Epidemics have caused major human and monetary losses through the course of human civilization. It is very important that epidemiologists and public health personnel are prepared to handle an impending infectious disease outbreak. the ever-changing demographics, evolving infrastructural resources of geographic regions, emerging and re-emerging diseases, compel the use of simulation to predict disease dynamics. By the means of simulation, public health personnel and epidemiologists can predict the disease dynamics, population groups at risk and their geographic locations beforehand, so that they are prepared to respond in case of an epidemic outbreak. As a consequence of the large numbers of individuals and inter-personal interactions involved in simulating infectious disease spread in a region such as a county, sizeable amounts of data may be produced that have to be analyzed. Methods to visualize this data would be effective in facilitating people from diverse disciplines understand and analyze the simulation. This thesis proposes a framework to simulate and visualize the spread of an infectious disease in a population of a region such as a county. As real-world populations have a non-homogeneous demographic and spatial distribution, this framework models the spread of an infectious disease based on population of and geographic distance between census blocks; social behavioral parameters for demographic groups. the population is stratified into demographic groups in individual census blocks using census data. Infection spread is modeled by means of local and global contacts generated between groups of population in census blocks. the strength and likelihood of the contacts are based on population, geographic distance and social behavioral parameters of the groups involved. the disease dynamics are represented on a geographic map of the region using a heat map representation, where the intensity of infection is mapped to a color scale. This framework provides a tool for public health personnel and epidemiologists to run what-if analyses on disease spread in specific populations and plan for epidemic response. By the means of demographic stratification of population and incorporation of geographic distance and social behavioral parameters into the modeling of the outbreak, this framework takes into account non-homogeneity in demographic mix and spatial distribution of the population. Generation of contacts per population group instead of individuals contributes to lowering computational overhead. Heat map representation of the intensity of infection provides an intuitive way to visualize the disease dynamics.

Susceptibles-infectives-removals (SIR) and its derivatives are the classic mathematical models for the study of infectious diseases in epidemiology. In order to model and simulate epidemics of an infectious disease, a global stochastic field simulation paradigm (GSFS) is proposed, which incorporates geographic and demographic based interactions. The interaction measure between regions is a function of population density and geographical distance, and has been extended to include demographic and migratory constraints. The progression of diseases using GSFS is analyzed, and similar behavior to the SIR model is exhibited by GSFS, using the geographic information systems (GIS) gravity model for interactions. The limitations of the SIR and similar models of homogeneous population with uniform mixing are addressed by the GSFS model. The GSFS model is oriented to heterogeneous population, and can incorporate interactions based on geography, demography, environment and migration patterns. The progression of diseases can be modeled at higher levels of fidelity using the GSFS model, and facilitates optimal deployment of public health resources for prevention, control and surveillance of infectious diseases.

Computational epidemiology involves processing, analysing and managing large volumes of data. Such massive datasets cannot be handled efficiently by using traditional standalone database management systems, owing to their limitation in the degree of computational efficiency and bandwidth to scale to large volumes of data. In this thesis, we address management and processing of large volumes of data for modeling, simulation and analysis in epidemiological studies. Traditionally, compute intensive tasks are processed using high performance computing resources and supercomputers whereas data intensive tasks are delegated to standalone databases and some custom programs. DiceX framework is a one-stop solution for distributed database management and processing and its main mission is to leverage and utilize supercomputing resources for data intensive computing, in particular relational data processing. While standalone databases are always on and a user can submit queries at any time for required results, supercomputing resources must be acquired and are available for a limited time period. These resources are relinquished either upon completion of execution or at the expiration of the allocated time period. This kind of reservation based usage style poses critical challenges, including building and launching a distributed data engine onto the supercomputer, saving the engine and resuming from the saved image, devising efficient optimization upgrades to the data engine and enabling other applications to seamlessly access the engine . These challenges and requirements cause us to align our approach more closely with cloud computing paradigms of Infrastructure as a Service(IaaS) and Platform as a Service(PaaS). In this thesis, we propose cloud computing like workflows, but using supercomputing resources to manage and process relational data intensive tasks. We propose and implement several services including database freeze and migrate and resume, ad-hoc resource addition and table redistribution. These services assist in carrying out the workflows defined. We also propose an optimization upgrade to the query planning module of postgres-XC, the core relational data processing engine of the DiceX framework. With a knowledge of domain semantics, we have devised a more robust data distribution strategy that would enable to push down most time consuming sql operations forcefully to the postgres-XC data nodes, bypassing its query planner's default shippability criteria without compromising correctness. Forcing query push down reduces the query processing time by a factor of almost 40%-60% for certain complex spatio-temporal queries on our epidemiology datasets. As part of this work, a generic broker service has also been implemented, which acts as an interface to the DiceX framework by exposing restful apis, which applications can make use of to query and retrieve results irrespective of the programming language or environment. / Master of Science

EpiFast is a parallel algorithm for large-scale epidemic simulations, based on an interpretation of the stochastic disease propagation in a contact network. The original EpiFast implementation is based on a master-slave computation model with a focus on distributed memory using message-passing-interface (MPI). However, it suffers from few shortcomings with respect to scale of networks being studied. This thesis addresses these shortcomings and provides two different implementations: Spark-EpiFast based on the Apache Spark big data processing engine and Charm-EpiFast based on the Charm++ parallel programming framework. The study focuses on exploiting features of both systems that we believe could potentially benefit in terms of performance and scalability. We present models of EpiFast specific to each system and relate algorithm specifics to several optimization techniques. We also provide a detailed analysis of these optimizations through a range of experiments that consider scale of networks and environment settings we used. Our analysis shows that the Spark-based version is more efficient than the Charm++ and MPI-based counterparts. To the best of our knowledge, ours is one of the preliminary efforts of using Apache Spark for epidemic simulations. We believe that our proposed model could act as a reference for similar large-scale epidemiological simulations exploring non-MPI or MapReduce-like approaches. / Master of Science

This dissertation examines the socio-behavioral determinants of vaccination and their impacts on public health, using a systems approach that emphasizes the interface between population health research, policy, and practice. First, we identify the facilitators and barriers of parental attitudes and beliefs toward school-located influenza vaccination in the United States. Next, we examine current vaccine sentiment on social media by constructing and analyzing semantic networks of vaccine information online. Finally, we estimate the health benefits, costs, and cost-effectiveness of influenza vaccination strategies in Seattle using a dynamic agent-based model. The underlying motivation for this research is to better inform public health policy by leveraging the facilitators and addressing potential barriers against vaccination; by understanding vaccine sentiment to improve health science communication; and by assessing potential vaccination strategies that may provide the greatest gains in health for a given cost in health resources. / PHD / Public health decisions are ultimately left to those in policy, however these decisions are often subjective and rarely informed by data. This dissertation comprises three studies that, individually, examine various public health aspects of vaccination, and collectively, aim to help inform decision makers by bridging the gaps that persist between scientific evidence and the implementation of relevant health policy. First, we identify the facilitators and barriers of parental attitudes and beliefs toward school-located influenza vaccination in the United States. Next, we examine current vaccine sentiment on social media by constructing and analyzing semantic networks of vaccine information online. Finally, we estimate the health benefits, costs, and cost-effectiveness of influenza vaccination strategies in Seattle using a dynamic agent-based model. The work presented here demonstrates a systems approach to public health by way of computational modeling and interdisciplinary perspectives that describe vaccination behavior at the intersection of public health research, policy, and practice. The motivation for this research is to better inform public health policy: by leveraging the facilitators and addressing potential barriers against vaccination; by understanding vaccine sentiment to improve health science communication; and by assessing potential vaccination strategies that may provide the greatest gains in health for a given cost in health resources.

Master of Science / Department of Biological & Agricultural Engineering / Caterina M. Scoglio / Lisa R. Wilken / Between the years 2014 and 2015, the world experienced a catastrophic outbreak of Ebola virus, which killed over 26,000 people. Several authorities and organizations actively participated in fighting the epidemic. Infectious disease modelers proved to be invaluable towards this goal. This report provides a background on the Ebola epidemic in West Africa and reviews the biological features of the Ebola virus. Moreover, this report applies a new model for Ebola propagation using data collected by the World Health Organization during the span of the outbreak. The model estimates the reproduction number and assesses the role of mitigation strategies in slowing down the progress of the disease. The report also concludes a review of recent advancements in vaccine production against Ebola.

Advances in scientific data management, discovery, dissemination, and sharing are changing the manner in which scientific studies are being conducted and repurposed. Data-intensive scientific practices increasingly require data management related services not available in existing digital libraries. Complicating the issue are the diversity of functional requirements and content in scientific domains as well as scientists' lack of expertise in information and library sciences. Researchers that utilize simulation and experimentation systems need digital libraries to maintain datasets, input configurations, results, analyses, and related documents. A digital library may be integrated with simulation infrastructures to provide automated support for research components, e.g., simulation interfaces to models, data warehouses, simulation applications, computational resources, and storage systems. Managing and provisioning simulation content allows streamlined experimentation, collaboration, discovery, and content reuse within a simulation community. Formal definitions of this class of digital libraries provide a foundation for producing a software toolkit and the semi-automated generation of digital library instances. We present a generic, component-based SIMulation-supporting Digital Library (SimDL) framework. The framework is formally described and provides a deployable set of domain-free services, schema-based domain knowledge representations, and extensible lower and higher level service abstractions. Services in SimDL are specialized for semi-structured simulation content and large-scale data producing infrastructures, as exemplified in data storage, indexing, and retrieval service implementations. Contributions to the scientific community include previously unavailable simulation-specific services, e.g., incentivizing public contributions, semi-automated content curating, and memoizing simulation-generated data products. The practicality of SimDL is demonstrated through several case studies in computational epidemiology and network science as well as performance evaluations. / Ph. D.

In recent years, several methods have been proposed for real-time modeling and forecasting of the epidemic curve. These methods range from simple compartmental models to complex agent-based models. In this dissertation, we present a model-based reasoning approach to forecasting the epidemic curve and estimating underlying disease model parameters. The method is based on building an epidemic library consisting of past and simulated influenza outbreaks. During an influenza epidemic, we use a combination of statistical, optimization and modeling techniques to either assign the epidemic to one of the cases in the library or propose parameters for modeling the epidemic. The method is presented in four steps. First, we discuss a sensitivity analysis study evaluating how minute changes in the disease model parameters influence the dynamics of simulated epidemics. Next, we present a supervised classification method for predicting the epidemic curve. The epidemic curve is forecasted by matching the partial surveillance curve to at least one of the epidemics in the library. We expand on the classification approach by presenting a method which identifies epidemics similar or different from those in the library. Lastly, we discuss a simulation optimization method for estimating model parameters to forecast the epidemic curve of an epidemic distinct from those in the library. / Ph. D.

Nos travaux de thèse portent sur la modélisation et la simulation à base d'agents de systèmes complexes, appliquées au phénomène de propagation de la Schistosomose. Plus particulièrement, nous nous sommes intéressés aux aspects spatiaux et sociaux de la propagation de cette maladie, en utilisant une approche de couplage de modèles à base d'agents. En effet, nous avons initialement étudié la modélisation mathématique de la Schistosomose et la complexité du phénomène de sa propagation. Ce qui nous a permis d'identifier deux dynamiques épidémiologiques (dynamiques spatiale et sociale) sous-jacentes à la propagation de la Schistosomose pour lesquelles, les modèles mathématiques présentent des limites. Cette problématique nous a poussés à étudier isolément ces deux dynamiques et à proposer un modèle multi-agents pour chacune d'elles. Ces deux modèles à base d'agents, représentant deux dynamiques complémentaires d'un même système, ont été implémentés selon des formalismes et des plateformes différentes : un modèle dans GAMA, une plateforme de simulation à base d'agents ; et un autre dans JASON, une plateforme de programmation d'agents BDI (Belief, Desire, Intention). Le modèle GAMA implémente l'aspect comportemental (pour la dynamique spatiale) qui se penche sur la réactivité des individus face à l'environnement physique et le suivi de l'infection. Le modèle JASON implémente l'aspect décisionnel (pour la dynamique sociale) qui introduit la dimension cognitive et mentale des individus en assurant leur capacité de décision et de sélection qui sont déterminées par leur environnement social, culturel, économique, etc. Pour assurer la composition des deux modèles, nous avons proposé une solution de couplage (par Co-simulations) des deux plateformes GAMA et JASON. Nous avons finalement expérimenté le modèle avec un cas de dynamique de propagation de la maladie à Niamey (au Niger) pour lequel les données étaient accessibles. / Our thesis work focuses on agent-based modeling and simulation of complex systems, applied to the spread of schistosomiasis. Specially, we were interested in the spatial and social aspects of the spread of the disease, using an agent-based coupling approach of models.Indeed, we initially studied the mathematical modeling of schistosomiasis and the complexity of its propagation, which allowed us to identify two epidemiological dynamics (spatial and social dynamics) underlying the spread of schistosomiasis for which mathematical models have limits. This problematic led us to study separately these two dynamics and propose an agent-based model for each. These two agent-based models, representing two complementary dynamics of a system, were implemented according different formalisms and different platforms: one model on GAMA (an agent-based simulation platform); and another on JASON (a programming platform of BDI agents). The GAMA model implements the behavioral aspect (for the spatial dynamic) that focuses on individuals reactivity with regards to the physical environment, and the monitoring of the infection. The JASON model implements the decisional aspect (for the social dynamic) that introduces the cognitive and mental dimension of individuals, ensuring their decision and selection capacities which are determined by their social, cultural and economic environment. To ensure the composition of the two models, we proposed an agent-based coupling solution (co-simulation) of the two platforms (GAMA and JASON). We finally experienced the model with a case of dynamic spread of the disease in Niamey (Niger) for which data were available.

The contact patterns among individuals can significantly affect the progress of an infectious outbreak within a population. Gathering data about these interaction and mixing patterns is essential to assess computational modeling of infectious diseases. Various self-report approaches have been designed in different studies to collect data about contact rates and patterns. Recent advances in sensing technology provide researchers with a bilateral automated data collection devices to facilitate contact gathering overcoming the disadvantages of previous approaches. In this study, a novel unilateral wearable sensing architecture has been proposed that overcome the limitations of the bi-lateral sensing. Our unilateral wearable sensing system gather contact data using hybrid sensor arrays embedded in wearable shirt. A smartphone application has been used to transfer the collected sensors data to the cloud and apply deep learning model to estimate the number of human contacts and the results are stored in the cloud database. The deep learning model has been developed on the hand labelled data over multiple experiments. This model has been tested and evaluated, and these results were reported in the study. Sensitivity analysis has been performed to choose the most suitable image resolution and format for the model to estimate contacts and to analyze the model's consumption of computer resources.