Identifying and tracking individuals affected by this virus in densely populated areas is a unique and an urgent challenge in the public health sector. Currently, mapping the spread of the Ebola virus is done manually, however with the help of social contact networks we can model dynamic graphs and predictive diffusion models of Ebola virus based on the impact on either a specific person or a specific community. With the help of this model, we can make more precise forward predictions of the disease propagations and to identify possibly infected individuals which will help perform trace – back analysis to locate the possible source of infection for a social group. This model will visualize and identify the families and tightly connected social groups who have had contact with an Ebola patient and is a proactive approach to reduce the risk of exposure of Ebola spread within a community or geographic location. / Includes bibliography. / Thesis (M.S.)--Florida Atlantic University, 2017. / FAU Electronic Theses and Dissertations Collection

Les fièvres hémorragiques à virus Lassa (LASV) et Ebola (EBOV) représentent un important problème de santé publique en Afrique. Les réponses immunes et la pathogenèse associées à ces maladies sont peu connues. Les cellules NK ont un rôle important dans la réponse immune innée par leurs propriétés cytotoxiques, mais également dans l'induction des réponses adaptatives par leur production de cytokines et leurs interactions avec les cellules dendritiques (DC) et les macrophages. Ce projet s'attache à comprendre le rôle des cellules NK dans le contrôle de la réplication virale et dans l'induction des réponses immunitaires au cours de ces infections. Un modèle in vitro de coculture de cellules NK humaines avec des DC et macrophages autologues a été développé. L'activation des cellules NK et leurs fonctions ont été analysées après l'infection par LASV et EBOV. Par ailleurs, les réponses des cellules NK en réponse à LASV ont été comparées avec celles induites lors de l'infection par le virus Mopeia (MOPV), très proche de LASV mais non pathogène pour l'homme. Les macrophages, mais pas les DC, infectés par LASV ou MOPV induisent l'activation et l'augmentation des capacités cytotoxiques des cellules NK. Toutefois, les cellules NK ne sont pas capables de lyser les cellules infectées et ne produisent pas d'IFN-γ. Les cellules NK s'activent et sont capables de lyser les cellules infectées en présence de macrophages mais également de DC infectés par des LASV mutants. Cependant, les IFN de type I sécrétés en grande quantité en réponse à ces virus ne sont pas impliqués dans l'activation des cellules NK. L'infection par EBOV n'induit qu'une très faible activation des cellules NK en présence de DC ou macrophages et ne conduit pas à la sécrétion de cytokines, ni à la modification du potentiel cytotoxique.Ces résultats permettent d'améliorer la compréhension des réponses immunes et des mécanismes de pathogenèse mis en place lors des fièvres hémorragiques Lassa et Ebola.

See front file / Health Studies / DLITT ET PHIL (HEALTH ST)

abstract: The increased number of novel pathogens that potentially threaten the human population has motivated the development of mathematical and computational modeling approaches for forecasting epidemic impact and understanding key environmental characteristics that influence the spread of diseases. Yet, in the case that substantial uncertainty surrounds the transmission process during a rapidly developing infectious disease outbreak, complex mechanistic models may be too difficult to be calibrated quick enough for policy makers to make informed decisions. Simple phenomenological models that rely on a small number of parameters can provide an initial platform for assessing the epidemic trajectory, estimating the reproduction number and quantifying the disease burden from the early epidemic phase. Chapter 1 provides background information and motivation for infectious disease forecasting and outlines the rest of the thesis. In chapter 2, logistic patch models are used to assess and forecast the 2013-2015 West Africa Zaire ebolavirus epidemic. In particular, this chapter is concerned with comparing and contrasting the effects that spatial heterogeneity has on the forecasting performance of the cumulative infected case counts reported during the epidemic. In chapter 3, two simple phenomenological models inspired from population biology are used to assess the Research and Policy for Infectious Disease Dynamics (RAPIDD) Ebola Challenge; a simulated epidemic that generated 4 infectious disease scenarios. Because of the nature of the synthetically generated data, model predictions are compared to exact epidemiological quantities used in the simulation. In chapter 4, these models are applied to the 1904 Plague epidemic that occurred in Bombay. This chapter provides evidence that these simple models may be applicable to infectious diseases no matter the disease transmission mechanism. Chapter 5, uses the patch models from chapter 2 to explore how migration in the 1904 Plague epidemic changes the final epidemic size. The final chapter is an interdisciplinary project concerning within-host dynamics of cereal yellow dwarf virus-RPV, a plant pathogen from a virus group that infects over 150 grass species. Motivated by environmental nutrient enrichment due to anthropological activities, mathematical models are employed to investigate the relevance of resource competition to pathogen and host dynamics. / Dissertation/Thesis / Doctoral Dissertation Applied Mathematics 2016

Les virus Ebola (EBOV) et Marburg (MARV) causent des fièvres hémorragiques chez les primates, y compris l’homme. Le taux de létalité peut atteindre 90% et il n’existe ni vaccin ni traitement contre ces virus. En raison de leurs caractéristiques moléculaires communes, EBOV et MARV sont regroupés au sein de la famille des Filoviridae. Le virion est composé de 7 protéines, dont la VP24, qui joue un rôle important dans l’assemblage et la condensation des nucléocapsides, et pour EBOV, elle est également responsable de l’inhibition de la réponse à l’IFN. Des mutations dans la séquence protéique de VP24 sont impliquées dans le processus d’adaptation chez un nouvel hôte. La protéine VP24 d’EBOV est donc multifonctionnelle. Pour MARV, cette protéine ne semble pas porter les fonctions décrites pour la VP24 d’EBOV. Afin de comprendre le rôle de la VP24 de MARV, nous avons identifié ses partenaires cellulaires par un crible double-hybride en levures. Nous avons mis en évidence l’interaction entre Keap1 et la VP24 de MARV, et confirmé ce résultat en cellules mammifères. Keap1 est une protéine impliquée dans le contrôle de la réponse au stress oxydatif, car elle inhibe le facteur de transcription Nrf2, qui régule l’expression d’enzymes impliquées dans la réduction des ERO. Nos résultats montrent que le domaine de Keap1 liant la VP24 est le même que celui liant Nrf2, et que la VP24 de MARV active Nrf2 pour la synthèse de molécules anti-oxydantes. Nous avons enfin évalué l’impact de la VP24 de MARV sur ERR, une autre cible de Keap1, et mesuré l’activité Nrf2 au cours de l’infection par EBOV. Nos résultats montrent des effets opposés des VP24 d’ EBOV et de MARV sur l’activité de Nrf2. / Ebola (EBOV) and Marburgvirus (MARV) are responsible for severe hemorrhagic syndrome in primates, including humans. The lethality rate can reach 90%, and no vaccine or treatment is available to counteract these diseases. EBOV and MARV have similar genomic organization and thus are placed in a distinct family, Filoviridae. VP24 is one of the 7 structural proteins which form the virion and has been shown to play an important role in assembly and condensation of viral nucleocapsids. VP24 of EBOV is responsible for prevention of cellular response to IFN. Mutations in EBOV VP24 gene are necessary for the adaptation to a new host. EBOV VP24 thus acts as a multifunctional factor. Available data suggest that MARV VP24 is not implicated in either the counteraction of IFN response, or in the adaptation process. In order to discover new functions for VP24 of MARV, we searched for its interaction with cellular proteins, using a yeast-double hybrid approach. We discovered an interaction between MARV VP24 and Keap1 protein and further confirmed this interaction in mammalian cells. Keap1 is a cellular protein involved in intracellular detection of Reactive Oxygen Species (ROS) and in the control of oxidative stress response. It inhibits the Nrf2 transcription factor, which regulates expression of antioxidant enzymes. Our results indicate that Keap1 binding domain for VP24 is the same as the one involved in Nrf2 binding, resulting in activation of transcriptional activity of Nrf2. Impact of MARV VP24 on ERRa, another target of Keap1, was also measured, as well as Nrf2 activity during EBOV infection. Our results showed that VP24 of EBOV and MARV have opposite effect on Nrf2 activity.

During the 2013/16 Ebola virus disease outbreak in West Africa, numerous restrictions were placed on the movement and public gathering of local people, regardless of if the area had active Ebola cases or not. Specifically, the district of Koinadugu, Sierra Leone, preemptively enforced movement regulations before there were any cases within the district. This research demonstrates that ongoing regulations on movement and public gathering affected the livelihoods of those involved in agricultural markets in the short-term, while the outbreak was active, and in the long-term. The forthcoming thesis details the ways in which the Ebola outbreak international and national response affected locals involved in agricultural value chains in Koinadugu, Sierra Leone.

<p dir="ltr">The Ebola virus (EBOV) is a filamentous RNA virus which causes severe hemorrhagic fever. It is one of the most dangerous known pathogens with a high fatality rate. Multiple outbreaks of EBOV have occurred since the 1970s with the most widespread outbreak starting in December 2013. This outbreak continued through May of 2016 and had a fatality rate of approximately 50%. EBOV outbreaks are recurrent because the virus is still present in animal reservoirs. Despite multiple EBOV outbreaks we still lack a clear understanding of how new viral particles are formed and spread through virus assembly and release. Given the widespread global travel, EBOV now poses a threat to the entire world. EBOV encodes for the matrix protein, VP40, which is one of the most conserved viral proteins. VP40 can form different structures leading to different functions of the protein in different stages of the EBOV life cycle. The VP40 dimer traffics to the inner leaflet of the plasma membrane to facilitate assembly and budding. The VP40 octameric ring has been implicated in transcriptional regulation. This thesis focuses on understanding in further detail the determinates of VP40 plasma membrane assembly and exit from an infected cell.</p><p dir="ltr">The assembly and trafficking of VP40 to the plasma membrane requires a network of protein-protein and lipid-protein interactions (PPIs and LPIs). Studying these interfaces is important for understanding how VP40 structure and function regulates trafficking and assembly and can shed light on therapeutic strategies to target EBOV. The alteration of host cell Ca²⁺ levels is one of the strategies that viruses use to perturb the host cell signaling transduction mechanism in their favor. Evidence has emerged demonstrating that Ca²⁺ is important for the assembly and budding of EBOV in a VP40-dependent manner. The relationship between intracellular Ca²⁺ levels and EBOV matrix protein VP40 function is still unknown. In this work we utilize biophysical techniques to study the role of LPIs and intracellular Ca²⁺ on VP40 dynamics at the plasma membrane and key residues for assembly and budding. This work highlights the sensitivity of slight electrostatic changes on the VP40 surface for assembly and budding and a critical interaction between Ca²⁺ and the VP40 dimer that are important for lipid binding at the plasma membrane.</p>

At the intersection of geography and public health, the field of spatial epidemiology seeks to use the tools of geospatial analysis to answer questions about disease. In this work we explore two areas: the use of geostatistical modeling as an extension of niche modeling, and the use of mobility metrics to augment modeling for epidemic responses. Niche modeling refers to the practice of using statistical methods to relate the underlying spatially distributed environmental variables to an outcome, typically presence or absence of a species. Such work is common in disease ecology, and often focuses on exploring the range of a disease vector or pathogen. The technique also allows one to explore the importance of each underlying regressor, and the effect it has on the outcome. We demonstrate that this concept can be extended, through geostatistical modeling, to explore non-logistic phenomena such as incidence. When combined with weather forecasts, such efforts can even predict incidence of an upcoming season, allowing us to estimate the total number of expected cases, and where we would expect to find them. We demonstrate this in Chapter 2, by forecasting the incidence of melioidosis in Australia given weather forecasts a year prior. We also evaluate the efficacy of this technique and explore the impact of environmental variables such as elevation on melioidosis. But these techniques are not limited to free-living and vector-borne pathogens. We theorize that they can also be applied to diseases that spread exclusively by person-to-person contact. Exploring this allows us to find areas of underreporting, as well as areas with unusual local forcing which might merit further investigation by the health department. We also explore this in Chapter 4, by relating the incidence of hepatitis C in rural Virginia to demographic data. The West African Ebola Outbreak of 2014 demonstrated the need to include mobility in predictive disease modeling. One can no longer assume that neglected tropical diseases will remain contained and immobile, and the assumption of random mixing across large areas is unwise. Our efforts with modeling mobility are twofold. In Chapter 3, we demonstrate the creation of mobility metrics from open source road and river network data. We then demonstrate the usefulness of such data in a meta-population patch model meant to forecast the spread of Ebola in the Democratic Republic of Congo. In Chapter 4, we also demonstrate that mobility data can be used to strengthen outbreak detection via hotspot analysis, and to augment incidence models by factoring in the incidence rates of neighboring areas. These efforts will allow health departments to more accurately forecast incidence, and more readily identify disease hotspots of atypical size and shape. / Doctor of Philosophy / The focus of this work is called “spatial epidemiology”, which combines geography with public health, to answer the where, and why, of disease. This is a growing field, and you’ve likely seen it in the news and media. Have you ever seen a map of the United States turning red in some virus disaster movie? The real thing looks a lot like that. After the Ebola outbreak of 2014, public health agencies wanted to know where the next one might hit. Now that there is another outbreak, we need to ask where and how will it spread? What areas are hardest hit, and how bad is it going to get? We can answer all these questions with spatial epidemiology. Our work adds to two aspects of spatial epidemiology: niche modeling, and mobility. We use niche modeling to determine where we could find certain diseases, usually those that are spread by insects or animals. Consider Lyme disease, you get it from the bite of a tick, and the tick gets it from a white-footed mouse. But both the mice and ticks only live in certain parts of the country. With niche modeling we can determine where those are, and we can also guess at what makes those areas attractive to the mice and ticks. Is it winter harshness, summer temperatures, rainfall, and/or elevation? Is it something else? In Chapter 2, we show that you can extend this idea. Instead of just looking at where the disease is, what if we could guess how many people will get infected? What if we could do so, a year in advance? We show that this can be done, but we need a good idea of what the weather will be like next year. In Chapter 4, we show that you can do the same thing with hepatitis C. Instead of Lyme’s ticks and mice, hepatitis C depends on drug-use, unregulated tattooing, and unsafe sex. And like with Lyme, these things are only found in certain places. Instead of temperature or rainfall, we now need to find areas with drug-problems and poverty. But we can get an idea of this from the Census Bureau, and we can make a map of hepatitis C as easily as we did for Lyme. But hepatitis C spreads person-to-person. So, we need some idea of how people move around the area. This is where mobility comes in. Mobility is important for most public health work, from detecting outbreaks to estimating where the disease will spread next. In Chapter 3, we show how one could create a mobility model for a rural area where few maps exist. We also show how to use that model to guess where the next cases of Ebola will show up. In Chapter 4, we show how you could use mobility to improve outbreak and hotspot detection. We also show how it’s used to help estimate the number of cases in an area. Because that number depends on how many cases are imported from the surrounding areas. And the only way to estimate that is with mobility.

Les vaccins ont été une grande réussite en matière de santé publique au cours des dernières années. Cependant, le développement de vaccins efficaces contre les maladies infectieuses telles que le VIH ou le virus Ebola reste un défi majeur. Cela peut être attribué à notre manque de connaissances approfondies en immunologie et sur le mode d'action de la mémoire immunitaire. Les modèles mathématiques peuvent aider à comprendre les mécanismes de la réponse immunitaire, à quantifier les processus biologiques sous-jacents et à développer des vaccins fondés sur un rationnel scientifique. Nous présentons un modèle mécaniste de la dynamique de la réponse immunitaire humorale après injection d'un vaccin Ebola basé sur des équations différentielles ordinaires. Les paramètres du modèle sont estimés par maximum de vraisemblance dans une approche populationnelle qui permet de quantifier le processus de la réponse immunitaire et ses facteurs de variabilité. En particulier, le schéma vaccinal n'a d'impact que sur la réponse à court terme, alors que des différences significatives entre des sujets de différentes régions géographiques sont observées à plus long terme. Cela pourrait avoir des implications dans la conception des futurs essais cliniques. Ensuite, nous développons un outil numérique basé sur la programmation dynamique pour optimiser des schémas d'injections répétées. En particulier, nous nous intéressons à des patients infectés par le VIH sous traitement mais incapables de reconstruire leur système immunitaire. Des injections répétées d'un produit immunothérapeutique (IL-7) sont envisagées pour améliorer la santé de ces patients. Le processus est modélisé par un modèle de Markov déterministe par morceaux et des résultats récents de la théorie du contrôle impulsionnel permettent de résoudre le problème numériquement à l'aide d'une suite itérative. Nous montrons dans une preuve de concept que cette méthode peut être appliquée à un certain nombre de pseudo-patients. Dans l'ensemble, ces résultats s'intègrent dans un effort de développer des méthodes sophistiquées pour analyser les données d'essais cliniques afin de répondre à des questions cliniques concrètes. / Vaccines have been one of the most successful developments in public health in the last years. However, a major challenge still resides in developing effective vaccines against infectious diseases such as HIV or Ebola virus. This can be attributed to our lack of deep knowledge in immunology and the mode of action of immune memory. Mathematical models can help understanding the mechanisms of the immune response, quantifying the underlying biological processes and eventually developing vaccines based on a solid rationale. First, we present a mechanistic model for the dynamics of the humoral immune response following Ebola vaccine immunizations based on ordinary differential equations. The parameters of the model are estimated by likelihood maximization in a population approach, which allows to quantify the process of the immune response and its factors of variability. In particular, the vaccine regimen is found to impact only the response on a short term, while significant differences between subjects of different geographic locations are found at a longer term. This could have implications in the design of future clinical trials. Then, we develop a numerical tool based on dynamic programming for optimizing schedule of repeated injections. In particular, we focus on HIV-infected patients under treatment but unable to recover their immune system. Repeated injections of an immunotherapeutic product (IL-7) are considered for improving the health of these patients. The process is first by a piecewise deterministic Markov model and recent results of the impulse control theory allow to solve the problem numerically with an iterative sequence. We show in a proof-of-concept that this method can be applied to a number of pseudo-patients. All together, these results are part of an effort to develop sophisticated methods for analyzing data from clinical trials to answer concrete clinical questions.

Cette étude se place dans le contexte des maladies infectieuses émergentes maintenant reconnues comme une menace majeure de la biodiversité. Engendrant un taux de mortalité atteignant 95 %, les épidémies à virus Ebola ayant affecté les populations de gorilles de plaine de l'Ouest (Gorilla gorilla gorilla) conduisirent à la classification de ce taxon comme " En danger critique d'extinction ". Cette étude s'intéresse aux capacités de récupération de ses populations. Grâce à des données uniques d'observation en phase pré- et post-épidémique, nous avons évalué l'impact de l'épidémie sur la structure et la dynamique sociale d'une population et estimé son potentiel de récupération au cours des six ans qui ont suivi. Nos résultats de démographie et de dynamique, couplés à des approches statistique et de modélisation démographique détaillée au niveau des classes d'âge et de sexe, et intégrant l'immigration, nous ont permis de mettre en évidence 1) un impact délétère sur le potentiel reproducteur, du fait de l'organisation sociale du gorille ; 2) les atouts de la flexibilité et de l'organisation sociale dans la récupération de la structure de la population ; 3) le rôle de l'immigration pour la récupération à long-terme des effectifs. La mise en évidence de caractéristiques structurelles typiques d'une population affectée par Ebola nous a permis de montrer qu'une population voisine étudiée était indemne. Ceci met en évidence l'impact hétérogène des épidémies au niveau régional, induisant probablement un certain degré de fragmentation des populations. Ce nouvel élément permet de discuter les hypothèses d'émergence et de propagation du virus, et pose la question de l'impact de la fragmentation de la population sur sa dynamique globale et sur la récupération des populations locales affectées. Nos résultats suggèrent une faible résilience des populations de gorilles de plaine face à Ebola et la menace de ce virus pour la persistance des populations. Cependant, une meilleure connaissance du potentiel de flux d'individus au niveau régional et le développement de modèles démographiques prenant en compte cette dimension permettrait de mieux préciser cette résilience.