Clustering MIC data through Bayesian mixture models: an application to detect M. Tuberculosis resistance mutations

Antimicrobial resistance is becoming a major threat to public health throughout the world. Researchers are attempting to contrast it by developing both new antibiotics and patient-specific treatments. In the second case, whole-genome sequencing has had a huge impact in two ways: first, it is becoming cheaper and faster to perform whole-genome sequencing, and this makes it competitive with respect to standard phenotypic tests; second, it is possible to statistically associate the phenotypic patterns of resistance to specific mutations in the genome. Therefore, it is now possible to develop catalogues of genomic variants associated with resistance to specific antibiotics, in order to improve prediction of resistance and suggest treatments. It is essential to have robust methods for identifying mutations associated to resistance and continuously updating the available catalogues. This work proposes a general method to study minimal inhibitory concentration (MIC) distributions and to identify clusters of strains showing different levels of resistance to antimicrobials. Once the clusters are identified and strains allocated to each of them, it is possible to perform regression method to identify with high statistical power the mutations associated with resistance. The method is applied to a new 96-well microtiter plate used for testing M. Tuberculosis

MIXTURE MODELS FOR INTERVAL CENSORED OUTCOMES

Silent events such as the first detectable HIV infection, the onset of Type 2 diabetes and prostate cancer progression are often ascertained by diagnostic tests and/or self-reports that are scheduled periodically. In such applications, we only observe the time to the event of interest to lie between the times of last negative and the first positive tests, resulting in interval-censored observations. In addition, in some medical studies, a substantial proportion of participants may experience the events before the study, so-called prevalent cases, or participants may never experience the event, that is regarded as non-susceptible cases (or indolent cancer or long-term survivor). In this dissertation, I develop mixture models for the analysis of heterogeneous survival data subject to interval-censoring. The first chapter of this dissertation is motivated by a study of the effects of maternal and infant antiretroviral therapy on the sensitivity of DNA PCR diagnostic tests in detecting HIV infection in infants born to HIV-positive mothers. We apply a mixture model to evaluate the association of a set of predictors with an interval-censored time to first detectable DNA PCR test, while accounting for the subset of infants who test positive at birth. The mixture model is applied to data from the Pediatric AIDS Collaborative Transmission Study and the Women and Infants Transmission Study to evaluate the effects of maternal/infant antiretroviral therapy in HIV subtype B infected mother-infant pairs. In Chapter 2, we propose a parametric mixture model for interval censored time to event outcomes, while relaxing the commonly used proportional hazards assumption. The proposed model is applied to data collected in the National Health and Nutrition Examination Survey to evaluate risk factors of Type 2 diabetes. Chapter 3 is motivated by a Canary Prostate Active Surveillance Study (PASS) where the time to cancer progression (i.e., biopsy upgrade) is of primary interest. We propose a semiparametric mixture model to handle misclassification of progressed cancer at baseline and non-susceptible cases (or, indolent cancer). In addition, we account for imperfect diagnostic tests at each visit and risk factors that change over time in the proposed model. Extensive simulation studies are conducted to assess the performance of the proposed approaches with/without mixture components. The proposed approach is applied to the Canary Prostate Active Surveillance Study to evaluate the effects of factors on the risk of cancer progression and estimate the indolent fraction under a range of sensitivity rates of biopsy

Book of Abstracts XVIII Congreso de Biometría CEBMADRID

Abstracts of the XVIII Congreso de Biometría CEBMADRID held from 25 to 27 May in MadridInteractive modelling and prediction of patient evolution via multistate models / Leire Garmendia Bergés, Jordi Cortés Martínez and Guadalupe Gómez Melis : This research was funded by the Ministerio de Ciencia e Innovación (Spain) [PID2019104830RBI00]; and the Generalitat de Catalunya (Spain) [2017SGR622 and 2020PANDE00148].Operating characteristics of a model-based approach to incorporate non-concurrent controls in platform trials / Pavla Krotka, Martin Posch, Marta Bofill Roig : EU-PEARL (EU Patient-cEntric clinicAl tRial pLatforms) project has received funding from the Innovative Medicines Initiative (IMI) 2 Joint Undertaking (JU) under grant agreement No 853966. This Joint Undertaking receives support from the European Union’s Horizon 2020 research and innovation programme and EFPIA and Children’s Tumor Foundation, Global Alliance for TB Drug Development non-profit organisation, Spring works Therapeutics Inc.Modeling COPD hospitalizations using variable domain functional regression / Pavel Hernández Amaro, María Durbán Reguera, María del Carmen Aguilera Morillo, Cristobal Esteban Gonzalez, Inma Arostegui : This work is supported by the grant ID2019-104901RB-I00 from the Spanish Ministry of Science, Innovation and Universities MCIN/AEI/10.13039/501100011033.Spatio-temporal quantile autoregression for detecting changes in daily temperature in northeastern Spain / Jorge Castillo-Mateo, Alan E. Gelfand, Jesús Asín, Ana C. Cebrián / Spatio-temporal quantile autoregression for detecting changes in daily temperature in northeastern Spain : This work was partially supported by the Ministerio de Ciencia e Innovación under Grant PID2020-116873GB-I00; Gobierno de Aragón under Research Group E46_20R: Modelos Estocásticos; and JC-M was supported by Gobierno de Aragón under Doctoral Scholarship ORDEN CUS/581/2020.Estimation of the area under the ROC curve with complex survey data / Amaia Iparragirre, Irantzu Barrio, Inmaculada Arostegui : This work was financially supported in part by IT1294-19, PID2020-115882RB-I00, KK-2020/00049. The work of AI was supported by PIF18/213.INLAMSM: Adjusting multivariate lattice models with R and INLA / Francisco Palmí Perales, Virgilio Gómez Rubio and Miguel Ángel Martínez Beneito : This work has been supported by grants PPIC-2014-001-P and SBPLY/17/180501/000491, funded by Consejería de Educación, Cultura y Deportes (Junta de Comunidades de Castilla-La Mancha, Spain) and FEDER, grant MTM2016-77501-P, funded by Ministerio de Economía y Competitividad (Spain), grant PID2019-106341GB-I00 from Ministerio de Ciencia e Innovación (Spain) and a grant to support research groups by the University of Castilla-La Mancha (Spain). F. Palmí-Perales has been supported by a Ph.D. scholarship awarded by the University of Castilla-La Mancha (Spain)

Mathematical modeling of clostridium difficile transmission in healthcare settings

Clostridium difficile is a frequent source of healthcare-associated infection, especially among patients on antibiotics or proton pump inhibitors (PPIs). The rate of C. difficile infection (CDI) has been steadily rising since 2000 and now represents a major burden on the healthcare system in terms of both morbidity and mortality. However, despite its public health importance, there are few mathematical models of C. difficile which might be used to evaluate our current evidence base or new control measures. Three different data sources were analyzed to provide parameters for a mathematical model: a cohort of incident CDI cases in the Duke Infection Control Outreach Network (DICON), a hospital-level surveillance time series, also from DICON, and inpatient records from UNC Healthcare, all from 7/1/2009 to 12/31/2010. Using estimates from these data, as well as from the literature, a pair of compartmental transmission models, one deterministic and the other stochastic, were created to evaluate the potential effect of the use of fecal transplantation as a treatment to prevent CDI. The analysis of the cohort of incident cases suggested that ICU patients experience a greater burden of mortality while infected with C. difficile and have longer lengths of stay and times until death, suggesting this population as one of special interest. Two interventions were simulated using the stochastic model: the use of fecal transplantation to treat CDI and prevent recurrent cases and the use of fecal transplantation after treatment with antibiotics or PPIs to prevent the development of CDI. Simulation results showed that treating patients with CDI was effective in preventing recurrence but not in reducing the overall number of incident cases of CDI. Transplantation after treatment with antibiotics or PPIs had no effect on preventing recurrence and a statistically significant reduction in incident cases that did not reach clinical significance. These results suggest that routine fecal transplantation for patients with CDI may be an effective treatment to prevent recurrence. Mathematical models such as the one described in this dissertation are powerful tools to evaluate potential interventions, suggest new directions for study, and understand the dynamics of infection on a population level.Doctor of Philosoph

Population Dynamics of Enteric Salmonella in Response to Antibiotic Use in Feedlot Cattle

The various uses of antibiotics in feedlot cattle have been a concern as a potential source of antibiotic resistant Salmonella infections in humans. A 26-day randomized controlled longitudinal field trial was undertaken to assess the effects of injectable ceftiofur crystalline-free acid (CCFA) versus in-feed chlortetracycline (CTC) on the temporal dynamics of Salmonella enterica subsp. enterica in feedlot cattle. Two replicates of 8 pens (total of 176 steers) received one of 4 different treatment regimens. All, or one, out of 11 steers were treated with CCFA on day 0 in 8 pens, with half of the pens later receiving three 5-day regimens of CTC. We isolated Salmonella from fecal samples, and antimicrobial susceptibility was assessed. Salmonella in the feces were quantified with probe real-time qPCR targeting invA gene and by direct spiral plating on brilliant green agar. Whole-genome sequencing was performed for all Salmonella isolates to analyze serotype, resistance genotype, MLST, and to explore the phylogenetic relations of the isolates. The mean Salmonella prevalence was 75.0% on day 0, and most isolates were pansusceptible to 14 antibiotics. Both CCFA and CTC reduced the overall prevalence of Salmonella; however, these treatments increased the proportion of multi-drug resistant (MDR) Salmonella. Ceftriaxone and tetracycline resistant Salmonella were detectable in day 0 samples, suggesting that resistant Salmonella existed in the population before antibiotics use. The quantity of resistant Salmonella remained at approximately 10^3 CFU / gram of feces throughout the study. Significantly (P < 0.05) more animals were detected with resistant Salmonella following antibiotic treatments. Among the six serotypes detected, all S. Reading isolates were MDR and carrying an IncA/C2 plasmid, suggesting a strong association between serotype and resistance type. The S. Reading isolates consisted of 2 phylogenetic clades with differential selection by CCFA versus CTC (alone). Our study demonstrated that the selection pressures of a 3rd generation cephalosporin and of CTC during the cattle feeding period selects for antibiotic resistant Salmonella and increases the proportion of cattle carrying resistant Salmonella, even after the treatment period ends. Further investigations are needed to assess whether an extended feeding period of 150 days provides a sufficient ‘wash-out’ period for the gut microbiota to return to normal status

The public health risk posed by Listeria monocytogenes in frozen fruit and vegetables including herbs, blanched during processing

A multi-country outbreak ofListeria monocytogenesST6 linked to blanched frozen vegetables (bfV)took place in the EU (2015–2018). Evidence of food-borne outbreaks shows thatL. monocytogenesisthe most relevant pathogen associated with bfV. The probability of illness per serving of uncooked bfV,for the elderly (65–74 years old) population, is up to 3,600 times greater than cooked bfV and verylikely lower than any of the evaluated ready-to-eat food categories. The main factors affectingcontamination and growth ofL. monocytogenesin bfV during processing are the hygiene of the rawmaterials and process water; the hygienic conditions of the food processing environment (FPE); andthe time/Temperature (t/T) combinations used for storage and processing (e.g. blanching, cooling).Relevant factors after processing are the intrinsic characteristics of the bfV, the t/T combinations usedfor thawing and storage and subsequent cooking conditions, unless eaten uncooked. Analysis of thepossible control options suggests that application of a complete HACCP plan is either not possible orwould not further enhance food safety. Instead, specific prerequisite programmes (PRP) andoperational PRP activities should be applied such as cleaning and disinfection of the FPE, water control,t/T control and product information and consumer awareness. The occurrence of low levels ofL. monocytogenesat the end of the production process (e.g.<10 CFU/g) would be compatible with thelimit of 100 CFU/g at the moment of consumption if any labelling recommendations are strictly followed(i.e. 24 h at 5°C). Under reasonably foreseeable conditions of use (i.e. 48 h at 12°C),L. monocytogeneslevels need to be considerably lower (not detected in 25 g). Routine monitoring programmes forL. monocytogenesshould be designed following a risk-based approach and regularly revised based ontrend analysis, being FPE monitoring a key activity in the frozen vegetable industry

Experimental evolution of herbicide resistance in chlamydomonas reinhardtii

Our understanding of the evolutionary dynamics of selection for herbicide resistance is limited by the time and space required to conduct meaningful selection experiments in higher plants. This constrains the study of the dynamics of resistance evolution predominantly to mathematical models. The primary goal of this thesis was to overcome these limitations, and to study the evolutionary phenomena underpinning several management strategies. To do so, a series of experimental evolution studies were conducted using Chlamydomonas reinhardtii, a single-­‐cell green chlorophyte susceptible to a range of commercial herbicides. In particular, this thesis explored the impact of herbicide sequences, rotations and mixtures, as well the impact of herbicide dose, on evolution of resistance. Applying herbicides in sequence allowed the study of the impact of environmental perturbation on the dynamics of resistance and the associated fitness costs, finding more rapid selection for resistance to a second and third mode of action in some populations. Cycling between herbicides creates conditions of temporal environmental heterogeneity, the outcomes of which are not easily predictable as resistance was slowed down in some cycling regimes, while in others it accelerated the evolution of resistance or gave rise to cross-­‐resistance. Herbicide mixtures are a management strategy relying on increases in environmental complexity to provide better control of resistance. The results presented show that mixtures were effective at slowing the evolution of resistance when all mixture components were used at fully effective doses, while low doses of mixtures accelerated resistance evolution and led to more cross-­‐resistance. Finally, modifications of the applied herbicide dose allowed the study of local adaptation along an environmental gradient, where the differences in outcomes based on the specific herbicides used were again evident. Overall, the work presented here uses applied scenarios to study the underlying evolutionary phenomena, in order to feed back into the applied thinking