-
Nonparametric comparison of epidemic time trends: the case of COVID-19
The COVID-19 pandemic is one of the most pressing issues at present. A q...
read it
-
Estimation of COVID-19 spread curves integrating global data and borrowing information
Currently, novel coronavirus disease 2019 (COVID-19) is a big threat to ...
read it
-
The Framework for the Prediction of the Critical Turning Period for Outbreak of COVID-19 Spread in China based on the iSEIR Model
The goal of this study is to establish a general framework for predictin...
read it
-
Large-scale Feature Selection of Risk Genetic Factors for Alzheimer's Disease via Distributed Group Lasso Regression
Genome-wide association studies (GWAS) have achieved great success in th...
read it
-
Excess deaths, baselines, Z-scores, P-scores and peaks
The recent Covid-19 epidemic has lead to comparisons of the countries su...
read it
-
The test-negative design with additional population controls: a practical approach to rapidly obtain information on the causes of the SARS-CoV-2 epidemic
Testing of symptomatic persons for infection with SARS-CoV-2 is increasi...
read it
-
Neural network based country wise risk prediction of COVID-19
The recent worldwide outbreak of the novel corona-virus (COVID-19) opene...
read it
MFL_COVID19: Quantifying Country-based Factors affecting Case Fatality Rate in Early Phase of COVID-19 Epidemic via Regularised Multi-task Feature Learning
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.
READ FULL TEXT
Comments
There are no comments yet.