758 research outputs found
Modeling Infection with Multi-agent Dynamics
Developing the ability to comprehensively study infections in small
populations enables us to improve epidemic models and better advise individuals
about potential risks to their health. We currently have a limited
understanding of how infections spread within a small population because it has
been difficult to closely track an infection within a complete community. The
paper presents data closely tracking the spread of an infection centered on a
student dormitory, collected by leveraging the residents' use of cellular
phones. The data are based on daily symptom surveys taken over a period of four
months and proximity tracking through cellular phones. We demonstrate that
using a Bayesian, discrete-time multi-agent model of infection to model
real-world symptom reports and proximity tracking records gives us important
insights about infec-tions in small populations
Sequential Data-Adaptive Bandwidth Selection by Cross-Validation for Nonparametric Prediction
We consider the problem of bandwidth selection by cross-validation from a
sequential point of view in a nonparametric regression model. Having in mind
that in applications one often aims at estimation, prediction and change
detection simultaneously, we investigate that approach for sequential kernel
smoothers in order to base these tasks on a single statistic. We provide
uniform weak laws of large numbers and weak consistency results for the
cross-validated bandwidth. Extensions to weakly dependent error terms are
discussed as well. The errors may be {\alpha}-mixing or L2-near epoch
dependent, which guarantees that the uniform convergence of the cross
validation sum and the consistency of the cross-validated bandwidth hold true
for a large class of time series. The method is illustrated by analyzing
photovoltaic data.Comment: 26 page
Understanding home water treatment systems (1995)
"New 7/93, Reprinted 6/95/5M.""Water Quality.""Focus area : drinking water.""Published by University Extension. University of Missouri-Columbia.""Reviewed and adapted for Missouri by Wanda Eubank, Jerry Carpenter, Bev Maltsberger, University of Missouri-Columbia, and Nix Anderson, Missouri Department of Health. From Buying Home Water Treatment Equipment by Adel L. Pfeil, Department of Consumer Sciences and Retailing, Purdue University.
Epidemiological and economic impact of pandemic influenza in Chicago: Priorities for vaccine interventions.
The study objective is to estimate the epidemiological and economic impact of vaccine interventions during influenza pandemics in Chicago, and assist in vaccine intervention priorities. Scenarios of delay in vaccine introduction with limited vaccine efficacy and limited supplies are not unlikely in future influenza pandemics, as in the 2009 H1N1 influenza pandemic. We simulated influenza pandemics in Chicago using agent-based transmission dynamic modeling. Population was distributed among high-risk and non-high risk among 0-19, 20-64 and 65+ years subpopulations. Different attack rate scenarios for catastrophic (30.15%), strong (21.96%), and moderate (11.73%) influenza pandemics were compared against vaccine intervention scenarios, at 40% coverage, 40% efficacy, and unit cost of $28.62. Sensitivity analysis for vaccine compliance, vaccine efficacy and vaccine start date was also conducted. Vaccine prioritization criteria include risk of death, total deaths, net benefits, and return on investment. The risk of death is the highest among the high-risk 65+ years subpopulation in the catastrophic influenza pandemic, and highest among the high-risk 0-19 years subpopulation in the strong and moderate influenza pandemics. The proportion of total deaths and net benefits are the highest among the high-risk 20-64 years subpopulation in the catastrophic, strong and moderate influenza pandemics. The return on investment is the highest in the high-risk 0-19 years subpopulation in the catastrophic, strong and moderate influenza pandemics. Based on risk of death and return on investment, high-risk groups of the three age group subpopulations can be prioritized for vaccination, and the vaccine interventions are cost saving for all age and risk groups. The attack rates among the children are higher than among the adults and seniors in the catastrophic, strong, and moderate influenza pandemic scenarios, due to their larger social contact network and homophilous interactions in school. Based on return on investment and higher attack rates among children, we recommend prioritizing children (0-19 years) and seniors (65+ years) after high-risk groups for influenza vaccination during times of limited vaccine supplies. Based on risk of death, we recommend prioritizing seniors (65+ years) after high-risk groups for influenza vaccination during times of limited vaccine supplies
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