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

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

Meox2 Haploinsufficiency Accelerates Axonal Degeneration in DBA/2J Glaucoma.

Purpose: Glaucoma is a complex disease with major risk factors including advancing age and increased intraocular pressure (IOP). Dissecting these earliest events will likely identify new avenues for therapeutics. Previously, we performed transcriptional profiling in DBA/2J (D2) mice, a widely used mouse model relevant to glaucoma. Here, we use these data to identify and test regulators of early gene expression changes in DBA/2J glaucoma. Methods: Upstream regulator analysis (URA) in Ingenuity Pathway Analysis was performed to identify potential master regulators of differentially expressed genes. The function of one putative regulator, mesenchyme homeobox 2 (Meox2), was tested using a combination of genetic, biochemical, and immunofluorescence approaches. Results: URA identified Meox2 as a potential regulator of early gene expression changes in the optic nerve head (ONH) of DBA/2J mice. Meox2 haploinsufficiency did not affect the characteristic diseases of the iris or IOP elevation seen in DBA/2J mice but did cause a significant increase in the numbers of eyes with axon damage compared to controls. While young mice appeared normal, aged Meox2 haploinsufficient DBA/2J mice showed a 44% reduction in MEOX2 protein levels. This correlated with modulation of age- and disease-specific vascular and myeloid alterations. Conclusions: Our data support a model whereby Meox2 controls IOP-dependent vascular remodeling and neuroinflammation to promote axon survival. Promoting these earliest responses prior to IOP elevation may be a viable neuroprotective strategy to delay or prevent human glaucoma

Complexity in the genetic architecture of leukoaraiosis in hypertensive sibships from the GENOA Study

<p>Abstract</p> <p>Background</p> <p>Subcortical white matter hyperintensity on magnetic resonance imaging (MRI) of the brain, referred to as leukoaraiosis, is associated with increased risk of stroke and dementia. Hypertension may contribute to leukoaraiosis by accelerating the process of arteriosclerosis involving penetrating small arteries and arterioles in the brain. Leukoaraiosis volume is highly heritable but shows significant inter-individual variability that is not predicted well by any clinical covariates (except for age) or by single SNPs.</p> <p>Methods</p> <p>As part of the Genetics of Microangiopathic Brain Injury (GMBI) Study, 777 individuals (74% hypertensive) underwent brain MRI and were genotyped for 1649 SNPs from genes known or hypothesized to be involved in arteriosclerosis and related pathways. We examined SNP main effects, epistatic (gene-gene) interactions, and context-dependent (gene-environment) interactions between these SNPs and covariates (including conventional and novel risk factors for arteriosclerosis) for association with leukoaraiosis volume. Three methods were used to reduce the chance of false positive associations: 1) false discovery rate (FDR) adjustment for multiple testing, 2) an internal replication design, and 3) a ten-iteration four-fold cross-validation scheme.</p> <p>Results</p> <p>Four SNP main effects (in <it>F3</it>, <it>KITLG</it>, <it>CAPN10</it>, and <it>MMP2</it>), 12 SNP-covariate interactions (including interactions between <it>KITLG </it>and homocysteine, and between <it>TGFB3 </it>and both physical activity and C-reactive protein), and 173 SNP-SNP interactions were significant, replicated, and cross-validated. While a model containing the top single SNPs with main effects predicted only 3.72% of variation in leukoaraiosis in independent test samples, a multiple variable model that included the four most highly predictive SNP-SNP and SNP-covariate interactions predicted 11.83%.</p> <p>Conclusion</p> <p>These results indicate that the genetic architecture of leukoaraiosis is complex, yet predictive, when the contributions of SNP main effects are considered in combination with effects of SNP interactions with other genes and covariates.</p

Multiple interactions between the alpha2C- and beta1-adrenergic receptors influence heart failure survival

<p>Abstract</p> <p>Background</p> <p>Persistent stimulation of cardiac β₁-adrenergic receptors by endogenous norepinephrine promotes heart failure progression. Polymorphisms of this gene are known to alter receptor function or expression, as are polymorphisms of the α_2C-adrenergic receptor, which regulates norepinephrine release from cardiac presynaptic nerves. The purpose of this study was to investigate possible synergistic effects of polymorphisms of these two intronless genes (<it>ADRB1 </it>and <it>ADRA2C</it>, respectively) on the risk of death/transplant in heart failure patients.</p> <p>Methods</p> <p>Sixteen sequence variations in <it>ADRA2C </it>and 17 sequence variations in <it>ADRB1 </it>were genotyped in a longitudinal study of 655 white heart failure patients. Eleven sequence variations in each gene were polymorphic in the heart failure cohort. Cox proportional hazards modeling was used to identify polymorphisms and potential intra- or intergenic interactions that influenced risk of death or cardiac transplant. A leave-one-out cross-validation method was utilized for internal validation.</p> <p>Results</p> <p>Three polymorphisms in <it>ADRA2C </it>and five polymorphisms in <it>ADRB1 </it>were involved in eight cross-validated epistatic interactions identifying several two-locus genotype classes with significant relative risks ranging from 3.02 to 9.23. There was no evidence of intragenic epistasis. Combining high risk genotype classes across epistatic pairs to take into account linkage disequilibrium, the relative risk of death or transplant was 3.35 (1.82, 6.18) relative to all other genotype classes.</p> <p>Conclusion</p> <p>Multiple polymorphisms act synergistically between the <it>ADRA2C </it>and <it>ADRB1 </it>genes to increase risk of death or cardiac transplant in heart failure patients.</p

Translating Clinical Findings into Knowledge in Drug Safety Evaluation - Drug Induced Liver Injury Prediction System (DILIps)

Drug-induced liver injury (DILI) is a significant concern in drug development due to the poor concordance between preclinical and clinical findings of liver toxicity. We hypothesized that the DILI types (hepatotoxic side effects) seen in the clinic can be translated into the development of predictive in silico models for use in the drug discovery phase. We identified 13 hepatotoxic side effects with high accuracy for classifying marketed drugs for their DILI potential. We then developed in silico predictive models for each of these 13 side effects, which were further combined to construct a DILI prediction system (DILIps). The DILIps yielded 60–70% prediction accuracy for three independent validation sets. To enhance the confidence for identification of drugs that cause severe DILI in humans, the “Rule of Three” was developed in DILIps by using a consensus strategy based on 13 models. This gave high positive predictive value (91%) when applied to an external dataset containing 206 drugs from three independent literature datasets. Using the DILIps, we screened all the drugs in DrugBank and investigated their DILI potential in terms of protein targets and therapeutic categories through network modeling. We demonstrated that two therapeutic categories, anti-infectives for systemic use and musculoskeletal system drugs, were enriched for DILI, which is consistent with current knowledge. We also identified protein targets and pathways that are related to drugs that cause DILI by using pathway analysis and co-occurrence text mining. While marketed drugs were the focus of this study, the DILIps has a potential as an evaluation tool to screen and prioritize new drug candidates or chemicals, such as environmental chemicals, to avoid those that might cause liver toxicity. We expect that the methodology can be also applied to other drug safety endpoints, such as renal or cardiovascular toxicity