DeCom: Deep Coupled-Factorization Machine for Post COVID-19 Respiratory Syncytial Virus Prediction with Nonpharmaceutical Interventions Awareness

• URL: http://arxiv.org/abs/2305.01770v1
• Date: Tue, 2 May 2023 20:23:09 GMT
• Title: DeCom: Deep Coupled-Factorization Machine for Post COVID-19 Respiratory Syncytial Virus Prediction with Nonpharmaceutical Interventions Awareness
• Abstract summary: Respiratory syncytial virus (RSV) is one of the most dangerous respiratory diseases for infants and young children. Due to the nonpharmaceutical intervention (NPI) imposed in the COVID-19 outbreak, the seasonal transmission pattern of RSV has been discontinued in 2020 and then shifted months ahead in 2021 in the northern hemisphere. It is critical to understand how COVID-19 impacts RSV and build predictive algorithms to forecast the timing and intensity of RSV reemergence in post-COVID-19 seasons.
• Score: 15.31523014144133
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Respiratory syncytial virus (RSV) is one of the most dangerous respiratory diseases for infants and young children. Due to the nonpharmaceutical intervention (NPI) imposed in the COVID-19 outbreak, the seasonal transmission pattern of RSV has been discontinued in 2020 and then shifted months ahead in 2021 in the northern hemisphere. It is critical to understand how COVID-19 impacts RSV and build predictive algorithms to forecast the timing and intensity of RSV reemergence in post-COVID-19 seasons. In this paper, we propose a deep coupled tensor factorization machine, dubbed as DeCom, for post COVID-19 RSV prediction. DeCom leverages tensor factorization and residual modeling. It enables us to learn the disrupted RSV transmission reliably under COVID-19 by taking both the regular seasonal RSV transmission pattern and the NPI into consideration. Experimental results on a real RSV dataset show that DeCom is more accurate than the state-of-the-art RSV prediction algorithms and achieves up to 46% lower root mean square error and 49% lower mean absolute error for country-level prediction compared to the baselines.

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