  Recent outbreak of COVID-19 has led a rapid global spread around the world.
Many countries have implemented timely intensive suppression to minimize the
infections, but resulted in high case fatality rate (CFR) due to critical
demand of health resources. Other country-based factors such as sociocultural
issues, ageing population etc., has also influenced practical effectiveness of
taking interventions to improve morality in early phase. To better understand
the relationship of these factors across different countries with COVID-19 CFR
is of primary importance to prepare for potentially second wave of COVID-19
infections. In the paper, we propose a novel regularized multi-task learning
based factor analysis approach for quantifying country-based factors affecting
CFR in early phase of COVID-19 epidemic. We formulate the prediction of CFR
progression as a ML regression problem with observed CFR and other
countries-based factors. In this formulation, all CFR related factors were
categorized into 6 sectors with 27 indicators. We proposed a hybrid feature
selection method combining filter, wrapper and tree-based models to calibrate
initial factors for a preliminary feature interaction. Then we adopted two
typical single task model (Ridge and Lasso regression) and one state-of-the-art
MTFL method (fused sparse group lasso) in our formulation. The fused sparse
group Lasso (FSGL) method allows the simultaneous selection of a common set of
country-based factors for multiple time points of COVID-19 epidemic and also
enables incorporating temporal smoothness of each factor over the whole early
phase period. Finally, we proposed one novel temporal voting feature selection
scheme to balance the weight instability of multiple factors in our MTFL model.
