  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.
