278 research outputs found
Supplemental materials for paper: Using Predictive Analytics for Public Policy: The Case for Lost Work due to the COVID-19
No full textUsing Predictive Analytics for Public Policy: The Case for Lost Work due to the COVID-19
No full textIn this brief research article, I demonstrate how predictive analytics or machine learning can be used to predict outcomes that are of interest in public policy. I developed a predictive model that determined who were not able to work during the past four weeks because the COVID-19 pandemic led their employer to close or lose business. I used the Current Population Survey (CPS) collected from May to November 2020 (N=352,278). Predictive models considered were logistic regression and ensemble-based methods (bagging of regression trees, random forests, and boosted regression trees). Predictors included (1) individual-, (2) family-, (3) and community or societal- level factors. To validate the models, I used the random training test splits with equal allocation of samples for the training and testing data. The random forest with the full set of predictors and number of splits set to the square root of the number of predictors yielded the lowest testing error rate. Predictive analytics that seek to forecast the inability to work due to the pandemic can be used for automated means-testing to determine who gets aid like unemployment benefits or food stamps
Supplemental materials for paper: Using Predictive Analytics for Public Policy: The Case for Lost Work due to the COVID-19
No full textUnhealthier States have Lower COVID-19 Testing Rates
No full textThis data slice dissects the various rates of state testing for COVID-19 across the country, and explains implications of the low test rates in the unhealthier states
Supplemental materials for paper: Using Predictive Analytics for Public Policy: The Case for Lost Work due to the COVID-19
No full textMore Kindergarteners are Exempted from Required School Vaccinations than in the Past
No full textThe percentage of kindergarteners being granted exemption from vaccination is growing across the U.S. as parents increasingly request exemptions for philosophical reasons. During the 2017-18 school year, 2.2% of kindergartners were exempted from vaccination. This is up from 1.6% during the 2011-12 school year
DNA fusion gene vaccination mobilizes effective anti-leukemic cytotoxic T lymphocytes from a tolerized repertoire
No full textThe majority of known human tumor-associated antigens derive from non-mutated self proteins. T cell tolerance, essential to prevent autoimmunity, must therefore be cautiously circumvented to generate cytotoxic T cell responses against these targets. Our strategy uses DNA fusion vaccines to activate high levels of peptide-specific CTL. Key foreign sequences from tetanus toxin activate tolerance-breaking CD4+ T cell help. Candidate MHC class Ibinding tumor peptide sequences are fused to the C terminus for optimal processing and presentation. To model performance against a leukemia-associated antigen in a tolerized setting, we constructed a fusion vaccine encoding an immunodominant CTL epitopederived from Friend murine leukemia virus gag protein (FMuLVgag) and vaccinated tolerant FMuLVgag-transgenic (gag-Tg) mice. Vaccination with the construct induced epitopespecificIFN-c-producing CD8+ T cells in normal and gag-Tg mice. The frequency and avidity of activated cells were reduced in gag-Tg mice, and no autoimmune injury resulted. However, these CD8+ T cells did exhibit gag-specific cytotoxicity in vitro and in vivo. Also, epitope-specific CTL killed FBL-3 leukemia cells expressing endogenous FMuLVgag antigen and protected against leukemia challenge in vivo. These results demonstrate a simple strategy to engage anti-microbial T cell help to activate epitope-specific polyclonal CD8+ T cell responses from a residual tolerized repertoire
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