Pandemics can significantly impact public health and society, for instance, the 2009 H1N1<br />and the 2003 SARS. In addition to analyzing the historic epidemic data, computational simulation of epidemic propagation processes and disease control strategies can help us understand the spatio-temporal dynamics of epidemics in the laboratory. Consequently, the public can be better prepared and the government can control future epidemic outbreaks more effectively. Recently, epidemic propagation simulation systems, which use high performance computing technology, have been proposed and developed to understand disease propagation processes. However, run-time infection situation assessment and intervention adjustment, two important steps in modeling disease propagation, are not well supported in these simulation systems. In addition, these simulation systems are computationally efficient in their simulations, but most of them have<br />limited capabilities in terms of modeling interventions in realistic scenarios.<br />In this dissertation, we focus on building a modeling and simulation environment for epidemic propagation and propagation control strategy. The objective of this work is to<br />design such a modeling environment that both supports the previously missing functions,<br />meanwhile, performs well in terms of the expected features such as modeling fidelity,<br />computational efficiency, modeling capability, etc. Our proposed methodologies to build<br />such a modeling environment are: 1) decoupled and co-evolving models for disease propagation, situation assessment, and propagation control strategy, and 2) assessing situations and simulating control strategies using relational databases. Our motivation for exploring these methodologies is as follows: 1) a decoupled and co-evolving model allows us to design modules for each function separately and makes this complex modeling system design simpler, and 2) simulating propagation control strategies using relational databases improves the modeling capability and human productivity of using this modeling environment. To evaluate our proposed methodologies, we have designed and built a loosely coupled and database supported epidemic modeling and simulation environment. With detailed experimental results and realistic case studies, we demonstrate that our modeling environment provides the missing functions and greatly enhances many expected features, such as modeling capability, without significantly sacrificing computational efficiency and scalability. / Ph. D.

Scalability is an important problem in epidemiological applications that simulate complex intervention scenarios over large datasets. Indemics is one such interactive data intensive framework for High-performance computing (HPC) based large-scale epidemic simulations. In the Indemics framework, interventions are supplied from an external, standalone database which proved to be an effective way of implementing interventions. Although this setup performs well for simple interventions and small datasets, performance and scalability of complex interventions and large datasets remain an issue. In this thesis, we present IndemicsXC, a scalable and massively parallel high-performance data engine for Indemics in a supercomputing environment. IndemicsXC has the ability to implement complex interventions over large datasets. Our distributed database solution retains the simplicity of Indemics by using the same SQL query interface for expressing interventions. We show that our solution implements the most complex interventions by intelligently offloading them to the supercomputer nodes and processing them in parallel. We present an extensive performance evaluation of our database engine with the help of various intervention case studies over synthetic population datasets. The evaluation of our parallel and distributed database framework illustrates its scalability over standalone database. Our results show that the distributed data engine is efficient as it is parallel, scalable and cost-efficient means of implementing interventions. The proposed cost-model in this thesis could be used to approximate intervention query execution time with decent accuracy. The usefulness of our distributed database framework could be leveraged for fast, accurate and sensible decisions by the public health officials during an outbreak. Finally, we discuss the considerations for using distributed databases for driving large-scale simulations. / Master of Science

Epidemiological models are used to inform health policy on issues such as target vaccination levels, comparing quarantine options and estimating the eventual size of an epidemic. Models that incorporate some elements of the social network structure are used for diseases where close contact is required for transmission. The motivation of this research is to extend epidemic models to include the relationship with a broader set of relevant real world network properties. The impact of degree distribution by itself is reasonably well understood, but studies with assortativity or clustering are limited and none examine their interaction. To evaluate the impact of these properties, I simulate epidemics on networks with a range of property values. However, a suitable algorithm to generate the networks is not available in the literature. There are thus two research aspects: generating networks with relevant properties, and estimating the impact of social network structure on epidemic behaviour. Firstly, I introduce a flexible network generation algorithm that can independently control degree distribution, clustering coefficient and degree assortativity. Results show that the algorithm is able to generate networks with properties that are close to those targeted. Secondly, I fit models that account for the relationship between network properties and epidemic behaviour. Using results from a large number of epidemic simulations over networks with a range of properties, regression models are fitted to estimate the separate and joint effect of the identified social network properties on the probability of an epidemic occurring and the basic reproduction ratio. The latter is a key epidemic parameter that represents the number of people infected by a typical initial infected person in a population. Results show that social network properties have a significant influence on epidemic behaviour within the property space investigated. Ignoring the differences between social networks can lead to substantial errors when estimating the basic reproduction ratio from an epidemic and then applying the estimate to a different social network. In turn, these errors could lead to failure in public health programs that rely on such estimates.

Made available in DSpace on 2015-07-22T20:49:51Z (GMT). No. of bitstreams: 0 Previous issue date: 2011-03-30 / A idéia central desta dissertação de mestrado foi contribuir para a modernização do sistema de vigilância epidemiológica por meio do desenvolvimento de um modelo para o estudo da evolução da Tuberculose (TB) no tempo, disponibilizando este modelo em um ambiente computacional. O modelo citado foi desenvolvido para suportar experimentos controlados com estimativas realísticas, parametrizado segundo a mobilidade e interação da tuberculose com a população, bem como pelo seu desenvolvimento entre os hospedeiros. O modelo de disseminação da tuberculose foi baseado em um autômato celular probabilístico, projetado para explorar explicitamente o comportamento espaço-temporal de uma população heterogênea. As regras de transição mimetizaram os tipos de interação relacionados ao desenvolvimento da doença nessa população. Os resultados obtidos indicaram que a vacina, no que diz respeito à tuberculose, não influencia no seu desaparecimento e que apesar de apresentar um impacto muito grande na tuberculose primária, não tem poder relevante sobre a tuberculose secundária (pulmonar), sendo essa, a forma que transporta a doença de um individuo para outro. Apenas com tratamentos completamente realizados será possível o ver recrudescimento da TB. / TEDE / BV UNIFESP: Teses e dissertações