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Respiratory syncytial virus (RSV) is one of the most dangerous respiratorydiseases for infants and young children. Due to the nonpharmaceuticalintervention (NPI) imposed in the COVID-19 outbreak, the seasonal transmissionpattern of RSV has been discontinued in 2020 and then shifted months ahead in2021 in the northern hemisphere. It is critical to understand how COVID-19impacts RSV and build predictive algorithms to forecast the timing andintensity of RSV reemergence in post-COVID-19 seasons. In this paper, wepropose a deep coupled tensor factorization machine, dubbed as DeCom, for postCOVID-19 RSV prediction. DeCom leverages tensor factorization and residualmodeling. It enables us to learn the disrupted RSV transmission reliably underCOVID-19 by taking both the regular seasonal RSV transmission pattern and theNPI into consideration. Experimental results on a real RSV dataset show thatDeCom is more accurate than the state-of-the-art RSV prediction algorithms andachieves up to 46% lower root mean square error and 49% lower mean absoluteerror for country-level prediction compared to the baselines.

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