conference on Artificial Intelligence, 36(11), 11971-11981. https://doi.org/10.1609/aaai.v36i11.21455
AAAI Special Track on AI for Social Impact