Data Informed Health Simulation Modeling

Combining reliable data with dynamic models can enhance the understanding of health-related phenomena. Smartphone sensor data characterizing discrete states is often suitable for analysis with machine learning classifiers. For dynamic models with continuous states, high-velocity data also serves an important role in model parameterization and calibration. Particle filtering (PF), combined with dynamic models, can support accurate recurrent estimation of continuous system state. This thesis explored these and related ideas with several case studies. The first employed multivariate Hidden Markov models (HMMs) to identify smoking intervals, using time-series of smartphone-based sensor data. Findings demonstrated that multivariate HMMs can achieve notable accuracy in classifying smoking state, with performance being strongly elevated by appropriate data conditioning. Reflecting the advantages of dynamic simulation models, this thesis has contributed two applications of articulated dynamic models: An agent-based model (ABM) of smoking and E-Cigarette use and a hybrid multi-scale model of diabetes in pregnancy (DIP). The ABM of smoking and E-Cigarette use, informed by cross-sectional data, supports investigations of smoking behavior change in light of the influence of social networks and E-Cigarette use. The DIP model was evidenced by both longitudinal and cross-sectional data, and is notable for its use of interwoven ABM, system dynamics (SD), and discrete event simulation elements to explore the interaction of risk factors, coupled dynamics of glycemia regulation, and intervention tradeoffs to address the growing incidence of DIP in the Australia Capital Territory. The final study applied PF with an SD model of mosquito development to estimate the underlying Culex mosquito population using various direct observations, including time series of weather-related factors and mosquito trap counts. The results demonstrate the effectiveness of PF in regrounding the states and evolving model parameters based on incoming observations. Using PF in the context of automated model calibration allows optimization of the values of parameters to markedly reduce model discrepancy. Collectively, the thesis demonstrates how characteristics and availability of data can influence model structure and scope, how dynamic model structure directly affects the ways that data can be used, and how advanced analysis methods for calibration and filtering can enhance model accuracy and versatility

A Review Of Hybrid Simulation In Healthcare

Hybrid Simulation (HS) has been applied to healthcare systems, but there is still limited literature and an opportunity to develop research. This review explores applications of HS in healthcare, to outline research gaps and foster new research in HS to solve complex real healthcare problems. The twelve application papers found through a systematic literature search covered nearly all hybrid combinations. Discrete Event (DES) and System Dynamics (SD) were found to be the most popular combination, and AnyLogic, the most used HS tool. We found that none of the papers we reviewed used the SD-ABS approach, which raises questions about the need and challenges associated with certain combinations. HS in healthcare applications, for the most part, are published in conference proceedings. We discuss opportunities for research and, in particular, the potential for HS application in problems related to communicable disease and healthcare services planning

Transmission Modeling with Smartphone-based Sensing

Infectious disease spread is difficult to accurately measure and model. Even for well-studied pathogens, uncertainties remain regarding the dynamics of mixing behavior and how to balance simulation-generated estimates with empirical data. Smartphone-based sensing data promises the availability of inferred proximate contacts, with which we can improve transmission models. This dissertation addresses the problem of informing transmission models with proximity contact data by breaking it down into three sub-questions. Firstly, can proximity contact data inform transmission models? To this question, an extended-Kalman-filter enhanced System Dynamics Susceptible-Infectious-Removed (EKF-SD-SIR) model demonstrated the filtering approach, as a framework, for informing Systems Dynamics models with proximity contact data. This combination results in recurrently-regrounded system status as empirical data arrive throughout disease transmission simulations---simultaneously considering empirical data accuracy, growing simulation error between measurements, and supporting estimation of changing model parameters. However, as revealed by this investigation, this filtering approach is limited by the quality and reliability of sensing-informed proximate contacts, which leads to the dissertation's second and third questions---investigating the impact of temporal and spatial resolution on sensing inferred proximity contact data for transmission models. GPS co-location and Bluetooth beaconing are two of those common measurement modalities to sense proximity contacts with different underlying technologies and tradeoffs. However, both measurement modalities have shortcomings and are prone to false positives or negatives when used to detect proximate contacts because unmeasured environmental influences bias the data. Will differences in sensing modalities impact transmission models informed by proximity contact data? The second part of this dissertation compares GPS- and Bluetooth-inferred proximate contacts by accessing their impact on simulated attack rates in corresponding proximate-contact-informed agent-based Susceptible-Exposed-Infectious-Recovered (ABM-SEIR) models of four distinct contagious diseases. Results show that the inferred proximate contacts resulting from these two measurement modalities are different and give rise to significantly different attack rates across multiple data collections and pathogens. While the advent of commodity mobile devices has eased the collection of proximity contact data, battery capacity and associated costs impose tradeoffs between the frequency and scanning duration used for proximate-contact detection. The choice of a balanced sensing regime involves specifying temporal resolutions and interpreting sensing data---depending on circumstances such as the characteristics of a particular pathogen, accompanying disease, and underlying population. How will the temporal resolution of sensing impact transmission models informed by proximity contact data? Furthermore, how will circumstances alter the impact of temporal resolution? The third part of this dissertation investigates the impacts of sensing regimes on findings from two sampling methods of sensing at widely varying inter-observation intervals by synthetically downsampling proximity contact data from five contact network studies---with each of these five studies measuring participant-participant contact every 5 minutes for durations of four or more weeks. The impact of downsampling is evaluated through ABM-SEIR simulations from both population- and individual-level for 12 distinct contagious diseases and associated variants of concern. Studies in this part find that for epidemiological models employing proximity contact data, both the observation paradigms and the inter-observation interval configured to collect proximity contact data exert impacts on the simulation results. Moreover, the impact is subject to the population characteristics and pathogen infectiousness reflective (such as the basic reproduction number, $R_0$). By comparing the performance of two sampling methods of sensing, we found that in most cases, periodically observing for a certain duration can collect proximity contact data that allows agent-based models to produce a reasonable estimation of the attack rate. However, higher-resolution data are preferred for modeling individual infection risk. Findings from this part of the dissertation represent a step towards providing the empirical basis for guidelines to inform data collection that is at once efficient and effective. This dissertation addresses the problem of informing transmission models with proximity contact data in three steps. Firstly, the demonstration of an EKF-SD-SIR model suggests that the filtering approach could improve System Dynamics transmission models by leveraging proximity contact data. In addition, experiments with the EKF-SD-SIR model also revealed that the filtering approach is constrained by the limited quality and reliability of sensing-data-inferred proximate contacts. The following two parts of this dissertation investigate spatial-temporal factors that could impact the quality and reliability of sensor-collected proximity contact data. In the second step, the impact of spatial resolution is illustrated by differences between two typical sensing modalities---Bluetooth beaconing versus GPS co-location. Experiments show that, in general, proximity contact data collected with Bluetooth beaconing lead to transmission models with results different from those driven by proximity contact data collected with GPS co-location. Awareness of the differences between sensing modalities can aid researchers in incorporating proximity contact data into transmission models. Finally, in the third step, the impact of temporal resolution is elucidated by investigating the differences between results of transmission models led by proximity contact data collected with varying observation frequencies. These differences led by varying observation frequencies are evaluated under circumstances with alternative assumptions regarding sampling method, disease/pathogen type, and the underlying population. Experiments show that the impact of sensing regimes is influenced by the type of diseases/pathogens and underlying population, while sampling once in a while can be a decent choice across all situations. This dissertation demonstrated the value of a filtering approach to enhance transmission models with sensor-collected proximity contact data, as well as explored spatial-temporal factors that will impact the accuracy and reliability of sensor-collected proximity contact data. Furthermore, this dissertation suggested guidance for future sensor-based proximity contact data collection and highlighted needs and opportunities for further research on sensing-inferred proximity contact data for transmission models

Psr1p interacts with SUN/sad1p and EB1/mal3p to establish the bipolar spindle

Regular Abstracts - Sunday Poster Presentations: no. 382During mitosis, interpolar microtubules from two spindle pole bodies (SPBs) interdigitate to create an antiparallel microtubule array for accommodating numerous regulatory proteins. Among these proteins, the kinesin-5 cut7p/Eg5 is the key player responsible for sliding apart antiparallel microtubules and thus helps in establishing the bipolar spindle. At the onset of mitosis, two SPBs are adjacent to one another with most microtubules running nearly parallel toward the nuclear envelope, creating an unfavorable microtubule configuration for the kinesin-5 kinesins. Therefore, how the cell organizes the antiparallel microtubule array in the first place at mitotic onset remains enigmatic. Here, we show that a novel protein psrp1p localizes to the SPB and plays a key role in organizing the antiparallel microtubule array. The absence of psr1+ leads to a transient monopolar spindle and massive chromosome loss. Further functional characterization demonstrates that psr1p is recruited to the SPB through interaction with the conserved SUN protein sad1p and that psr1p physically interacts with the conserved microtubule plus tip protein mal3p/EB1. These results suggest a model that psr1p serves as a linking protein between sad1p/SUN and mal3p/EB1 to allow microtubule plus ends to be coupled to the SPBs for organization of an antiparallel microtubule array. Thus, we conclude that psr1p is involved in organizing the antiparallel microtubule array in the first place at mitosis onset by interaction with SUN/sad1p and EB1/mal3p, thereby establishing the bipolar spindle.postprin

Removal of antagonistic spindle forces can rescue metaphase spindle length and reduce chromosome segregation defects

Regular Abstracts - Tuesday Poster Presentations: no. 1925Metaphase describes a phase of mitosis where chromosomes are attached and oriented on the bipolar spindle for subsequent segregation at anaphase. In diverse cell types, the metaphase spindle is maintained at a relatively constant length. Metaphase spindle length is proposed to be regulated by a balance of pushing and pulling forces generated by distinct sets of spindle microtubules and their interactions with motors and microtubule-associated proteins (MAPs). Spindle length appears important for chromosome segregation fidelity, as cells with shorter or longer than normal metaphase spindles, generated through deletion or inhibition of individual mitotic motors or MAPs, showed chromosome segregation defects. To test the force balance model of spindle length control and its effect on chromosome segregation, we applied fast microfluidic temperature-control with live-cell imaging to monitor the effect of switching off different combinations of antagonistic forces in the fission yeast metaphase spindle. We show that spindle midzone proteins kinesin-5 cut7p and microtubule bundler ase1p contribute to outward pushing forces, and spindle kinetochore proteins kinesin-8 klp5/6p and dam1p contribute to inward pulling forces. Removing these proteins individually led to aberrant metaphase spindle length and chromosome segregation defects. Removing these proteins in antagonistic combination rescued the defective spindle length and, in some combinations, also partially rescued chromosome segregation defects. Our results stress the importance of proper chromosome-to-microtubule attachment over spindle length regulation for proper chromosome segregation.postprin

Preservation management modelling in archival and library collections

As cultural heritage institutions, libraries and archives are responsible for managing collections in order to ensure access for present and future generations, and sustainable preservation. In pursuing these two goals, institutions face the challenge of determining to what extent preservation actions are beneficial in the context of their own collections. This project contributes to the complex decision-making processes of collections management by developing a mathematical model that shows, quantitatively, the effects of different preservation decisions during a collection’s lifetime. The novelty of this research lies in its approach to preservation management not as single, independent measures, but as a process that is part of a complex system: preservation management is not seen in isolation, but in relation to the other archival and library functions in the broader context of collections management. To meet this aim, complex systems modelling and simulation paradigms, such as system dynamics (SD) and agentbased modelling (ABM), are applied. Applying simulation to model preservation management decisions has the potential to develop into an integrated approach for evaluating and comparing the potential benefits of different preservation measures, which, so far, is lacking. This model will support collection keepers in the complex decision-making process of collection management by comparing different strategies, and therefore finding potential synergies as well as counter-intuitive decision outcomes which otherwise might not have been identified

Dichotomic role of NAADP/two-pore channel 2/Ca2+ signaling in regulating neural differentiation of mouse embryonic stem cells

Poster Presentation - Stem Cells and Pluripotency: abstract no. 1866The mobilization of intracellular Ca2+stores is involved in diverse cellular functions, including cell proliferation and differentiation. At least three endogenous Ca2+mobilizing messengers have been identified, including inositol trisphosphate (IP3), cyclic adenosine diphosphoribose (cADPR), and nicotinic adenine acid dinucleotide phosphate (NAADP). Similar to IP3, NAADP can mobilize calcium release in a wide variety of cell types and species, from plants to animals. Moreover, it has been previously shown that NAADP but not IP3-mediated Ca2+increases can potently induce neuronal differentiation in PC12 cells. Recently, two pore channels (TPCs) have been identified as a novel family of NAADP-gated calcium release channels in endolysosome. Therefore, it is of great interest to examine the role of TPC2 in the neural differentiation of mouse ES cells. We found that the expression of TPC2 is markedly decreased during the initial ES cell entry into neural progenitors, and the levels of TPC2 gradually rebound during the late stages of neurogenesis. Correspondingly, perturbing the NAADP signaling by TPC2 knockdown accelerates mouse ES cell differentiation into neural progenitors but inhibits these neural progenitors from committing to the final neural lineage. Interestingly, TPC2 knockdown has no effect on the differentiation of astrocytes and oligodendrocytes of mouse ES cells. Overexpression of TPC2, on the other hand, inhibits mouse ES cell from entering the neural lineage. Taken together, our data indicate that the NAADP/TPC2-mediated Ca2+signaling pathway plays a temporal and dichotomic role in modulating the neural lineage entry of ES cells; in that NAADP signaling antagonizes ES cell entry to early neural progenitors, but promotes late neural differentiation.postprin