The Effects of Different Deployment Strategies of Artemisinin Combination Therapies on Slowing Down the Spread of Antimalarial Drug Resistence: Investigation With Individual-Based Simulations

Despite the success of recent global malaria control efforts, which have halved global malaria mortality since 2000, malaria is still one of the world’s most deadly diseases causing an estimated half a million deaths, mostly among African children, and around a quarter of billion clinical episodes every year as reported in 2014. Drug resistance is one of the most important challenges to malaria elimination. To contain drug resistance, many efforts have been put forth including improvement of surveillance systems and mass treatment in order to stop or slow down the transmission of the resistant strain. To find out whether a population-level treatment strategy can have any benefit in containing drug resistance, mathematical models are an appropriate approach to this problem and individual-based models allow us to have a better understanding of the effect of individual heterogeneities on the outcome. The first part of the thesis is about building and validating an individual based microsimulation. The model is implemented as an individual-based discrete-time event simulation model in C++. The behaviors and the state changes of human individuals are determined by relevant events and mathematical formulas. This integrated model combines components that reproduce the most important features of malaria transmission and epidemiology: the infectiousness of human populations; clinical model of acute illness; heterogeneities in individuals’ age, biting-rate level, drug absorption, drug action, multiple parasite populations, and human immunity. To validate this individual-based model, two types of validation have been done. The model’s parameters were obtained from field or clinical data were used directly in the model. For those parameters that cannot be obtained directly from literature review, sensitivity analysis has been done to find how variation in parameter values affects certain key features of malaria epidemiology. The second part of the thesis focused on the comparison between population-scaled treatment strategies. The results showed that using multiple first-line therapies (MFT) results in a lower number of treatment failures compared to other strategies where a single first-line ACT is recommended. This result is robust to various epidemiological, pharmacological, and evolutionary features of malaria transmission. In addition, including non-ACT therapy in an MFT strategy seems to have a significant benefit in reducing the pressure on artemisinin-resistance evolution, delaying its emergence and slowing its spread. The third part of the thesis focused on individual-level treatment strategies to combat artemisinin resistance. The results showed that lengthening an ACT course or using multiple courses of ACT can reduce the long-term number of treatment failures significantly. The work reported here introduces a novel individual-based simulation that includes drug resistance evolution and the ability to be scaled up to millions number of individuals. The challenge that remains is to evaluate the feasibility of these novel treatment strategies given that they will need to be implemented in the real world of malaria control programs, their operations, human behavior, and economic realities

Predicting the cost-effectiveness of strategies for case management of "plasmodium falciparum" malaria in Sub-Saharan Africa

Malaria is an important cause of death and illness in children and adults, particularly in the tropics. The World Health Organization (WHO) estimated that, worldwide, there were 655,000 malaria deaths in 2010, of which 91% were in Africa, and 216 million cases, of which 91% were due to Plasmodium falciparum (P.falciparum). However, case estimates are particularly uncertain, due to the ambiguous definition of a malaria case and methods used for their quantification. Efficacious interventions against malaria exist, but it is not clear what their full impact will be or how they could be most efficiently implemented. A cornerstone of malaria strategies is case management, which consists primarily of administering drug treatment to cure the disease, and was the focus of this thesis. Currently, the aim of most countries in sub-Saharan Africa is to control malaria and reduce the disease burden by increasing coverage of effective preventive and curative interventions. However, in some places successes in reducing disease burden have lead countries to consider whether and how local interruption of malaria transmission could be achieved and maintained. In these settings, improved surveillance is critical, but it is not clear what it should consist of. It is important to consider the long-term effects of intervention and intervention combinations, such as the dynamic effects on population immunity, which are not captured within the time frame of intervention trials, and their impact in real health systems. Mathematical models can offer guidance in these situations. In 2006, Smith and colleagues presented individual-based stochastic simulation models of the biology and epidemiology of P. falciparum malaria. As part of this project, a model for the case management of malaria was developed which permitted simulation of the dynamic effects of treatment on transmission. For this thesis, these models were extended to low-transmission settings and used to predict the levels of passive case detection and treatment that would be needed to prevent local re-establishment of transmission in different settings. We assessed the uncertainties in model predictions resulting from stochastic variation and from the assumptions in our model formulations. We found that, even at rather low levels of receptivity, case management alone could not reliably prevent re-establishment of P. falciparum malaria transmission in the face of medium to high importation rates. Model assumptions regarding rates of decay of natural immunity resulted in significantly different odds of transmission re-establishment, highlighting the urgent need for research in this area. We also developed a literature-based estimate of the per-person cost of screening an entire population for P.falciparum infection using diagnostic tests. We used this cost estimate along with simulation model outputs to analyse the cost-effectiveness of mass screening and treatment (MSAT) as a burden-reducing intervention, relative to the cost-effectiveness of scaling up case management or insecticide-treated net (ITN) coverage. We found that MSAT may be a cost-effective strategy at medium to high transmission levels and at moderate ITN coverage. This finding is in contrast to the current focus on MSAT as an intervention for low or near-elimination settings. Future analyses comparing the cost-effectiveness of case management with that of preventive interventions should include both disability and deaths averted (expressed in DALYs) as an outcome measure. The analysis also highlighted the need for alternative measures of uncomplicated malaria burden to capture the impact of case management in simulation models of its cost-effectiveness. An approach to do this, using data available in community surveys, is presented in this thesis. Finally, the previous case management model was extended to allow a finer-grained simulation of health systems and a drug action model was integrated to allow simulation of the effects of case management on parasite densities. The development and parameterization of the new case management model, and its potential future uses and limitations, are presented in the last sections of this thesis

Advisory Committee on Immunization Practices (ACIP) summary report : February 22-23, 2012, Atlanta, Georgia)

Publication date from document properties.min-feb12.pdf2012607

Estimating the Heritability of Virulence in HIV

The rate that HIV-infected individuals progress to AIDS and death varies greatly. Viral load taken during the asymptomatic phase of the disease is one of the best-known predictors of HIV progression rate and transmission risk, and is known to be in uenced by both host and environmental factors. However, the role that the virus itself plays in determining the viral load is less clear. Previous studies have attempted to quantify the amount the viral genome in uences viral load, or the heritability of viral load, using transmission pairs and phylogenetic signal in small sample sizes, but have produced highly disparate estimates. E cient and accurate methods to estimate heritability have been utilised by quantitative geneticists for years, but are rarely applied to non-pedigree data. Here, I present a novel application of a population-scale method based in quantitative genetics to estimate the heritability of viral load in HIV using a viral phylogeny. This new phylogenetic method allows the inclusion of more samples than ever previously used, and avoids confounding e ects associated with transmission pair studies. This new method was applied to the two largest HIV subtypes found in the UK, subtypes B and C, using sequences and clinical data from UK-wide HIV databases. For subtype B (n=8,483) and C (n=1,821), I estimated that 5.7% (CI 2.8{8.6%) and 29.7% (CI 14.8{44.7%) of the variance in viral load is determined by the viral genome, respectively. These estimates suggest that viral in uence on viral load varies greatly between subtypes, with subtype C having much larger viral control over viral load than subtype B. I expanded the phylogenetic method to test whether the component of the viral load determined by the virus has changed over time. In subtype B, I foundevidence of a small but signi cant decrease in the viral component of viral load of -0.05 log10 copies/mL/yr. I built a stochastic, individual-based model capable of simulating a realistic HIV epidemic, with heritable viral loads that in uence transmission and disease progression, capable of generating data sets to assess the accuracy of phylogenetic methods. This was successfully used to generate epidemics approximating those in a small African village and a Western `men who have sex with men' community under a variety of conditions. To test the accuracy of the new phylogenetic heritability estimation method, simulated datasets were generated with the heritability of viral load set at values of 30%, 50%, 70%, and 90%. Unfortunately, complications in the heritability equation used prevented full assessment of the new phylogenetic method on the simulated data. Future development of the model will enable simulation of realistic viral loads under varying heritability values, enabling simulation of data sets that can be used to test this and other heritability estimation methods. This new phylogenetic method allows accurate estimation of heritability in large datasets, and has provided valuable insight into the viral in uence on viral load in HIV

Scalable visual analytics over voluminous spatiotemporal data

2018 Fall.Includes bibliographical references.Visualization is a critical part of modern data analytics. This is especially true of interactive and exploratory visual analytics, which encourages speedy discovery of trends, patterns, and connections in data by allowing analysts to rapidly change what data is displayed and how it is displayed. Unfortunately, the explosion of data production in recent years has led to problems of scale as storage, processing, querying, and visualization have struggled to keep pace with data volumes. Visualization of spatiotemporal data pose unique challenges, thanks in part to high-dimensionality in the input feature space, interactions between features, and the production of voluminous, high-resolution outputs. In this dissertation, we address challenges associated with supporting interactive, exploratory visualization of voluminous spatiotemporal datasets and underlying phenomena. This requires the visualization of millions of entities and changes to these entities as the spatiotemporal phenomena unfolds. The rendering and propagation of spatiotemporal phenomena must be both accurate and timely. Key contributions of this dissertation include: 1) the temporal and spatial coupling of spatially localized models to enable the visualization of phenomena at far greater geospatial scales; 2) the ability to directly compare and contrast diverging spatiotemporal outcomes that arise from multiple exploratory "what-if" queries; and 3) the computational framework required to support an interactive user experience in a heavily resource-constrained environment. We additionally provide support for collaborative and competitive exploration with multiple synchronized clients

Parallel and Distributed Computing

The 14 chapters presented in this book cover a wide variety of representative works ranging from hardware design to application development. Particularly, the topics that are addressed are programmable and reconfigurable devices and systems, dependability of GPUs (General Purpose Units), network topologies, cache coherence protocols, resource allocation, scheduling algorithms, peertopeer networks, largescale network simulation, and parallel routines and algorithms. In this way, the articles included in this book constitute an excellent reference for engineers and researchers who have particular interests in each of these topics in parallel and distributed computing

Mining Public Opinion on COVID-19 Vaccines using Unstructured Social Media Data

The emergence of the novel coronavirus (COVID-19), and the necessary separation of populations led to an unprecedented number of new social media users seeking information related to the pandemic. Nowadays, with an estimated 4.5 billion users worldwide, social media data offer an opportunity for near real-time analysis of large bodies of text related to disease outbreaks and vaccination. This study investigated and compared public discourse related to COVID-19 vaccines expressed on two popular social media platforms, Reddit and Twitter. Approximately 9.5 million Tweets and 70 thousand Reddit comments were analyzed from dates January 1, 2020, to March 1, 2022, and analyzed through topic modeling, sentiment analysis, and semantic network analysis. Sentiment analysis through the fine-tuned DistilRoBERTa model revealed that even though Twitter content was overall more negative than content expressed on Reddit, relatively similar changes in sentiment occurred among users of both online platforms. Reversals in sentiment trends typically occurred within relative proximity to events such as vaccine development news, vaccine release, frequent discussion of side-effects, the discovery of new variants, and pandemic fatigue. Topic modeling and semantic network analysis provided insight into how public discourse related to COVID-19 and vaccinations, misinformation, and vaccine hesitancy evolved over 26 months. Though misinformation and mention of conspiracy theories were detected with the analysis, the occurrence of both was less frequent than expected. This work provides a framework that could be scaled and utilized by public health officials to monitor disease outbreaks in near real-time in large communities as well as smaller local groups. Hopefully, the results from this study will help to guide and facilitate the implementation of targeted digital interventions among vaccine-hesitant populations and provide insights to public health officials to inform decision-making and effective policy development

Individual-based modeling and predictive simulation of fungal infection dynamics

The human-pathogenic fungus Aspergillus fumigatus causes life-threatening infections in immunocompromised patients and poses increasing challenges for the modern medicine. A. fumigatus is ubiquitously present and disseminates via small conidia over the air of the athmosphere. Each human inhales several hundreds to thousands of conidia every day. The small size of conidia allows them to pass into the alveoli of the lung, where primary infections with A. fumigatus are typically observed. In alveoli, the interaction between fungi and the innate immune system of the host takes place. This interaction is the core topic of this thesis and covered by mathematical modeling and computer simulations. Since in vivo laboratory studies of A. fumigatus infections under physiological conditions is hard to realize a modular software framework was developed and implemented, which allows for spatio-temporal agent-based modeling and simulation. A to-scale A. fumigatus infection model in a typical human alveolus was developed in order to simulate and analyze the infection scenario under physiological conditions. The process of conidial discovery by alveolar macrophages was modeled and simulated with different migration modes and different parameter configurations. It could be shown that chemotactic migration was required to find the pathogen before the onset of germination. A second model took advantage of evolutionary game theory on graphs. Here, the course of infection was modeled as a consecutive sequence of evolutionary games related to the complement system, alveolar macrophages and polymorphonuclear neutrophilic granulocytes. The results revealed a central immunoregulatory role of alveolar macrophages. In the case of high infectious doses it was found that the host required fully active phagocytes, but in particular a qualitative response of quantitatively sufficient polymorphonuclear neutrophilic granulocytes.Der human-pathogene Schimmelpilz Aspergillus fumigatus verursacht tödliche Infektionen und Erkrankungen vorrangig bei immunsupprimierten Patienten und stellt die moderne Medizin vor zunehmende Herausforderungen. A. fumigatus ist ubiquitär präsent und verbreitet sich über sehr kleine Konidien durch Luftströmungen in der Athmosphäre. Mehrere Hundert bis Tausende dieser Konidien werden täglich durch jeden Menschen eingeatmet. Die geringe Größe der infektiösen Konidien erlauben es dem Pilz bis in die Alveolen der Lunge des Wirtes vorzudringen,in denen eine Primärinfektionen mit A. fumigatus am häufigsten stattfindet. Die Alveolen sind der zentrale Schauplatz der Interaktion zwischen dem Pilz und dem angeborenen Immunsystem, welche Gegenstand dieser Arbeit ist. Diese Interaktion wird mit Hilfe von mathematischen Modellen und Computersimulationen nachgestellt und untersucht, da eine A. fumigatus Infektion im Nasslabor in vivo unter physiologischen Bedingungen nur sehr schwer realisiert werden kann. Als Grundlage für dieses Vorhaben wurde ein modulares Software-Paket entwickelt, welches agentenbasierte Modellierung und entsprechende Simulationen in Raum und Zeit ermöglicht. Ein maßstabsgetreues mathematisches Infektionsmodell in einer typischen menschlichen Alveole wurde entwickelt und die Suchstrategien von Alveolarmakrophagen unter der Berücksichtigung verschiedener Parameter wie Migrationsgeschwindigkeit, dem Vorhandensein von Chemokinen, dessen Diffusion und Chemotaxis untersucht. Es zeigte sich, dass Chemotaxis, notwendig ist, um die Konidie rechtzeitig finden zu können. In einem weiteren Modell, welches auf das Konzept evolutionärer Spieltheorie auf Graphen zurückgegriff, wurde der Infektionsverlauf als aufeinanderfolgende Serie evolutionärer Spiele mit dem Komplementsystem, Alveolarmakrophagen und Neutrophilen nachgestellt. Aus den Simulationsergebnissen konnte eine zentrale immunregulatorische Rolle von Alveolarmakrophagen entnommen werden

Mathematical Modeling of Malaria: Theories of Malaria Elimination

This dissertation describes the development and application of a new mathematical model for simulating the progression of Plasmodium falciparum infections in individuals with no malarial acquired immunity. The model allows for stochastic simulation of asexual and sexual parasitemias as well as the onset of fever and human to mosquito infectivity on a daily time scale. The model components for the asexual and sexual stages were developed elsewhere but are here extended to allow for simulation of the full range of dynamics observed in a subset of malaria therapy patients. As a first application of the model, I calculate the human component of malarial R0, the basic reproductive number. I then compare this value to those from three other models and describe how this quantity can be used to model malaria transmission. The second application of the model incorporates the effects of drug treatment on progression of infection by utilizing modeled pharmacokinetic and pharmacodynamic properties of a variety of antimalarials. I utilize a stage specific proportional killing model for sexual stages, informed from recent in vitro data. The relationship of effect sizes to treatment coverage and type of treatment in both early and late treatment seeking settings is calculated. In the third chapter, I consider the economic and epidemiological ramifications of antimalarial and rapid diagnostic subsidization for malaria control. For the epidemiological modeling I utilize a semi-mechanistic model of the spread of drug resistance parameterized from historical malaria mortality data; for the economic model I consider the effect of rapid diagnostics on the intensive and extensive margins of antibiotics and antimalarials, as well as the benefits to improved targeting of both. I find that rapid diagnostic testing is justified given our baseline assumptions for areas with low proportions of malarious individuals among all treatment-seekers, but that caution is necessary before deployment worldwide. For antimalarial subsidization, we find that this is a cost-effective method for reducing mortality in developing countries, though efforts to delay the onset and slow the spread of resistance are urgently needed

Computing resources sensitive parallelization of neural neworks for large scale diabetes data modelling, diagnosis and prediction

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Diabetes has become one of the most severe deceases due to an increasing number of diabetes patients globally. A large amount of digital data on diabetes has been collected through various channels. How to utilize these data sets to help doctors to make a decision on diagnosis, treatment and prediction of diabetic patients poses many challenges to the research community. The thesis investigates mathematical models with a focus on neural networks for large scale diabetes data modelling and analysis by utilizing modern computing technologies such as grid computing and cloud computing. These computing technologies provide users with an inexpensive way to have access to extensive computing resources over the Internet for solving data and computationally intensive problems. This thesis evaluates the performance of seven representative machine learning techniques in classification of diabetes data and the results show that neural network produces the best accuracy in classification but incurs high overhead in data training. As a result, the thesis develops MRNN, a parallel neural network model based on the MapReduce programming model which has become an enabling technology in support of data intensive applications in the clouds. By partitioning the diabetic data set into a number of equally sized data blocks, the workload in training is distributed among a number of computing nodes for speedup in data training. MRNN is first evaluated in small scale experimental environments using 12 mappers and subsequently is evaluated in large scale simulated environments using up to 1000 mappers. Both the experimental and simulations results have shown the effectiveness of MRNN in classification, and its high scalability in data training. MapReduce does not have a sophisticated job scheduling scheme for heterogonous computing environments in which the computing nodes may have varied computing capabilities. For this purpose, this thesis develops a load balancing scheme based on genetic algorithms with an aim to balance the training workload among heterogeneous computing nodes. The nodes with more computing capacities will receive more MapReduce jobs for execution. Divisible load theory is employed to guide the evolutionary process of the genetic algorithm with an aim to achieve fast convergence. The proposed load balancing scheme is evaluated in large scale simulated MapReduce environments with varied levels of heterogeneity using different sizes of data sets. All the results show that the genetic algorithm based load balancing scheme significantly reduce the makespan in job execution in comparison with the time consumed without load balancing.This work is funded by the EPSRC and China Market Association