Mathematical Models of Infection Prevention Programs in Hospital Settings

Hospitals play a vital role in providing for the healthcare needs of a community. Patients can develop hospital-acquired infections (HAIs) during their hospitalization due to exposure to foreign bacteria, viruses, and fungi. Infection prevention programs target and reduce HAIs, but implementing the infection prevention programs often comes with a cost. The goal of my research is to use mathematical models to quantify the impact of infection prevention programs on cases of HAIs and total healthcare costs. First, I use a Markov chain model to quantify how one infection prevention program reduces general HAIs in the hospital. Then, I calculate the impact of resistance by healthcare leaders to implement two infection prevention techniques on two HAIs in the hospital. I used ordinary differential equations to quantify the timing of initiation and termination of two infection prevention programs within a region divided into two components to understand how a community intervention and a localized intervention affect the peak number of infections in an epidemic. Finally, I used an agent-based model to quantify the impact of one specific infection prevention program on one HAI in one ward within the hospital. Overall, my research supports implementing the specific infection prevention programs examined to reduce the burden on healthcare systems and improve patient outcomes

Simulations on a Mathematical Model of Dengue Fever with a Focus on Mobility

Dengue fever is a major public health threat, especially for countries in tropical climates. In order to investigate the spread of dengue fever in neighboring communities, an ordinary differential equation model is formulated based on two previous models of vector-borne diseases, one that specifically describes dengue fever transmission and another that incorporates movement of populations when describing malaria transmission. The resulting SIR/SI model is used to simulate transmission of dengue fever in neighboring communities of differing population size with particular focus on cities in Sri Lanka. Models representing connections between two communities and among three communities are investigated. Initial infection details and relative population size may affect the dynamics of disease spread. An outbreak in a highly populated area may spread somewhat more rapidly through that area as well as neighboring communities than an outbreak beginning in a nearby rural area

The Development and Application of a Risk Index to Predict Individualized Chronic Disease Risk.

Integrating clinical and genetic information to improve clinicians’ ability to estimate an individual’s disease risk is an important biomedical research challenge. This dissertation develops a “risk index” procedure that combines clinical data and genome-wide genotypes to make predictions about individuals’ risk of disease. For a set of 100 simulated datasets containing 1,000 individuals, 8 clinical covariates, 500 Single nucleotide polymorphisms, and an outcome prevalence of 30%, the average area under the receiver operating characteristics (ROC) curve (AUC) for a risk index model built with clinical covariates and SNPs was significantly higher than a model built with clinical covariates alone (0.846 vs. 0.832, p=0.0002). A risk index model that includes the principal components that account for 90% of the variability in the SNPs also significantly increased the average AUC compared to a clinical covariates only model (0.839 vs. 0.826, p=0.008). For a set of 25 simulated datasets containing 10,000 individuals, 29 clinical covariates, and 38,835 SNPs, a significant difference in average AUC was observed between clinical and clinical+genotype models (0.939 vs. 0.926, p=0.001), using the 500 SNPs most highly associated with the outcome. A risk index model including the 500 largest principal components of the 38,835 SNPs did not significantly increase the mean AUC beyond the clinical model (0.931 vs. 0.931, p=0.98). The risk index methodology was then applied to individuals from the Framingham Heart Study using 27 clinical covariates and 48,071 SNPs. Clinical+genotype risk index models built to predict ten-year incident hypertension, ten-year incident diabetes, or prevalent hypertension had AUCs of 0.475, 0.682, and 0.692, respectively, using the 500 SNPs most highly associated with the outcome, and AUCs of 0.563, 0.782, and 0.712, respectively, using the 500 largest principal components of the SNPs. The results from these analyses suggest that the risk index methodology has utility for predicting an individual’s risk of developing a chronic disease, and that the use of principal components of a large set of SNPs in place of a smaller selected set of associated SNPs provides the best predictive performance.Ph.D.BioinformaticsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/64650/1/reagank_1.pd

Meeting Colorado's future water supply needs: opportunities and challenges associated with potential agricultural water conservation measures

September 2008.Presented by Colorado Agricultural Water Alliance.Includes bibliographical references

Children’s Poster Contest on Healthy Eating

Objective: To encourage children in 3rd, 4th, and 5th grades to learn about good nutrition and display their knowledge in an attractive poster. Method: A children’s poster contest was conducted through schools in the Washington, DC metro area in conjunction with the 2004 National Health Education Week’s campaign, “Healthy Eating – Every Bite Counts!”. Posters were judged on a 100 point scale, and six winners were chosen for each grade level. The children with the winning posters received cash prizes and were honored at an awards ceremony at the Society for Public Health Education’s (SOPHE) annual meeting. Results: Eligible entries were received from 76 students at 14 schools in the Arlington, VA and Washington, DC school districts. Almost all of the posters showed a good knowledge of nutrition by the students. Conclusion: National Health Education Week themes that are specific to children should encourage participation among schools, teachers, and parents. Partnerships offer possibilities for dissemination of public health education campaigns. A children’s poster contest about healthy eating in schools in the Washington, DC area was successful in gaining 76 entries from 14 schools, and children displayed a high level of knowledge of which foods were healthy for them and a high level of creativity and artistic talent

Selecting a single model or combining multiple models for microarray-based classifier development? – A comparative analysis based on large and diverse datasets generated from the MAQC-II project

<p>Abstract</p> <p>Background</p> <p>Genomic biomarkers play an increasing role in both preclinical and clinical application. Development of genomic biomarkers with microarrays is an area of intensive investigation. However, despite sustained and continuing effort, developing microarray-based predictive models (i.e., genomics biomarkers) capable of reliable prediction for an observed or measured outcome (i.e., endpoint) of unknown samples in preclinical and clinical practice remains a considerable challenge. No straightforward guidelines exist for selecting a single model that will perform best when presented with unknown samples. In the second phase of the MicroArray Quality Control (MAQC-II) project, 36 analysis teams produced a large number of models for 13 preclinical and clinical endpoints. Before external validation was performed, each team nominated one model per endpoint (referred to here as 'nominated models') from which MAQC-II experts selected 13 'candidate models' to represent the best model for each endpoint. Both the nominated and candidate models from MAQC-II provide benchmarks to assess other methodologies for developing microarray-based predictive models.</p> <p>Methods</p> <p>We developed a simple ensemble method by taking a number of the top performing models from cross-validation and developing an ensemble model for each of the MAQC-II endpoints. We compared the ensemble models with both nominated and candidate models from MAQC-II using blinded external validation.</p> <p>Results</p> <p>For 10 of the 13 MAQC-II endpoints originally analyzed by the MAQC-II data analysis team from the National Center for Toxicological Research (NCTR), the ensemble models achieved equal or better predictive performance than the NCTR nominated models. Additionally, the ensemble models had performance comparable to the MAQC-II candidate models. Most ensemble models also had better performance than the nominated models generated by five other MAQC-II data analysis teams that analyzed all 13 endpoints.</p> <p>Conclusions</p> <p>Our findings suggest that an ensemble method can often attain a higher average predictive performance in an external validation set than a corresponding “optimized” model method. Using an ensemble method to determine a final model is a potentially important supplement to the good modeling practices recommended by the MAQC-II project for developing microarray-based genomic biomarkers.</p

Flattening the Curve: The effects of intervention strategies during COVID-19

COVID-19 has plagued countries worldwide due to its infectious nature. Social distancing and the use of personal protective equipment (PPE) are two main strategies employed to prevent its spread. A SIR model with a time-dependent transmission rate is implemented to examine the effect of social distancing and PPE use in hospitals. These strategies’ effect on the size and timing of the peak number of infectious individuals are examined as well as the total number of individuals infected by the epidemic. The effect on the epidemic of when social distancing is relaxed is also examined. Overall, social distancing was shown to cause the largest impact in the number of infections. Studying this interaction between social distancing and PPE use is novel and timely. We show that decisions made at the state level on implementing social distancing and acquiring adequate PPE have dramatic impact on the health of its citizens