This thesis investigates the optimal use of intervention strategies to mitigate the spread of infectious diseases. Three main problems are addressed: (i) The optimal use vaccination and isolation resources under the assumption that these resources are limited. Specifically we address the problem of minimizing the outbreak size and we determine the optimal vaccination-only, isolation-only and mixed vaccination-isolation strategies. (ii) The optimal use of a single antiviral drug to minimize the total outbreak size, under the assumption that treatment causes de novo resistance. (iii) The optimal use of two antiviral drugs to minimize the total infectious burden. Specifically we address the situation where there are two different strains and each strain is effectively treated by only one drug. / Thesis (Ph.D, Mathematics & Statistics) -- Queen's University, 2011-01-25 19:59:17.263

This dissertation focuses on immunological, epidemiological, and economic modeling of HIV, influenza, and fungal meningitis, and includes three research studies. In the first study on HIV, the study objective is to analyze the dynamics of HIV-1, CD4+ T cells and macrophages during the acute, clinically latent and late phases of HIV infection in order to predict their dynamics from acute infection to clinical latency and finally to AIDS in treatment naive HIV-infected individuals. The findings of the study show that the peak in viral load during acute HIV infection is due to virus production by infected CD4+ T cells, while during the clinically latent and late phases of infection infected macrophages dominate the overall viral production. This leads to the conclusion that macrophage-induced virus production is the significant driver of HIV progression from asymptomatic phase to AIDS in HIV-infected individuals. In the second study on influenza, the study objective is to estimate the direct and indirect epidemiological and economic impact of vaccine interventions during an influenza pandemic in Chicago, and assist in vaccine intervention priorities. Population is distributed among high-risk and non-high risk within 0-19, 20-64 and 65+ years subpopulations. The findings show that based on risk of death and return on investment, high-risk groups of the three age group subpopulations can be prioritized for vaccination, and the vaccine interventions are cost-saving for all age and risk groups. In the third study on fungal meningitis, the study objective is to evaluate the effectiveness and cost of the fungal meningitis outbreak response in New River Valley of Virginia during 2012-2013, from the local public health department and clinical perspectives. We estimate the epidemiological effectiveness of this outbreak response to be 153 DALYs averted among the patients, and the costs incurred by the local health department and clinical facilities to be $30,413 and $39,580 respectively. Moving forward, multi-scale analysis of infectious diseases connecting the different scales of evolutionary, immunological, epidemiological, and economic dynamics has good potential to derive meaningful inferences for decision making in clinical and public health practice, and improve health outcomes. / Ph. D.

Emerging infectious diseases (EID) pose a serious threat to global public health. Computational epidemiology is a nascent subfield of public health that can provide insight into an outbreak in advance of traditional methodologies. Research in this dissertation will use fuse nontraditional, publicly available data sources with more traditional epidemiological data to build and parameterize models of emerging infectious diseases. These methods will be applied to avian influenza A (H7N9), Middle Eastern Respiratory Syndrome Coronavirus (MERS-CoV), and Ebola virus disease (EVD) outbreaks. This effort will provide quantitative, evidenced-based guidance for policymakers and public health responders to augment public health operations. / Ph. D.

Individual-level models are a class of complex statistical models, often fitted within a Bayesian Markov chain Monte Carlo framework, that have been effectively used to model the spread of infectious diseases. The ability of these models to incorporate individual-level covariate information allows them to be highly flexible, and to account for such characteristics as population heterogeneity. However, these models can be subject to inherent uncertainties often found in infectious disease data. As well, their complex nature can lead to a significant computational expense when fitting these models to epidemic data, particularly for large populations. An individual-level model that incorporates a latent grouping structure into the modeling procedure, based on some heterogeneous population characteristics, is investigated. The dependence of this latent conditional individual-level model on a discrete latent grouping variable alleviates the need for explicit, although possibly unreliable, covariate information. A simulation study is used to assess the posterior predictive ability of this model, in comparison to individual-level models that utilize the full covariate information, or that assume population homogeneity. These models are also applied to data from the 2001 UK foot-and-mouth disease epidemic. When attempting to compare complex models fitted within the Bayesian framework, the identification of appropriate model selection tools would be beneficial. The use of deviance information criterion (DIC) as model comparison tool, particularly for the latent conditional individual-level models, is investigated. A simulation study is used to compare five variants of the DIC, and the ability of each DIC variant to select the true model is determined. Finally, an investigation into methods to reduce the computational burden associated with individual-level models is carried out, based on an individual-level model that also incorporates population heterogeneity through a discrete grouping variable. A simulation study is used to determine the effect of reducing the overall population size by aggregating the data into spatial clusters. Reparameterized individual-level models, accounting for the aggregation effect, are fitted to the aggregated data. The effect of data aggregation on the ability of two reparameterized individual-level models to identify a covariate effect, as well as on the computational expense of the model fitting procedure, is explored.

Commonly used simulation models for predicting outbreaks of re-emerging infectious diseases (EIDs) take an individual-level or a population-level approach to modeling contact dynamics. These approaches are a trade-off between the ability to incorporate individual-level dynamics and computational efficiency. Agent-based network models (ABNM) use an individual-level approach by simulating the entire population and its contact structure, which increases the ability of adding detailed individual-level characteristics. However, as this method is computationally expensive, ABNMs use scaled-down versions of the full population, which are unsuitable for low prevalence diseases as the number of infected cases would become negligible during scaling-down. Compartmental models use differential equations to simulate population-level features, which is computationally inexpensive and can model full-scale populations. However, as the compartmental model framework assumes random mixing between people, it is not suitable for diseases where the underlying contact structures are a significant feature of disease epidemiology. Therefore, current methods are unsuitable for simulating diseases that have low prevalence and where the contact structures are significant. The conceptual framework for a new simulation method, Evolving Contact Network Algorithm (ECNA), was recently proposed to address the above gap. The ECNA combines the attributes of ABNM and compartmental modeling. It generates a contact network of only infected persons and their immediate contacts, and evolves the network as new persons become infected. The conceptual framework of the ECNA is promising for application to diseases with low prevalence and where contact structures are significant. This thesis develops and tests different algorithms to advance the computational capabilities of the ECNA and its flexibility to model different network settings. These features are key components that determine the feasibility of ECNA for application to disease prediction. Results indicate that the ECNA is nearly 20 times faster than ABNM when simulating a population of size 150,000 and flexible for modeling networks with two contact layers and communities. Considering uncertainties in epidemiological features and origin of future EIDs, there is a significant need for a computationally efficient method that is suitable for analyses of a range of potential EIDs at a global scale. This work holds promise towards the development of such a model.

Ces dernières années, la puissance croissante des ordinateurs a permis à la fois de rassembler une quantité sans précédent de données décrivant la société moderne et d'envisager des outils numériques capables de s'attaquer à l'analyse et la modélisation les processus dynamiques qui se déroulent dans cette réalité complexe. Dans cette perspective, l'approche quantitative de la physique est un des catalyseurs de la croissance de nouveaux domaines interdisciplinaires visant à la compréhension des systèmes complexes techno-sociaux. Dans cette thèse, nous présentons dans cette thèse un cadre théorique et numérique pour simuler des épidémies de maladies infectieuses émergentes dans des contextes réalistes. Dans ce but, nous utilisons le rôle crucial de la mobilité des agents dans la diffusion des maladies infectieuses et nous nous appuyons sur l'étude des réseaux complexes pour gérer les ensembles de données à grande échelle décrivant les interconnexions de la population mondiale. En particulier, nous abordons deux différents problèmes de santé publique. Tout d'abord, nous considérons la propagation d’une épidémie au niveau mondial, et présentons un modèle de mobilité (GLEAM) conçu pour simuler la propagation d'une maladie de type grippal à l'échelle globale, en intégrant des données réelles de mobilité dans le monde entier. La dernière pandémie de grippe H1N1 2009 a démontré la nécessité de modèles mathématiques pour fournir des prévisions épidémiques et évaluer l'efficacité des politiques d'interventions. Dans cette perspective, nous présentons les résultats obtenus en temps réel pendant le déroulement de l'épidémie, ainsi qu'une analyse a posteriori portant sur les stratégies de lutte et sur la validation du modèle. Le deuxième problème que nous abordons est lié à la propagation de l'épidémie sur des systèmes en réseau dépendant du temps. En particulier, nous analysons des données décrivant les mouvements du bétail en Italie afin de caractériser les corrélations temporelles et les propriétés statistiques qui régissent ce système. Nous étudions ensuite la propagation d'une maladie infectieuse, en vue de caractériser la vulnérabilité du système et de concevoir des stratégies de contrôle. Ce travail est une approche interdisciplinaire qui combine les techniques de la physique statistique et de l'analyse des systèmes complexes dans le contexte de la mobilité des agents et de l'épidémiologie numérique. / In recent years, the increasing availability of computer power has enabled both to gather an unprecedented amount of data depicting the global interconnections of the modern society and to envision computational tools able to tackle the analysis and the modeling of dynamical processes unfolding on such a complex reality. In this perspective, the quantitative approach of Physics is catalyzing the growth of new interdisciplinary fields aimed at the understanding of complex techno-socio-ecological systems. By recognizing the crucial role of host mobility in the dissemination of infectious diseases and by leveraging on a network science approach to handle the large scale datasets describing the global interconnectivity, in this thesis we present a theoretical and computational framework to simulate epidemics of emerging infectious diseases in real settings. In particular we will tackle two different public health related issues. First, we present a Global Epidemic and Mobility model (GLEaM) that is designed to simulate the spreading of an influenza-like illness at the global scale integrating real world-wide mobility data. The 2009 H1N1 pandemic demonstrated the need of mathematical models to provide epidemic forecasts and to assess the effectiveness of different intervention policies. In this perspective we present the results achieved in real time during the unfolding of the epidemic and a posteriori analysis on travel related mitigation strategies and model validation. The second problem that we address is related to the epidemic spreading on evolving networked systems. In particular we analyze a detailed dataset of livestock movements in order to characterize the temporal correlations and the statistical properties governing the system. We then study an infectious disease spreading, in order to characterize the vulnerability of the system and to design novel control strategies. This work is an interdisciplinary approach that merges statistical physics techniques, complex and multiscale system analysis in the context of hosts mobility and computational epidemiology.

La modélisation des maladies infectieuses représente un outil important pour évaluer la vulnérabilité d'une population à l'introduction d'un nouveau agent pathogène. La possibilité d’enregistrer les contacts responsables de la propagation des maladies représente à la fois une ressource et un défi pour les modèles épidémiques. En particulier, l'interaction entre la dynamique des maladies et l'évolution dans le temps des structures de contact influence la façon dont les agents pathogènes se propagent, en changeant les conditions qui mènent à une flambée épidémique (seuil épidémique). Jusqu'à maintenant, les chercheurs n'ont caractérisé le seuil épidémique sur des structures de contact qui évoluent dans le temps que dans des contextes spécifiques. En utilisant un formalisme multi-couches, nous calculons analytiquement le seuil épidémique sur un réseau temporel générique. Nous utilisons cette méthode pour évaluer l'impact de la résolution temporelle et la durée du réseau sur l'estimation du seuil. De plus, grâce à cette méthode, nous évaluons la vulnérabilité globale de différents systèmes à l'introduction d'agents pathogènes, et en particulier nous analysons les réseaux de mouvements des bovins. Les données de contact souvent ne sont pas disponible en temps réel, et cela limite notre capacité de prévision. Pour répondre à ça, nous développons une méthodologie numérique pour prédire le risque épidémique ciblé, qui repose uniquement sur les données de contact passées. Notre travail fournit de nouvelles méthodologies pour évaluer et prédire le risque associé à un agent pathogène émergent, à la fois à l'échelle de la population et en ciblant des hôtes spécifiques. / Infectious disease modeling represents a powerful tool for assessing the vulnerability of a population to the introduction of a new infectious pathogen. The increased availability of highly resolved data tracking host interactions is making epidemic models potentially increasingly accurate. Integrating into them all the features emerging from these data, however, still represents a challenge. In particular, the interaction between disease dynamics and the time evolution of contact structures has been shown to impact the way pathogens spread, changing the conditions that lead to the wide-spreading regime, as encoded in epidemic threshold. Up to now researchers have characterized the epidemic threshold on time evolving contact structures only in specific settings. Using a multilayer formalism, we analytically compute the epidemic threshold on a generic temporal network, accounting for several different disease features. We use this methodology to assess the impact of time resolution and network duration on the estimation of the threshold. Then, thanks to it, we assess the global vulnerability of different systems to pathogen introduction, and in particular we analyze the networks of cattle trade movements Data collection strategies often inform us only about past network configurations, and that limits our prediction capabilities. We face this by developing a data-driven methodology for predicting targeted epidemic that relies only past contact data. Our work provides new methodologies for assessing and predicting the risk associated to an emerging pathogen, both at the population scale and targeting specific hosts.

Introduction: The Aedes aegypti mosquito is responsible for the transmission of Yellow Fever, Dengue, Chikungunya and Zikavirus, making it a deadly vector and global public health threat. Zikavirus and Chikungunya, which were previously restricted to smaller geographic areas, have both appeared in the Western Hemisphere in the past three years and spread to areas where A. aegypti are present. This means that the pathogens have now entered areas in which the population has no previous immunity, which can lead to extensive outbreaks and epidemics. As the effects of global climate change become apparent, the areas of the globe that are suitable for inhabitance by A. aegypti may change. Additionally, this vector prefers human hosts for blood meals and requires standing water to breed, which is often created by water storage containers. This means that increasing urbanization and human population density are likely to put populations at higher risk of exposure to this vector. Methods: To create maps of the future risk of exposure to Aedes aegypti globally, species occurrence data for the vector and the Maxent modeling approach were used. Current and projected climate data were downloaded from WorldClim.org for the four representative concentration pathways (RCPs) used to model future climate change. Human population density, projected to 2050, the same timeframe as the future climate data, were used to model changes in human populations. To identify areas at high risk for future presence of A. aegypti populations, current and future models were compared across areas with at least a 50% probability of increased risk. These results where then used to create maps displaying high risk areas. Results: The AUC, an indicator of model fit, signaled that the models had high predictive power. However, high omission rates indicated that the trade-off of risk mapping may be a need to decrease probability thresholds below 50% to capture the full at-risk population. Future high-risk areas were most often those surrounding current cities, which supports the idea that the combination of urbanization and increasing human population density will work synergistically to increase the disease burden within and around urban centers. Additionally, expansion at the current geographic margins of this species shows that incursion into currently non-endemic areas is possible. Conclusions: Urban and peri-urban populations are likely to be at higher risk of exposure compared to rural areas due to global climate change and changes in population density. Attempts to model expansion of vector habitats should consider how these human population characteristics will change the risk to populations and how to best identify the areas at highest risk. Thresholds for the probability of a population being at risk of exposure to a vector may need to be different from those required to determine whether or not a habitat is suitable for a species. Appropriately determining which areas are high-risk results in maps and models can then be used to identify areas where climate change mitigation and vector control efforts are likely to have the highest impacts.

The Aedes aegypti mosquito is responsible for transmission of four vector-borne diseases that cause considerable global morbidity and mortality. Projections of the future effects of global climate change indicate that expansion of this species due to changing habitats is possible. Furthermore, since A. aegypti is highly dependent on human populations for feeding and egg-laying sites, changing human population characteristics are likely to alter the risk of exposure for humans based on geographic location. This study aims to create future potential risk maps for human exposure to A. aegypti using human population density as a predictor. Using current population density data and future growth trajectories, high-resolution human population density forecasts were created for 2050, then included as variables in ecological niche models developed using Maxent. Species occurrence data and high resolution climate data for current and future conditions (best and worst case scenarios) were included in the model, as well. Model fit indices and variable contributions indicated that the inclusion of human population density improves model accuracy for A. aegypti. Risk maps created by these models showed that areas currently adjacent to large cities within endemic regions, such as central Africa and western Brazil, are likely to see the greatest increase in risk to human populations. This corroborates current projections on increasing urbanization in the future and suggests that these models can be used to target interventions in high risk areas.

No description available.