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1
Bingham, Adrienna N. "Controlling Infectious Disease: Prevention and Intervention Through Multiscale Models." W&M ScholarWorks, 2019. https://scholarworks.wm.edu/etd/1582642581.
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Controlling infectious disease spread and preventing disease onset are ongoing challenges, especially in the presence of newly emerging diseases. While vaccines have successfully eradicated smallpox and reduced occurrence of many diseases, there still exists challenges such as fear of vaccination, the cost and difficulty of transporting vaccines, and the ability of attenuated viruses to evolve, leading to instances such as vaccine derived poliovirus. Antibiotic resistance due to mistreatment of antibiotics and quickly evolving bacteria contributes to the difficulty of eradicating diseases such as tuberculosis. Additionally, bacteria and fungi are able to produce an extracellular matrix in biofilms that protects them from antibiotics/antifungals. Mathematical models are an effective way of measuring the success of various control measures, allowing for cost savings and efficient implementation of those measures. While many models exist to investigate the dynamics on a human population scale, it is also beneficial to use models on a microbial scale to further capture the biology behind infectious diseases. In this dissertation, we develop mathematical models at several spatial scales to help improve disease control. At the scale of human populations, we develop differential equation models with quarantine control. We investigate how the distribution of exposed and infectious periods affects the control efficacy and suggest when it is important for models to include realistically narrow distributions. At the microbial scale, we use an agent-based stochastic spatial simulation to model the social interactions between two yeast strains in a biofilm. While cheater strains have been proposed as a control strategy to disrupt the harmful cooperative biofilm, some yeast strains cooperate only with other cooperators via kin recognition. We study under what circumstances kin recognition confers the greatest fitness benefit to a cooperative strain. Finally, we look at a multiscale, two-patch model for the dynamics between wild-type (WT) poliovirus and defective interfering particles (DIPs) as they travel between organs. DIPs are non-viable variants of the WT that lack essential elements needed for reproduction, causing them to steal these elements from the WT. We investigate when DIPs can lower the WT population in the host.
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Venkatachalam, Sangeeta. "Modeling Infectious Disease Spread Using Global Stochastic Field Simulation." Thesis, University of North Texas, 2006. https://digital.library.unt.edu/ark:/67531/metadc5335/.
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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.
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McBryde, Emma Sue. "Mathematical and statistical modelling of infectious diseases in hospitals." Queensland University of Technology, 2006. http://eprints.qut.edu.au/16330/.
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Antibiotic resistant pathogens, such as methicillin-resistant Staphylococcus aureus (MRSA), and vancomycin-resistant enterococci (VRE), are an increasing burden on healthcare systems. Hospital acquired infections with these organisms leads to higher morbidity and mortality compared with the sensitive strains of the same species and both VRE and MRSA are on the rise worldwide including in Australian hospitals. Emerging community infectious diseases are also having an impact on hospitals. The Severe Acute Respiratory Syndrome virus (SARS Co-V) was noted for its propensity to spread throughout hospitals, and was contained largely through social distancing interventions including hospital isolation. A detailed understanding of the transmission of these and other emerging pathogens is crucial for their containment. The statistical inference and mathematical models used in this thesis aim to improve understanding of pathogen transmission by estimating the transmission rates of contagions and predicting the impact of interventions. Datasets used for these studies come from the Princess Alexandra Hospital in Brisbane, Australia and Shanxi province, mainland China. Epidemiological data on infection outbreaks are challenging to analyse due to the censored nature of infection transmission events. Most datasets record the time on symptom onset, but the transmission time is not observable. There are many ways of managing censored data, in this study we use Bayesian inference, with transmission times incorporated into the augmented dataset as latent variables. Hospital infection surveillance data is often much less detailed that data collected for epidemiological studies, often consisting of serial incidence or prevalence of patient colonisation with a resistant pathogen without individual patient event histories. Despite the lack of detailed data, transmission characteristics can be inferred from such a dataset using structured HiddenMarkovModels (HMMs). Each new transmission in an epidemic increases the infection pressure on those remaining susceptible, hence infection outbreak data are serially dependent. Statistical methods that assume independence of infection events are misleading and prone to over-estimating the impact of infection control interventions. Structured mathematical models that include transmission pressure are essential. Mathematical models can also give insights into the potential impact of interventions. The complex interaction of different infection control strategies, and their likely impact on transmission can be predicted using mathematical models. This dissertation uses modified or novel mathematical models that are specific to the pathogen and dataset being analysed. The first study estimates MRSA transmission in an Intensive Care Unit, using a structured four compartment model, Bayesian inference and a piecewise hazard methods. The model predicts the impact of interventions, such as changes to staff/patient ratios, ward size and decolonisation. A comparison of results of the stochastic and deterministic model is made and reason for differences given. The second study constructs a Hidden Markov Model to describe longitudinal data on weekly VRE prevalence. Transmission is assumed to be either from patient to patient cross-transmission or sporadic (independent of cross-transmission) and parameters for each mode of acquisition are estimated from the data. The third study develops a new model with a compartment representing an environmental reservoir. Parameters for the model are gathered from literature sources and the implications of the environmental reservoir are explored. The fourth study uses a modified Susceptible-Exposed-Infectious-Removed (SEIR) model to analyse data from a SARS outbreak in Shanxi province, China. Infectivity is determined before and after interventions as well as separately for hospitalised and community symptomatic SARS cases. Model diagnostics including sensitivity analysis, model comparison and bootstrapping are implemented.
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Brown, Victoria. "Modelling healthcare provision for an infectious disease using optimal control." Thesis, University of Bath, 2010. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.518274.
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The development of a vaccine against some strains of the human papillomavirus (HPV) has led to many interesting public health questions [1]. We address some of these questions in the following work. We develop a compartmental mathematical model and examine the effect of waning immunity, vaccinating individuals prior to their becoming sexually active and the current government policy of vaccinating only females [2]. We calculate parameters based on data. We consider both time-dependent and age dependent ODE models and an age- and time-dependent PDE model and compare the results. We find the “effective” R0 value, Re0, for the time-dependent models. We introduce optimal control to both the time-dependent and age-dependent ODE models to assess the most cost-effective method for introducing the vaccine into a population. We find that the duration of protection offered by the vaccine can influence whether it is possible to eradicate infection from the population. We find the critical proportion to vaccinate to eradicate the disease. We see that introducing male vaccination would lead to a greater proportion of individuals to be vaccinated if the disease is to be eradicated. The PDE model shows that the proportion of females vaccinated has a large impact on the proportion of females infected. We show that it is cost-effective to vaccinate males and females. Our results support current government policy for age of vaccination [2]. We conclude that potential waning immunity will impact the success of the vaccine. We broadly support government policy for vaccination but recommend including male vaccination to most cost-effectively eradicate the disease.
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Gaythorpe, Katherine. "The impact of natural disasters on the dynamics of infectious diseases." Thesis, University of Bath, 2016. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.681058.
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Over the course of this thesis we will build and develop a model for the dynamics of an environmentally transmitted disease such as cholera. We will also develop methods to analyse and understand that model. The dynamics of a disease in a heterogeneous developing world city have not yet been fully explored, particularly when those dynamics are affected by a natural disaster. Yet, natural disasters such as floods alter infrastructure and population characteristics in a manner that affects disease transmission. Therefore, we shall address this omission from the literature. We will also develop a novel model analysis framework for 'systems epidemiology' where we combine systems biology techniques with epidemiological modelling.
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Deger, Kristen Alanna. "Modeling the Tradeoff between Transmission and Movement in Infectious Disease Dynamics." The Ohio State University, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=osu1397509244.
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Kubiak, Ruben J. "Insights into the emergence of novel infectious diseases to humans." Thesis, University of Oxford, 2012. http://ora.ox.ac.uk/objects/uuid:0b323e33-f536-4a47-a000-fc9d4d67b49c.
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Novel infectious diseases in humans are of great concern to public health authorities and researchers in epidemiology. Zoonotic pathogens in particular have the potential to cause epidemics without any or little warning. In this thesis, I investigate evolutionary and environmental conditions, and the interactions between both, which facilitate the zoonotic emergence of novel pathogens. I start with a list of the mechanisms and processes which might influence a zoonotic emergence, and identify some unsolved problems. I address these with multiple, theoretical models. First, I use a village-city model with different adaptation scenarios to examine the influence of spatial heterogeneity on the emergence process. I derive general analytical results for the statistical properties of emergence events, including the probability distribution of outbreak sizes. My results suggest that, for typical connection strengths between communities, spatial heterogeneity has only a weak effect on outbreak size distributions, and on the risk of emergence per introduction. Next, I extend the research on environmental conditions by looking at pathogen specialisation in multi-host systems. I derive threshold connectivities for which generalist pathogens, which infect multiple species and might therefore be more dangerous to cross into the human species, can sustain transmission and are not dominated by specialists, which can only cause sustained transmission chains in a single host species, but are able to cause emergences with little warning. My third research chapter is interested in the effect of the loss of biodiversity. I analytically derive expected prevalences for fast growing and slow growing species. If fast growing species tend to perform better in degraded environments, my analytical results suggest that the overall prevalence level of infectious diseases will rise as environments degrade, which facilitates the chance of zoonotic jumps. In my last research chapter, I examine the actual impact of a novel, emerging infectious disease. I use data from the recent `Swine flu' epidemic in England to estimate epidemiological parameters of the infectious agent. My results suggest that the majority of infected cases showed no or only mild symptoms. This reveals that more data than just the estimated number of cases are necessary to fully evaluate the danger of a possible zoonotic, emerging infectious disease. I conclude by discussing my results and the implications which these might have.
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Kwong, Kim-hung, and 鄺劍雄. "Spatio-temporal transmission modelling of an infectious disease: a case study of the 2003 SARS outbreak in Hong Kong." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2010. http://hub.hku.hk/bib/B45693900.
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Gates, Maureen Carolyn. "Controlling endemic disease in cattle populations : current challenges and future opportunities." Thesis, University of Edinburgh, 2014. http://hdl.handle.net/1842/9378.
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The British cattle population hosts a diverse community of endemic pathogens that impact the sustainability of beef and dairy production. As such, there has been a tremendous amount of ongoing research to develop more cost-effective strategies for controlling disease at the industry level. Cattle movements have come under particular scrutiny over the past decade both because of their role in spreading many economically important diseases and because the movements of individual cattle in Great Britain have been explicitly recorded in a centralized electronic database since 1998. Numerous studies have shown that these cattle movements organize into complex networks with key structural and temporal features that influence transmission dynamics. Building on previous work, this thesis used a variety of epidemiological and statistical models to highlight limitations in the current approaches to controlling disease as well as opportunities for reducing endemic disease prevalence through targeted interventions. Empirical disease data from the national bovine tuberculosis (bTB) control programme and from two seroprevalence studies of bovine viral diarrhoea virus (BVDV) in Scottish cattle herds were used in conjunction with movement data from the Cattle Tracing System (CTS) database. Endemic diseases are often challenging to control due to lack of affordable and accurate diagnostic tests as well as the presence of subclinically infected carriers that can easily escape detection. There was evidence that combined issues with the sensitivity and specificity of routine surveillance methods for bTB were contributing to a low level of disease transmission within and between Scottish cattle herds from 2002 to 2009. For BVDV, herds that purchased pregnant beef dams, beef dams with a calf at foot, and open dairy heifers were significantly more likely to be seropositive even though these movements were responsible for only a small number of network contacts. In both cases, targeting the subset of high risk movements with disease specific biosecurity measures may be a more cost-effective use of limited national disease control resources. Other researchers have suggested that control strategies should target multiple diseases simultaneously to reduce trade-offs in resource allocation. Using key indicators of herd reproductive performance derived from the CTS database, it was shown that improving the reproductive management of herds operating below industry standards could reduce endemic disease prevalence by reducing the movements of replacement breeding cattle. A series of network generation algorithms were also developed to study the effects of restricting contact formation based on key demographic and network characteristics of actively trading cattle farms. Strategies that increased network fragmentation either by forcing highly connected farms to form contacts with other highly connected farms or preventing the formation of movements with a high predicted betweenness centrality were found to be particularly effective in limiting disease transmission. For these models to be useful in guiding future policy decisions, it is important to incorporate financial and behavioural drivers of dynamic network change. Following the introduction of pre- and post-movement testing requirements for cattle imported into Scotland from endemic bTB regions, there was a significant decline in cross-border movements, which has likely contributed to the decreasing risk of bTB outbreaks as much as testing itself. Many endemic cattle diseases such as BVDV also spread through local transmission mechanisms, which may undermine the success of disease control programmes that exclusively target cattle movements. There was also evidence that in the absence of national animal legislation, few farmers were likely to adopt biosecurity measures against BVDV. This may be related to the perceived inefficacy of recommendations as well as general unawareness of farm disease status due to the non-specific clinical signs of BVDV outbreaks. Although the CTS database was originally intended for use in slaughter traceback investigations, results from this thesis show how the basic records of births, deaths, and movements can be used to generate valuable insights into the epidemiology of endemic cattle diseases. The findings also emphasize that the management decisions of individual herds can have a substantial impact on industry level transmission dynamics, which offers unique opportunities to develop novel and more cost-effective disease control programmes.
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Gbenga, Abiodun J. "Mathematical modeling and analysis of HIV/AIDS control measures." Thesis, University of the Western Cape, 2012. http://hdl.handle.net/11394/4016.
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>Magister Scientiae - MSc<br>In this thesis, we investigate the HIV/AIDS epidemic in a population which experiences a significant flow of immigrants. We derive and analyse a math-
ematical model that describes the dynamics of HIV infection among the im-
migrant youths and intervention that can minimize or prevent the spread of
the disease in the population. In particular, we are interested in the effects of
public-health education and of parental care.We consider existing models of public-health education in HIV/AIDS epidemi-ology, and provide some new insights on these. In this regard we focus atten-tion on the papers [b] and [c], expanding those researches by adding sensitivity analysis and optimal control problems with their solutions.Our main emphasis will be on the effect of parental care on HIV/AIDS epidemi-ology. In this regard we introduce a new model. Firstly, we analyse the model without parental care and investigate its stability and sensitivity behaviour.We conduct both qualitative and quantitative analyses. It is observed that
in the absence of infected youths, disease-free equilibrium is achievable and is
asymptotically stable. Further, we use optimal control methods to determine
the necessary conditions for the optimality of intervention, and for disease
eradication or control. Using Pontryagin’s Maximum Principle to check the
effects of screening control and parental care on the spread of HIV/AIDS, we
observe that parental care is more effective than screening control. However,
the most efficient control strategy is in fact a combination of parental care and screening control. The results form the central theme of this thesis, and are included in the manuscript [a] which is now being reviewed for publication.
Finally, numerical simulations are performed to illustrate the analytical results.
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Shi, Zhenzhen. "A MARKOV DECISION PROCESS EMBEDDED WITH PREDICTIVE MODELING: A MODELING APPROACH FROM SYSTEM DYNAMICS MATHEMATICAL MODELS, AGENT-BASED MODELS TO A CLINICAL DECISION MAKING." Diss., Kansas State University, 2015. http://hdl.handle.net/2097/20578.
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Doctor of Philosophy<br>Department of Industrial & Manufacturing Systems Engineering<br>David H. Ben-Arieh<br>Chih-Hang Wu<br>Patients who suffer from sepsis or septic shock are of great concern in the healthcare system. Recent data indicate that more than 900,000 severe sepsis or septic shock cases developed in the United States with mortality rates between 20% and 80%. In the United States alone, almost $17 billion is spent each year for the treatment of patients with sepsis. Clinical trials of treatments for sepsis have been extensively studied in the last 30 years, but there is no general agreement of the effectiveness of the proposed treatments for sepsis. Therefore, it is necessary to find accurate and effective tools that can help physicians predict the progression of disease in a patient-specific way, and then provide physicians recommendation on the treatment of sepsis to lower risk for patients dying from sepsis.
The goal of this research is to develop a risk assessment tool and a risk management tool for sepsis. In order to achieve this goal, two system dynamic mathematical models (SDMMs) are initially developed to predict dynamic patterns of sepsis progression in innate immunity and adaptive immunity. The two SDMMs are able to identify key indicators and key processes of inflammatory responses to an infection, and a sepsis progression. Second, an integrated-mathematical-multi-agent-based model (IMMABM) is developed to capture the stochastic nature embedded in the development of inflammatory responses to a sepsis. Unlike existing agent-based models, this agent-based model is enhanced by incorporating developed SDMMs and extensive experimental data. With the risk assessment tools, a Markov decision process (MDP) is proposed, as a risk management tool, to apply to clinical decision-makings on sepsis.
With extensive computational studies, the major contributions of this research are to firstly develop risk assessment tools to identify the risk of sepsis development during the immune system responding to an infection, and secondly propose a decision-making framework to manage the risk of infected individuals dying from sepsis.
The methodology and modeling framework used in this dissertation can be expanded to other disease situations and treatment applications, and have a broad impact to the research area related to computational modeling, biology, medical decision-making, and industrial engineering.
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Abuelezam, Nadia. "Mathematical AIDS Epidemic Model: Preferential Anti-Retroviral Therapy Distribution in Resource Constrained Countries." Scholarship @ Claremont, 2009. http://scholarship.claremont.edu/hmc_theses/67.
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HIV/AIDS is one of the largest health problems the world is currently facing. Even with anti-retroviral therapies (ART), many resource-constrained countries are unable to meet the treatment needs of their infected populations. ART-distribution methods need to be created that prevent the largest number of future HIV infections. We have developed a compartment model that tracks the spread of HIV in multiple two-sex populations over time in the presence of limited treatment. The model has been fit to represent the HIV epidemic in rural and urban areas in Uganda. With the model we examine the spread of HIV among urban and rural regions and observe the effects of preferential treatment to rural areas on the spread of HIV in the country as a whole. We also investigate the effects of preferentially treating women on the spread of HIV. We find that preferentially treating urban women produces the most dramatic effect in reducing the number of infected male and females in rural and urban areas.
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Moreno, Torres Karla Irazema. "The Wildlife-Livestock Interface of Infectious Disease Dynamics: A One Health Approach." The Ohio State University, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=osu1460896947.
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Montazeri, Shahtori Narges. "Quantifying the impact of contact tracing on ebola spreading." Thesis, Kansas State University, 2016. http://hdl.handle.net/2097/34540.
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Master of Science<br>Department of Electrical and Computer Engineering<br>Faryad Darabi Sahneh<br>Recent experience of Ebola outbreak of 2014 highlighted the importance of immediate response to impede Ebola transmission at its very early stage. To this aim, efficient and effective allocation of limited resources is crucial. Among standard interventions is the practice of following up with physical contacts of individuals diagnosed with Ebola virus disease -- known as contact tracing. In an effort to objectively understand the effect of possible contact tracing protocols, we explicitly develop a model of Ebola transmission incorporating contact tracing. Our modeling framework has several features to suit early–stage Ebola transmission: 1) the network model is patient–centric because when number of infected cases are small only the myopic networks of infected individuals matter and the rest of possible social contacts are irrelevant, 2) the Ebola disease model is individual–based and stochastic because at the early stages of spread, random fluctuations are significant and must be captured appropriately, 3) the contact tracing model is parameterizable to analyze the impact of critical aspects of contact tracing protocols.
Notably, we propose an activity driven network approach to contact tracing, and develop a Monte-Carlo method to compute the basic reproductive number of the disease spread in different scenarios. Exhaustive simulation experiments suggest that while contact tracing is important in stopping the Ebola spread, it does not need to be done too urgently. This result is due to rather long incubation period of Ebola disease infection. However, immediate hospitalization of infected cases is crucial and requires the most attention and resource allocation.
Moreover, to investigate the impact of mitigation strategies in the 2014 Ebola outbreak, we consider reported data in Guinea, one the three West Africa countries that had experienced the Ebola virus disease outbreak. We formulate a multivariate sequential Monte Carlo filter that utilizes mechanistic models for Ebola virus propagation to simultaneously estimate the disease progression states and the model parameters according to reported incidence data streams. This method has the advantage of performing the inference online as the new data becomes available and estimating the evolution of the basic reproductive ratio R₀(t) throughout the Ebola outbreak. Our analysis identifies a peak in the basic reproductive ratio close to the time of Ebola cases reports in Europe and the USA.
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Darbon, Alexandre. "Épidémiologie sur réseau pour l'évaluation des risques dans la prévention et le contrôle des infections Network-based assessment of the vulnerability of Italian regions to bovine brucellosis Disease persistence on temporal contact networks accounting for heterogeneous infectious periods." Thesis, Sorbonne université, 2019. http://www.theses.fr/2019SORUS077.
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L’objectif de ma thèse est de proposer des solutions contre la propagation des maladies infectieuses dans des cas précis, en tenant compte de l'évolution des contacts entre les hôtes. Ce travail porte en particulier sur la détermination du seuil épidémique, un indicateur clé du risque épidémique. Il exploite et étend un formalisme mathématique issu de la théorie des réseaux, qui permet de déterminer le seuil épidémique dans des situations réelles, pour en dégager des mesures de santé publique. Un premier projet met en lumière des facteurs à l'origine de la persistance de la brucellose bovine en Italie en dépit des mesures d'éradication en place. L'approche théorique permet de calculer le seuil épidémique dans chaque région du pays à l'aide de données exhaustives sur les déplacements de bovins entre les exploitations italiennes sur plusieurs années, ainsi que des relevés datés de flambées épidémiques. Est ensuite présentée une extension du formalisme qui prend en compte différentes durées moyennes d’infection dans le calcul du seuil épidémique. Ce travail montre dans différents contextes épidémiologiques comment l’hypothèse classique selon laquelle la durée moyenne d’infection est homogène peut biaiser l’estimation du risque épidémique. Cette méthode permet également d'identifier les hôtes d'une population qui sont principalement responsables du risque épidémique global<br>My doctoral thesis aims to propose solutions against the spread of infectious diseases in specific contexts, taking into account how host contacts evolve in time using a temporal network representation. It focuses on the determination of the epidemic threshold, a key indicator of the epidemic risk. By leveraging and extending a mathematical formalism from network theory, this work enables the computation of the epidemic threshold in real situations in order to identify public health measures. A first project addresses the persistence of bovine brucellosis in Italy despite the existing eradication measures. Using comprehensive data on cattle movements between Italian farms over several years, as well as time-stamped outbreak records, the epidemic threshold computation in each region of the country provides information on regions vulnerability and proposes factors that may explain disease persistence. An extension of the formalism is then presented, including heterogeneous average infectious periods in the epidemic threshold computation. This work shows in different epidemiological contexts how the classical assumption that the average infectious period is the same for all hosts in a population may bias epidemic risk assessments. This method also identifies the hosts in a population that are primarily responsible for the global epidemic risk
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Abdel-Moneim, Islam Ahmed. "Mathematical modelling and computer simulation of the spread of infectious diseases." Thesis, University of Strathclyde, 2002. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.248332.
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Ejigu, Amsalework Ayele. "Mathematical modelling of HIV/AIDS transmission under treatment structured by age of infection." Thesis, Stellenbosch : University of Stellenbosch, 2011. http://hdl.handle.net/10019.1/6628.
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Thesis (MSc (Mathematical Sciences))--University of Stellenbosch, 2011.<br>Includes bibliography.<br>ENGLISH ABSTRACT: This thesis takes into account the different levels of infectiousness of the human immunodeficiency
virus (HIV) infected individuals throughout their period of infection. Infectiousness
depends on the time since infection. It is high shortly after the infection occurs and
then much lower for several years, and thereafter a higher plateau is reached before the acquired
immunodeficiency syndrome (AIDS) phase sets in. In line with this, we formulated
a mathematical model which is structured according to the age of infection. To understand
the dynamics of the disease, we first discuss and analyse a simple model in which the age
of infection is not considered, but progression of the HIV-AIDS transmission is taken into
consideration by introducing three stages of infection. Analysis of these models tells us
that the disease can be eradicated from the population only if on average one infected
individual infects less than one person in his or her infectious period, otherwise the disease
persists. To investigate the reduction of the number of infections caused by a single infectious
individual to less than one, we introduce different treatment strategies for a model
which depends on the age of infection, and we analyse it numerically. Current strategies
amount to introducing treatment only at a late stage of infection when the infected individual
has already lived through most of the infectious period. From our numerical results,
this strategy does not result in eradication of the disease, even though it does reduce the
burden for the individual. To eradicate the disease from the population, everyone would
need to be HIV tested regularly and undergo immediate treatment if found positive.<br>AFRIKAANSE OPSOMMING: Hierdie tesis hou rekening met die verskillende aansteeklikheidsvlakke van die menslike
immuniteitsgebreksvirus (MIV) deur besmette individue gedurende hulle aansteeklikheidstydperk.
Die graad van aansteeklikheid hang af van die tydperk sedert infeksie. Dit is
hoog kort nadat die infeksie plaasvind en daarna heelwat laer vir etlike jare, en dan volg
n hoer plato voordat uiteindelik die Verworwe-Immuniteitsgebreksindroom (VIGS) fase
intree. In ooreenstemming hiermee, formuleer ons n wiskundige model van MIV-VIGSoordrag
met n struktureer waarin die tydperk sedert infeksie bevat is. Om die dinamika
van die siekte te verstaan, bespreek en analiseer ons eers n eenvoudige model sonder inagneming
van die tydperk sedert infeksie, terwyl die progressie van MIV-VIGS-oordrag egter
wel in ag geneem word deur die beskouing van drie stadiums van infeksie. Analise van die
modelle wys dat die siekte in die bevolking slegs uitgeroei kan word as elke besmette mens
gemiddeld minder as een ander individu aansteek gedurende die tydperk waarin hy of sy
self besmet is, anders sal die siekte voortduur. Vir die ondersoek oor hoe om die aantal
infeksies per besmette individu tot onder die waarde van een te verlaag, beskou ons verskeie
behandelingsstrategiee binne die model, wat afhang van die tydperk sedert infeksie, en ondersoek
hulle numeries. Die huidige behandelingstrategiee kom neer op behandeling slegs
gedurende die laat sta- dium van infeksie, wanneer die besmette individu reeds die grootste
deel van die aansteeklikheidsperiode deurleef het. Ons numeriese resultate toon dat hierdie
strategie nie lei tot uitroeiing van die siekte nie, alhoewel dit wel die las van die siekte vir
die individu verminder. Om die siekte binne die bevolking uit te roei, sou elkeen gereeld
vir MIV getoets moes word en indien positief gevind, dadelik met behandeling moes begin.
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Majumder, Maimuna S. (Maimuna Shahnaz). "Modeling transmission heterogeneity for infectious disease outbreaks." Thesis, Massachusetts Institute of Technology, 2018. http://hdl.handle.net/1721.1/120885.
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Thesis: Ph. D. in Engineering Systems, Massachusetts Institute of Technology, School of Engineering, Institute for Data, Systems, and Society, 2018.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references.<br>The transmissibility of a given infectious disease is often described by its basic reproduction number (Ro) - namely, the average number of secondary infections caused by an index case in a fully susceptible population. Typical approaches to modeling transmission dynamics associated with infectious disease outbreaks frequently use Ro to produce deterministic case count projections, in effect treating the affected population as homogeneous (i.e. as if every individual in the population interest has an equal likelihood of passing on the infection of interest). As a result, such approaches often fail to effectively capture transmission dynamics during real-world outbreaks in heterogeneous populations. Here, we use analytical and simulation methods to show that the treatment of Ro as the mean of a random variable (thus permitting the estimation of non-deterministic case count projections) allows us to better assess outbreak trajectory and likelihood of disease propagation in non-homogeneous populations (Chapter 2). We then empirically investigate predictors of in-population transmission heterogeneity (i.e. the fact that some individuals in a given population are more likely than others to pass on the infection of interest) within the context of Middle East Respiratory Syndrome in South Korea using a combination of statistical- and review-driven approaches (Chapter 3). Then, in Chapter 4, we explore how in-population transmission heterogeneity can be used to our advantage through the deployment of risk-informed interventions (i.e. in which individuals who are more likely to pass on the infection of interest are exclusively targeted to receive the intervention) during infectious disease outbreaks. More specifically, we use the analytical and simulation methods first introduced in Chapter 2 - paired with inpopulation transmission heterogeneity data from Chapter 3 - to compare the utility of a variance-informed deployment scheme against a traditional, uniform deployment scheme (i.e. in which every individual has an equal likelihood of receiving the intervention). Finally, building off of our findings in Chapters 2, 3, and 4, we recommend four interrelated policies in Chapter 5 that aim to (1) normalize the treatment and reporting of Ro as the mean of a random variable and (2) improve access to the data required to sufficiently capture population heterogeneity when modeling disease propagation.<br>by Maimuna Shahnaz Majumder.<br>Ph. D. in Engineering Systems
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Ssebuliba, Doreen. "Mathematical modelling of the effectiveness of two training interventions on infectious diseases in Uganda." Thesis, Stellenbosch : Stellenbosch University, 2013. http://hdl.handle.net/10019.1/85637.
Full textAbstract:
Thesis (PhD)--Stellenbosch University, 2013.<br>ENGLISH ABSTRACT: Nurses, midwives and clinical officers referred to as Mid-level Practioners (MLPs) play
an important role in the health care system especially in rural Africa. With particular
reference to rural Uganda, due to the large shortage of doctors, MLPs handle most of
the duties usually meant for doctors, at health centre IV(s). From 2009 to 2011, two
training interventions of MLPs were performed at 36 sites in Uganda by the Integrated
Infectious Disease Capacity Building Evaluation (IDCAP). The two interventions were:
Integrated Management of Infectious Diseases (IMID) and On-site Support Services (OSS)
which aimed at improving MLPs’ case management for four diseases: HIV, TB, pneumonia
and malaria. In this thesis, we have developed three mathematical models to investigate
the effect of the two training interventions on these infectious diseases. All the models
are formulated using systems of ordinary differential equations which are structured in
three age groups: [0, 5), [5, 14) and [14, 50). We explored the effect of the two training
interventions in the context of malaria-pneumonia, HIV-TB co-infections and the four
diseases together. Our analysis shows that: i) For malaria-pneumonia, both IMID and
the combination of IMID and OSS reduce the number of cases, deaths and prevalence of
disease but have no effect on the incident episodes of disease. ii) Results from the HIVTB
model propose that HIV and TB testing are important steps in quality of health care
and are capable of offsetting slightly negative effects of reduction in ART enrollment and
provision of treatment. iii) The HIV-TB-malaria-pneumonia (HTMP) model concurs with
the results of the first two models and its results demonstrate that high coverage levels
of the training interventions increase the positive effects that the interventions have on
mortality and morbidity. Overall, our results suggest that training of MLPs is much more
effective for the short term duration diseases such as malaria and pneumonia, where the
baseline values for most of the performance indicators are ≥ 0.6, but not so much for
long term duration diseases such as HIV and TB, whose baseline values for most of the
performance indicators are < 0.6. The results further highlight that problems such as case
detection and drug stock-outs need to be addressed in order for training to have substantial
impact, especially in instances where the performance indicator proportions are low.<br>AFRIKAANSE OPSOMMING: Verpleegsters, vroedvroue en kliniese beamptes wat gesamentlik na verwys word as midvlak
praktisyns (MVPs) , speel n belangrike rol in die gesondheidsorg sisteem, veral in
landelike dele van Afrika. Met spesifieke verwysing na gesondheid sentrums in Uganda,
waar daar te min dokters is, hanteer MVPs die meeste van die pligte wat eintlik deur
dokters verrig moet word. Vanaf 2009 tot 2011 is twee opleidingsprogramme vir MVPs by
36 fasiliteite in Uganda deur die Integrated Infectious Disease Capacity Building Evaluation
(IDCAP) organisasie aangebied. Die twee programme staan bekend as: Integrated
Management of Infectious Diseases (IMID) and On-site Support Services (OSS). Beide die
programme stel ten doel om die MVPs se pasint bestuur vir die siektes MIV, tuberkulose
(TB), longontsteking en malaria te verbeter. Drie wiskundige modelle word in hierdie tesis
ontwikkel om die effek van die opleidingsprogramme op hierdie oordraagbare siektes te
ondersoek. Al die modelle word geformuleer deur gebruik te maak van stelsels van gewone
differensiaal vergelykings wat gestruktureer is in drie ouderdomsgroepe: [0, 5), [5, 14) en
[14, 50). Die effek van die opleidings programme word in die konteks van longontstekingmalaria
mede-infeksie, MIV- TB mede-infeksie en al vier siektes gelyk, ondersoek. Die
analise wys dat: i) Vir longontsteking-malaria mede-infeksie het beide IMID en die kombinasie
van IMID en OSS die aantal siekte-gevalle, sterftes en die prevalensie van die siektes
verminder, maar het geen effek op die insidensie van siekte-gevalle nie. ii) Resultate van
die MIV-TB model dui aan dat MIV en TB toetsing n belangrike aspek van die gehalte
van sorg is en dat dit die effense negatiewe effek van die afname in ART inskrywing en
voorsiening van behandeling, teenstaan. iii) Die MIV-TB-longontsteking-malaria model
(HTMP) stem ooreen met die resultate van die bogenoemde twee modelle en demonstreer
dat ho dekking van die opleidingsprogramme die positiewe effek van die programme op
mortaliteit en morbiditeit verhoog. In geheel stel die resultate van hierdie studie voor
dat die opleiding van MVPs baie meer effektief is vir die korttermyn siektes soos malaria
en longontsteking waarvoor die meeste van die beginwaardes van die prestasie-aanwysers
≥ 0.6 is, maar nie soveel vir lang-termyn siektes soos MIV en TB waarvoor die meeste
van die beginwaarde van die prestasie-aanwysers < 0.6 is. Die resultate dui verder aan dat
opleiding nie voldoende is wanneer die prestasie-aanwysers < 0.6 is nie en dat probleme
soos die opsporing van siekte-gevalle en n gebrek aan medisyne by die klinieke aangespreek
moet word vir opleiding om aansienlike impak te hê.
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Korobeinikov, Andrei. "Stability and bifurcation of deterministic infectious disease models." Thesis, University of Auckland, 2001. http://wwwlib.umi.com/dissertations/fullcit/3015611.
Full textAbstract:
Autonomous deterministic epidemiological models are known to be asymptotically stable. Asymptotic stability of these models contradicts observations. In this thesis we consider some factors which were suggested as able to destabilise the system. We consider discrete-time and continuous-time autonomous epidemiological models. We try to keep our models as simple as possible and investigate the impact of different factors on the system behaviour. Global methods of dynamical systems theory, especially the theory of bifurcations and the direct Lyapunov method are the main tools of our analysis. Lyapunov functions for a range of classical epidemiological models are introduced. The direct Lyapunov method allows us to establish their boundedness and asymptotic stability. It also helps investigate the impact of such factors as susceptibles' mortality, horizontal and vertical transmission and immunity failure on the global behaviour of the system. The Lyapunov functions appear to be useful for more complicated epidemiological models as well. The impact of mass vaccination on the system is also considered. The discrete-time model introduced here enables us to solve a practical problem-to estimate the rate of immunity failure for pertussis in New Zealand. It has been suggested by a number of authors that a non-linear dependence of disease transmission on the numbers of infectives and susceptibles can reverse the stability of the system. However it is shown in this thesis that under biologically plausible constraints the non-linear transmission is unable to destabilise the system. The main constraint is a condition that disease transmission must be a concave function with respect to the number of infectives. This result is valid for both the discrete-time and the continuous-time models. We also consider the impact of mortality associated with a disease. This factor has never before been considered systematically. We indicate mechanisms through which the disease-induced mortality can affect the system and show that the disease-induced mortality is a destabilising factor and is able to reverse the system stability. However the critical level of mortality which is necessary to reverse the system stability exceeds the mortality expectation for the majority of human infections. Nevertheless the disease-induced mortality is an important factor for understanding animal diseases. It appears that in the case of autonomous systems there is no single factor able to cause the recurrent outbreaks of epidemics of such magnitudes as have been observed. It is most likely that in reality they are caused by a combination of factors.<br>Subscription resource available via Digital Dissertations
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Evans, Jane A. (Jane Amanda). "Modeling social response to the spread of an infectious disease." Thesis, Massachusetts Institute of Technology, 2012. http://hdl.handle.net/1721.1/72647.
Full textAbstract:
Thesis (S.M.)--Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2012.<br>This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.<br>Cataloged from student-submitted PDF version of thesis.<br>Includes bibliographical references (p. 85-88).<br>With the globalization of culture and economic trade, it is increasingly important not only to detect outbreaks of infectious disease early, but also to anticipate the social response to the disease. In this thesis, we use social network analysis and data mining methods to model negative social response (NSR), where a society demonstrates strain associated with a disease. Specifically, we apply real world biosurveillance data on over 11,000 initial events to: 1) describe how negative social response spreads within an outbreak, and 2) analytically predict negative social response to an outbreak. In the first approach, we developed a meta-model that describes the interrelated spread of disease and NSR over a network. This model is based on both a susceptible-infective- recovered (SIR) epidemiology model and a social influence model. It accurately captured the collective behavior of a complex epidemic, providing insights on the volatility of social response. In the second approach, we introduced a multi-step joint methodology to improve the detection and prediction of rare NSR events. The methodology significantly reduced the incidence of false positives over a more conventional supervised learning model. We found that social response to the spread of an infectious disease is predictable, despite the seemingly random occurrence of these events. Together, both approaches offer a framework for expanding a society's critical biosurveillance capability.<br>by Jane A. Evans.<br>S.M.
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Menzies, Nicolas Alan. "Mathematical Modeling to Evaluate Disease Control Policy." Thesis, Harvard University, 2014. http://dissertations.umi.com/gsas.harvard:11356.
Full textAbstract:
In this dissertation I assessed three distinct policy questions: the implications of introducing a new tuberculosis diagnostic in southern Africa, the potential value of research related to HIV treatment policy in South Africa, and the causal effect of state cigarette taxes imposed between 1996 and 2013 on health outcomes in the United States.
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Ghosh, Saurav. "News Analytics for Global Infectious Disease Surveillance." Diss., Virginia Tech, 2017. http://hdl.handle.net/10919/80574.
Full textAbstract:
Traditional disease surveillance can be augmented with a wide variety of open sources, such
as online news media, twitter, blogs, and web search records. Rapidly increasing volumes of
these open sources are proving to be extremely valuable resources in helping analyze, detect,
and forecast outbreaks of infectious diseases, especially new diseases or diseases spreading
to new regions. However, these sources are in general unstructured (noisy) and construction
of surveillance tools ranging from real-time disease outbreak monitoring to construction of
epidemiological line lists involves considerable human supervision. Intelligent modeling of
such sources using text mining methods such as, topic models, deep learning and dependency
parsing can lead to automated generation of the mentioned surveillance tools. Moreover, realtime
global availability of these open sources from web-based bio-surveillance systems, such
as HealthMap and WHO Disease Outbreak News (DONs) can aid in development of generic
tools which will be applicable to a wide range of diseases (rare, endemic and emerging) across
different regions of the world.
In this dissertation, we explore various methods of using internet news reports to develop
generic surveillance tools which can supplement traditional surveillance systems and aid in
early detection of outbreaks. We primarily investigate three major problems related to infectious
disease surveillance as follows. (i) Can trends in online news reporting monitor and
possibly estimate infectious disease outbreaks? We introduce approaches that use temporal
topic models over HealthMap corpus for detecting rare and endemic disease topics as well as
capturing temporal trends (seasonality, abrupt peaks) for each disease topic. The discovery
of temporal topic trends is followed by time-series regression techniques to estimate future
disease incidence. (ii) In the second problem, we seek to automate the creation of epidemiological
line lists for emerging diseases from WHO DONs in a near real-time setting. For
this purpose, we formulate Guided Epidemiological Line List (GELL), an approach that
combines neural word embeddings with information extracted from dependency parse-trees
at the sentence level to extract line list features. (iii) Finally, for the third problem, we
aim to characterize diseases automatically from HealthMap corpus using a disease-specific
word embedding model which were subsequently evaluated against human curated ones for
accuracies.<br>Ph. D.
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Lo, Monique (Monique Chun-Ying) 1978. "Modeling and study of infectious disease : stochastic modeling for antibiotic resistance and treatment strategies." Thesis, Massachusetts Institute of Technology, 2001. http://hdl.handle.net/1721.1/68377.
Full textAbstract:
Thesis (M.C.P.)--Massachusetts Institute of Technology, Dept. of Urban Studies and Planning, 2001.<br>Includes bibliographical references (leaf 46).<br>Antibiotic-resistant bacteria pose a serious threat to immuno-compromised individuals in Intensive Care Units (ICU). This study examines several cycling treatments (7,14,30,60,120,240-day cycle) and random fraction treatment (50-50,60-40,80-20,100-0) strategies in ICU and finds that no single strategy will outperform all others. Human, hospital and pathogen conditions such as admission/departure rate, transmission rate, drug application rate, and incoming patients' characteristics influence the selection of the optimal treatment strategy. Random fraction treatment is generally favored when admission/departure rate is large. Cycling treatment is generally favored when admission/departure rate is small. When transmission rates are high, longer cycle period are preferred. When transmission rates are low, random fraction treatments are preferred. For cycling treatments, longer cycle periods is associated with lower drug application rates whereas shorter cycle periods are associated with larger drug application rates.Antibiotic-resistant bacteria pose a serious threat to immuno-compromised individuals in Intensive Care Units (ICU). This study examines several cycling treatments (7,14,30,60,120,240-day cycle) and random fraction treatment (50-50,60-40,80-20,100-0) strategies in ICU and finds that no single strategy will outperform all others. Human, hospital and pathogen conditions such as admission/departure rate, transmission rate, drug application rate, and incoming patients' characteristics influence the selection of the optimal treatment strategy. Random fraction treatment is generally favored when admission/departure rate is large. Cycling treatment is generally favored when admission/departure rate is small. When transmission rates are high, longer cycle period are preferred. When transmission rates are low, random fraction treatments are preferred. For cycling treatments, longer cycle periods is associated with lower drug application rates whereas shorter cycle periods are associated with larger drug application rates.<br>by Monique Lo.<br>M.C.P.
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Rivers, Caitlin. "Modeling Emerging Infectious Diseases for Public Health Decision Support." Diss., Virginia Tech, 2015. http://hdl.handle.net/10919/52023.
Full textAbstract:
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.<br>Ph. D.
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Njagarah, Hatson John Boscoh. "Modelling water-borne infections : the impact of hygiene, metapopulation movements and the biological control of cholera." Thesis, Stellenbosch : Stellenbosch University, 2014. http://hdl.handle.net/10019.1/95972.
Full textAbstract:
Thesis (PhD)--Stellenbosch University, 2014.<br>ENGLISH ABSTRACT: Water-borne infections have been a menace in many countries around the globe, claiming
millions of lives. Cholera in particular has spread to all continents and now on its seventh
epidemic. Although control measures have been continually developed through sanitation,
vaccination and rehydration, the infection still devastates populations whenever there is an
outbreak. In this research work, mathematical models for cholera transmission dynamics
with focus on the impact of sanitation and hygiene, metapopulation spread, optimal control
and biological control using a bacteriophage specific for pathogenic Vibrio cholerae are
constructed and analysed. Vital analyses for the models are precisely given as well as numerical
results depicting long term behaviour and the evolution of populations over time.
The results of our analysis indicate that; improved sanitation and hand-hygiene are vital in
reducing cholera infections; the spread of disease across metapopulations characterised by
exchange of individuals and no cross community infection is associated with synchronous
fluctuation of populations in both adjacent communities; during control of cholera, the control
measures/efforts ought to be optimal especially at the beginning of the epidemic where
the outbreak is often explosive in nature; and biological control if well implemented would
avert many potential infections by lowering the concentration of pathogenic vibrios in the
aquatic environment to values lower than the infectious dose.<br>AFRIKAANSE OPSOMMING: Water-infeksies is ’n bedreiging in baie lande regoor die wêreld en eis miljoene lewens.
Cholera in die besonder, het op sy sewende epidemie na alle kontinente versprei. Hoewel
beheermaatreëls voortdurend ontwikkel word deur middel van higiëne, inentings en rehidrasie,
vernietig die infeksie steeds bevolkings wanneer daar ’n uitbraak voorkom. In
hierdie navorsingswerk, word wiskundige modelle vir cholera-oordrag dinamika met die
fokus op die impak van higiëne, metabevolking verspreiding, optimale beheer en biologiese
beheer met behulp van ’n bakteriofaag spesifiek vir patogene Vibrio cholerae gebou en
ontleed. Noodsaaklike ontledings vir die modelle is gegee sowel as numeriese resultate wat
die langtermyn gedrag uitbeeld en die ontwikkeling van die bevolking oor tyd. Die resultate
van ons ontleding dui daarop dat; verbeterde higiëne is noodsaaklik in die vermindering
van cholera infeksies; die verspreiding van die siekte oor metapopulaties gekenmerk deur
die uitruil van individue en geen kruis gemeenskap infeksie wat verband houmet sinchrone
skommeling van bevolkings in beide aangrensende gemeenskappe; tydens die beheer van
cholera,behoort die beheermaatreëls/pogings optimaal te wees veral aan die begin van die
epidemie waar die uitbreking dikwels plofbaar in die natuur is; en biologiese beheer, indien
dit goed geïmplementeer word, kan baie potensiële infeksies voorkom deur ’n vermindering
in die konsentrasie van patogene vibrio in die water tot waardes laer as die aansteeklike
dosis.
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Thompson, Brett Morinaga. "Development, Implementation, and Analysis of a Contact Model for an Infectious Disease." Thesis, University of North Texas, 2009. https://digital.library.unt.edu/ark:/67531/metadc9824/.
Full textAbstract:
With a growing concern of an infectious diseases spreading in a population, epidemiology is becoming more important for the future of public health. In the past epidemiologist used existing data of an outbreak to help them determine how an infectious disease might spread in the future. Now with computational models, they able to analysis data produced by these models to help with prevention and intervention plans. This paper looks at the design, implementation, and analysis of a computational model based on the interactions of the population between individuals. The design of the working contact model looks closely at the SEIR model used as the foundation and the two timelines of a disease. The implementation of the contact model is reviewed while looking closely at data structures. The analysis of the experiments provide evidence this contact model can be used to help epidemiologist study the spread of an infectious disease based on the contact rate of individuals.
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Ning, Yao, and 宁耀. "The use of stochastic models of infectious disease transmission for public health: schistosomiasis japonica." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2010. http://hub.hku.hk/bib/B4553097X.
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Surujon, Defne. "Computational approaches in infectious disease research: Towards improved diagnostic methods." Thesis, Boston College, 2020. http://hdl.handle.net/2345/bc-ir:109089.
Full textAbstract:
Thesis advisor: Kenneth Williams<br>Due to overuse and misuse of antibiotics, the global threat of antibiotic resistance is a growing crisis. Three critical issues surrounding antibiotic resistance are the lack of rapid testing, treatment failure, and evolution of resistance. However, with new technology facilitating data collection and powerful statistical learning advances, our understanding of the bacterial stress response to antibiotics is rapidly expanding. With a recent influx of omics data, it has become possible to develop powerful computational methods that make the best use of growing systems-level datasets. In this work, I present several such approaches that address the three challenges around resistance. While this body of work was motivated by the antibiotic resistance crisis, the approaches presented here favor generalization, that is, applicability beyond just one context. First, I present ShinyOmics, a web-based application that allow visualization, sharing, exploration and comparison of systems-level data. An overview of transcriptomics data in the bacterial pathogen Streptococcus pneumoniae led to the hypothesis that stress-susceptible strains have more chaotic gene expression patterns than stress-resistant ones. This hypothesis was supported by data from multiple strains, species, antibiotics and non-antibiotic stress factors, leading to the development of a transcriptomic entropy based, general predictor for bacterial fitness. I show the potential utility of this predictor in predicting antibiotic susceptibility phenotype, and drug minimum inhibitory concentrations, which can be applied to bacterial isolates from patients in the near future. Predictors for antibiotic susceptibility are of great value when there is large phenotypic variability across isolates from the same species. Phenotypic variability is accompanied by genomic diversity harbored within a species. I address the genomic diversity by developing BFClust, a software package that for the first time enables pan-genome analysis with confidence scores. Using pan-genome level information, I then develop predictors of essential genes unique to certain strains and predictors for genes that acquire adaptive mutations under prolonged stress exposure. Genes that are essential offer attractive drug targets, and those that are essential only in certain strains would make great targets for very narrow-spectrum antibiotics, potentially leading the way to personalized therapies in infectious disease. Finally, the prediction of adaptive outcome can lead to predictions of future cross-resistance or collateral sensitivities. Overall, this body of work exemplifies how computational methods can complement the increasingly rapid data generation in the lab, and pave the way to the development of more effective antibiotic stewardship practices<br>Thesis (PhD) — Boston College, 2020<br>Submitted to: Boston College. Graduate School of Arts and Sciences<br>Discipline: Biology
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Roberts, Hannah E. "Modelling HIV dynamics and evolution : prospects for viral control." Thesis, University of Oxford, 2016. https://ora.ox.ac.uk/objects/uuid:1e2c153f-bd52-4da2-a1d2-47008687fd09.
Full textAbstract:
The human immunodeficiency virus (HIV) epidemic is far from over. Antiretroviral therapy (ART) is effective at suppressing viral replication within a patient but it must be taken daily and is life-long. Therefore, the development of a therapy that could induce drug-free remission or constitute a functional cure is a key focus of HIV research. In this thesis I explore three mechanisms which could lead to more individuals being able to control their viraemia in the absence of ART: (1) T-cell immunity, (2) early initiation of ART, and (3) viral evolution. Firstly, a strong HIV-specific T-cell response has been linked to rare cases of spontaneous viral control, but the extent to which this arm of the immune response contributes to viral control is debated. Several types of data are used to answer this question, including the rate at which the virus evolves to escape the CD8+ T-cell response. I study the frequency of incident immune escape in the largest cohort used for this purpose to date. Secondly, some patients, with characteristics dissimilar to spontaneous HIV controllers, are able to control the virus for years after the interruption of ART that was initiated early in infection. I use mathematical models to investigate a new hypothesis for the differing outcomes of early- and late- initiated ART. Thirdly, since HIV is a relatively new infection of humans it is still adapting to its new host. Recent studies suggest that the virus could be evolving towards decreased virulence at the population level. I study whether the widespread administration of ART has the potential to alter the course of virulence evolution and might result in a further attenuated virus. I conclude by discussing the implications of these results for viral control at the individual level and also for population level epidemic control.
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Worton, Adrian J. "Using mathematical models to understand the impact of climate change on tick-borne infections across Scotland." Thesis, University of Stirling, 2016. http://hdl.handle.net/1893/24918.
Full textAbstract:
Ticks are of global interest as the pathogens they spread can cause diseases that are of importance to both human health and economies. In Scotland, the most populous tick species is the sheep tick Ixodes ricinus, which is the vector of pathogens causing diseases such as Lyme borreliosis and Louping-ill. Recently, both the density and spread of I. ricinus ticks have grown across much of Europe, including Scotland, increasing disease risk. Due to the nature of the tick lifecycle they are particularly dependent on environmental factors, including temperature and habitat type. Because of this, the recent increase in tick-borne disease risk is believed to be linked to climate change. Many mathematical models have been used to explore the interactions between ticks and factors within their environments; this thesis begins by presenting a thorough review of previous modelling of tick and tick-borne pathogen dynamics, identifying current knowledge gaps. The main body of this thesis introduces an original mathematical modelling framework with the aim to further our understanding of the impact of climate change on tick-borne disease risk. This modelling framework takes into account how key environmental factors influence the I. ricinus lifecycle, and is used to create predictions of how I. ricinus density and disease risk will change across Scotland under future climate warming scenarios. These predictions are mapped using Geographical Information System software to give a clear spatial representation of the model predictions. It was found that as temperatures increase, so to do I. ricinus densities, as well as Louping-ill and Lyme borreliosis risk. These results give a strong indication of the disease risk implications of any changes to the Scottish environment, and so have the potential to inform policy-making. Additionally, the models identify areas of possible future research.
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Maku, Vyambwera Sibaliwe. "Mathematical modeling of TB disease dynamics in a crowded population." University of the Western Cape, 2020. http://hdl.handle.net/11394/7357.
Full textAbstract:
Philosophiae Doctor - PhD<br>Tuberculosis is a bacterial infection which is a major cause of death worldwide. TB is a
curable disease, however the bacterium can become resistant to the first line treatment
against the disease. This leads to a disease called drug resistant TB that is difficult
and expensive to treat. It is well-known that TB disease thrives in communities in overcrowded
environments with poor ventilation, weak nutrition, inadequate or inaccessible
medical care, etc, such as in some prisons or some refugee camps. In particular, the World
Health Organization discovered that a number of prisoners come from socio-economic disadvantaged
population where the burden of TB disease may be already high and access
to medical care may be limited. In this dissertation we propose compartmental models of
systems of differential equations to describe the population dynamics of TB disease under
conditions of crowding. Such models can be used to make quantitative projections of TB
prevalence and to measure the effect of interventions. Indeed we apply these models to
specific regions and for specific purposes. The models are more widely applicable, however
in this dissertation we calibrate and apply the models to prison populations.
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Kasaie, Sharifi Parasto Alsadat. "Agent-Based Simulation Modeling and Analysis of Infectious Disease Epidemics and Implications for Policy." University of Cincinnati / OhioLINK, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1396531551.
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Wu, Gianna. "Mathematical Modeling of Type 1 Diabetes." Scholarship @ Claremont, 2019. https://scholarship.claremont.edu/hmc_theses/231.
Full textAbstract:
Type 1 Diabetes (T1D) is an autoimmune disease where the pancreas produces little to no insulin, which is a hormone that regulates blood glucose levels. This happens because the immune system attacks (and kills) the beta cells of the pancreas, which are responsible for insulin production. Higher levels of glucose in the blood could have very negative, long term effects such as organ damage and blindness.
To date, T1D does not have a defined cause nor cure, and research for this disease is slow and difficult due to the invasive nature of T1D experimentation. Mathematical modeling provides an alternative approach for treatment development and can greatly advance T1D research. This thesis describes both a single-compartment and multi-compartment model for Type 1 Diabetes.
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Higgins, John M. (John Matthew). "Mathematical and mechanical modeling of vaso-occlusion in sickle cell disease." Thesis, Massachusetts Institute of Technology, 2007. http://hdl.handle.net/1721.1/38660.
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Thesis (S.M.)--Harvard-MIT Division of Health Sciences and Technology, 2007.<br>Includes bibliographical references.<br>Vaso-occlusive crises cause most of the morbidity and mortality associated with sickle cell disease. The proximal causes of these occlusive events are not well understood. The risks and consequences of vaso-occlusion however are clear. Ten percent of sickle cell disease patients will have a stroke by the age of 20. Two thirds of sickle cell disease patients require more than one hospitalization per year for treatment of pain crises. The flow behavior of blood samples from sickle cell patients was studied in an artificial microfluidic environment. This microfluidic environment allowed modulation of the hydrostatic pressure causing flow, the ambient oxygen concentration, and the vascular channel geometry. A range of blood samples was evaluated by selecting specimens with various hematocrits and concentrations of sickle hemoglobin. Velocity profiles were calculated following sudden changes in oxygen concentration. From these profiles, it was possible to create a phase space of vaso-occlusion in the artificial microfluidic environment. This phase space characterizes the environmental conditions in which sickle cell blood will stop flowing within a given interval of time.<br>(cont.) This work is a first step in characterizing the inter-relationships between some of the control parameters governing vaso-occlusion: pressure, oxygen concentration, channel size, hematocrit, and sickle hemoglobin concentration. This artificial device enables a quantification of the effect of a clinical therapy, red blood cell exchange, as performed on an actual sickle cell patient. Additionally, three sample small molecules known to alter rates of sickle hemoglobin polymerization were evaluated for their ability to perturb the tendency of sickle cell blood to stop flowing. These results suggest a possible application of this technique to the diagnosis and monitoring of sickle cell patients as well as to the investigation of new regimens of existing treatments and altogether novel therapies.<br>by John M. Higgins.<br>S.M.
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Jelassi, Mariem. "Modélisation, simulation et analyse multi-échelle de réseaux sociaux complexes : Application à l'aide à la prévention des maladies contagieuses." Thesis, Université Grenoble Alpes (ComUE), 2017. http://www.theses.fr/2017GREAS033/document.
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La présente thèse porte sur la mise en place d'un cadre théorique (conceptualisation et formalisation), visant à décrire la propagation de l'obésité au sein d'un réseau d'individus, pour parvenir à mettre en place les bonnes politiques de prévention, afin de limiter la diffusion de cette épidémie, dont la contamination est à caractère social. Pour ce faire, j’ai commencé d'abord à mettre en place une analyse approfondie des différents déterminants de l'obésité. Une fois cette étape achevée, j’ai développé un modèle de réseau, dans lequel les relations entre les individus (représentés par les nœuds du réseau) sont régies par des règles permettant d'évaluer la présence/absence de liens selon certaines valeurs d'influence, fonction de la tranche d'âge des nœuds en question et de leur caractère homophilique. Ce modèle, fondé sur la structuration en âges et la démographie, comporte deux processus; le premier permet de décrire l'obésité au niveau individuel, sous forme de compartiments épidémiologiques. Le deuxième, quant à lui, représente le niveau inter-individuel, sous forme de réseau individu-centré. Par la suite, une fois analysé le comportement asymptotique du modèle, j'ai étudié la structure sociale obtenue, pour y repérer les individus les plus influents. Ces derniers seront ceux à cibler dans la politique de prévention. Enfin, pour valider le modèle par des données de terrain, j'ai réalisé une enquête au sein d'un collège tunisien, et j'ai comparé les résultats obtenus par cette dernière avec ceux d'une enquête réalisée dans un collège français<br>This thesis deals with the establishment of a theoretical framework (conceptualization and formalization) capable of describing the obesity spread within a network of individuals, in order to achieve the right prevention policies and limit the epidemic spread. To do this, I started by initiating an in-depth analysis of the different obesity determinants. Once this stage completed, I developed a network model in which the relations between the individuals, (represented by the nodes of the network) are governed by rules allowing to evaluate the presence/absence of links according to their values of influence, age of the concerned nodes and their homophilic characteristics. This model, based on the age structure and demography, is constituted by two processes: the first one describes obesity at the individual level, by using epidemiological compartments. The second one describes the inter-individual level by using an individual-based network. Later, when the model reached its asymptotic behavior, I studied the social structure obtained to locate the most important individuals to be targeted in the prevention policy. Eventually, to validate the model with data, I realized an investigation in a Tunisian college and compared the obtained results from this study with those obtained from a French college survey
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El, Moustaid Fadoua. "Modeling Temperature Effects on Vector-Borne Disease Dynamics." Diss., Virginia Tech, 2019. http://hdl.handle.net/10919/102579.
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Vector-borne diseases (VBDs) cause significant harm to humans, plants, and animals worldwide. For instance, VBDs are very difficult to manage, as they are governed by complex interactions. VBD transmission depends on the pathogen itself, vector-host movement, and environmental conditions. Mosquito-borne diseases are a perfect example of how all these factors contribute to changes in VBD dynamics. Although vectors are highly sensitive to climate, modeling studies tend to ignore climate effects. Here, I am interested in the arthropod small vectors that are sensitive to climate factors such as temperature, precipitation, and drought. In particular, I am looking at the effect of temperature on vector traits for two VBDs, namely, dengue, caused by a virus that infects humans and bluetongue disease, caused by a virus that infects ruminants. First, I collect data on mosquito traits' response to temperature changes, this includes adult traits as well as juvenile traits. Next, I use these traits to model mosquito density, and then I incorporate the density into our mathematical models to investigate the effect it has on the basic reproductive ratio R0, a measure of how contagious the disease is. I use R0 to determine disease risk. For dengue, my results show that using mosquito life stage traits response to temperature improves our vector density approximation and disease risk estimates. For bluetongue, I use midge traits response to temperature to show that the suitable temperature for bluetongue risk is between 21.5 �C and 30.7 �C. These results can inform future control and prevention strategies.<br>Doctor of Philosophy
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Giorgakoudi, Kyriaki. "Mathematical modelling of the potential determinants of foot-and-mouth disease virus-induced death of bovine epithelial cells." Thesis, Loughborough University, 2014. https://dspace.lboro.ac.uk/2134/14931.
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Foot-and-mouth disease virus (FMDV) is a highly infectious virus affecting cloven-hoofed animals. The most prominent of its clinical signs is the development of vesicular lesions on the feet and in or around the mouth, which are a consequence of extensive FMDV-induced epithelial cell death. Currently, there is no certain biological knowledge on why extensive epithelial cell death occurs in some FMDV-infected tissues, but not in others. Using the epithelial tissues of tongue and dorsal soft palate as examples of a tissue where lesions occur and one that does not visibly exhibit FMDV-induced cell death, this work aims to identify the potential drivers of epithelial cell death and survival. A partial differential equation (PDE) model informed by experimental data on epithelial structure, is used to test epithelium thickness and cell layer structure as potential determinants. A second PDE model investigates FMDV-interferon (IFN) dynamics and their impact on the levels of cell death and survival, while an experimental study is undertaken to provide data for model validation. The work carried out casts light on the important role of a variety of factors including FMDV replication, IFN production and release, and IFN antiviral action.
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Barnabas, Ruanne V. "Mathematical modelling of the natural history of human papillomavirus infection and cervical carcinoma : the impact of intervention strategies on disease incidence." Thesis, University of Oxford, 2005. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.413982.
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Denholm, Scott J. "Mathematical models for investigating the long-term impact of Gyrodactylus salaris infections on Atlantic salmon populations." Thesis, University of Stirling, 2013. http://hdl.handle.net/1893/17021.
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Gyrodactylus salaris Malmberg, 1957, is a notifiable freshwater ecto-parasite that infects both wild and farmed populations of Atlantic salmon (Salmo salar, L.). It has caused catastrophic damage to wild salmon stocks in Norway since its accidental introduction in 1975, reducing salmon density in some rivers by 98% over a period of five years. It is estimated that G. salaris has cost the Norwegian salmon industry more than 500 million EUR. Currently the UK has G. salaris free status under EU law, however, it is believed that if G. salaris emerged in the UK the impact would be similar to that witnessed in Norway. The aim of this thesis is to develop mathematical models that describe the salmon-G. salaris system in order to gain a greater understanding of the possible long-term impact the parasite may have on wild populations of Atlantic salmon in G. salaris-free territories such as the UK. Mathematical models, including deterministic, Leslie matrix and individual based models, were used to investigate the impact of G. salaris on Atlantic salmon at the individual and population level. It is known that the Atlantic strain of Atlantic salmon, examples of which occur naturally in Norway and the UK, does not have any resistance to G. salaris infections and the parasite population is able to quickly grow to epidemic levels. In contrast, the Baltic strain of Atlantic salmon, examples of which occur naturally in Sweden and Russia, exhibits some form of resistance and the parasite is unable to persist. Thus, baseline models were extended to include immunity to infection, a trade-off on salmon reproductive rate, and finally, to consider interactions between populations of G. salaris and multiple strains of salmon exhibiting varying levels of immunity from fully susceptible to resistant. The models proposed predict that in the absence of host resistance or an immune response infections by G. salaris will result in an epidemic followed by the extinction of the salmon host population. Models also predict that if salmon are able to increase their resistance to G. salaris infections through mutations, salmon population recovery after the epidemic is indeed possible within 10-15 years post introduction with low level parasite coexistence. Finally, models also highlight areas where additional information is needed in order to improve predictions and enable the estimation of important parameter values. Model predictions will ultimately be used to assist in future contingency planning against G. salaris outbreaks in the UK and possibly as a basis for future models describing other fish/ecto-parasite systems.
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Gooding, Emily J. (Emily Joanne). "A mixed methods approach to modeling personal protective equipment supply chains for infectious disease outbreak response." Thesis, Massachusetts Institute of Technology, 2016. http://hdl.handle.net/1721.1/104810.
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Thesis: S.M. in Technology and Policy, Massachusetts Institute of Technology, School of Engineering, Institute for Data, Systems, and Society, Technology and Policy Program, 2016.<br>This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.<br>Cataloged from student-submitted PDF version of thesis.<br>Includes bibliographical references (pages 115-121).<br>Personal protective equipment (PPE) is critical to the protection of healthcare workers responding to infectious disease outbreaks. The ability of the PPE supply chain to provide adequate and consistent supply when there is a large spike in demand has not been well-considered. Humanitarian logistics literature rarely considers infectious disease outbreaks as possible humanitarian crises while epidemiology literature assumes perfectly responsive supply chains. This thesis uses a mixed methods approach - an exploratory case study and system dynamics model - to bridge the gap between these two fields. It provides one approach for connecting epidemiology and supply chain research. An explanatory case study of the 2014 West Africa Ebola outbreak is used to analyze the PPE supply chain and its in-crisis functionality. We gather primary data using semi-structured interviews with supply chain actors and analyze that data using qualitative coding analysis. The system dynamics model is developed based on the results of the case study to offer insight as to how the PPE supply chain could be improved to better respond to future outbreaks. Several scenarios are simulated to test the effects of various supply chain improvement strategies. Relationship-building between supply chain actors, unconstrained shipping channels, flexible funding pools, and pre-positioning are all found to be effective supply chain improvement strategies.<br>by Emily J. Gooding.<br>S.M. in Technology and Policy
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Telionis, Pyrros A. "Novel Applications of Geospatial Analysis in the Modeling of Infectious Diseases." Diss., Virginia Tech, 2019. http://hdl.handle.net/10919/89432.
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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.<br>Doctor of Philosophy<br>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.
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Curtis, Donald Ephraim. "Using Social Networks for Modeling and Optimization in a Healthcare Setting." Diss., University of Iowa, 2011. https://ir.uiowa.edu/etd/4833.
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Social networks encode important information about the relationships between individuals. The structure of social networks has important implications for how ideas, information, and even diseases spread within a population. Data on online social networks is becoming increasingly available, but fine-grained data from which physical proximity networks can be inferred is still a largely elusive goal. We address this problem by using nearly 20 million anonymized login records from University of Iowa Hospitals and Clinics to construct healthcare worker (HCW) contact networks. These networks serve as proxies for potentially disease-spreading contact patterns among HCWs. We show that these networks exhibit properties similar to social networks arising in other contexts (e.g., scientific collaboration, friendship, etc.) such as the "Six Degrees of Kevin Bacon" (i.e., small-world) phenomenon. In order to develop a theoretic framework for analyzing these HCW contact networks we consider a number of random graph models and show that models which only pay attention to local structure may not adequately model disease spread. We then consider the best known approximation algorithms for a number of optimization problems that model the problem of determining an optimal set of HCWs to vaccinate in order to minimize the spread of disease. Our results show that, in general, the quality of solutions produced by these approximations is highly dependent on the dynamics of disease spread. However, experiments show that simple policies, like vaccinating the most well-connected or most mobile individuals, perform much better than a random vaccination policy. And finally we consider the problem of finding a set of individuals to act as indicators for important healthcare related events on a social network for infectious disease experts. We model this problem as a generalization of the budgeted maximum coverage problem studied previously and show that in fact our problem is much more difficult to solve in general. But by exposing a property of this network, we provide analysis showing that a simple greedy approach for picking indicators provides a near-optimal (constant-factor) approximation.
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Mideo, Nicole. "Integrating theory and experimentation in the study of malaria." Thesis, Kingston, Ont. : [s.n.], 2009. http://hdl.handle.net/1974/5093.
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Dorratoltaj, Nargesalsadat. "Immunological, Epidemiological, and Economic modeling of HIV, Influenza, and Fungal Meningitis." Diss., Virginia Tech, 2016. http://hdl.handle.net/10919/81876.
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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.<br>Ph. D.
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Ireland, Jillian M. "Using mathematical models to determine the effect of seasonal host birth rates on population dynamics of infectious disease systems." Thesis, University of Stirling, 2004. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.440776.
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Nemaranzhe, Lutendo. "A mathematical modeling of optimal vaccination strategies in epidemiology." University of the Western Cape, 2010. http://hdl.handle.net/11394/3065.
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Magister Scientiae - MSc<br>We review a number of compartmental models in epidemiology which leads to a nonlinear system of ordinary differential equations. We focus an SIR, SEIR and SIS epidemic models with and without vaccination. A threshold parameter R0 is identified which governs the spread of diseases, and this parameter is known as the basic reproductive number. The models have at least two equilibria, an endemic equilibrium and the disease-free equilibrium. We demonstrate that the disease will die out, if the basic reproductive number R0 < 1. This is the case of a disease-free state, with no infection in the population. Otherwise the disease may become endemic if the basic reproductive number R0 is bigger than unity. Furthermore, stability analysis for both endemic and disease-free steady states are investigated and we also give some numerical simulations. The second part of this dissertation deals with optimal vaccination strategy in epidemiology. We use optimal control technique on vaccination to minimize the impact of the disease. Hereby we mean minimizing the spread of the disease in the population, while also minimizing the effort on vaccination roll-out. We do this optimization for the cases of SIR and SEIR models, and show how optimal strategies can be obtained which minimize the damage caused by the infectious disease. Finally, we describe the numerical simulations using the fourth-order Runge-Kutta method. These are the most useful references: [G. Zaman, Y.H Kang, II. H. Jung. BioSystems 93, (2008), 240 − 249], [K. Hattaf, N. Yousfi. The Journal of Advanced Studies in Biology, Vol. 1(8), (2008), 383 − 390.], [Lenhart, J.T. Workman. Optimal Control and Applied to Biological Models. Chapman and Hall/CRC, (2007).], [P. Van den Driessche, J. Watmough. Math. Biosci., 7, (2005)], and [J. Wu, G. R¨ost. Mathematical Biosciences and Engineering, Vol 5(2), (2008), 389 − 391].<br>South Africa
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Maude, Richard James. "Malaria elimination modelling in the context of antimalarial drug resistance." Thesis, University of Oxford, 2013. http://ora.ox.ac.uk/objects/uuid:3a5321ca-f8fc-45b2-a002-363d982d3cc5.
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Introduction: Antimalarial resistance, particularly artemisinin resistance, is a major threat to P. falciparum malaria elimination efforts worldwide. Urgent intervention is required to tackle artemisinin resistance but field data on which to base planning of strategies are limited. The aims were to collect available field data and develop population level mathematical models of P. falciparum malaria treatment and artemisinin resistance in order to determine the optimal strategies for elimination of artemisinin resistant malaria in Cambodia and treatment of pre-hospital and severe malaria in Cambodia and Bangladesh. Methods: Malaria incidence and parasite clearance data from Cambodia and Bangladesh were collected and analysed and modelling parameters derived. Population dynamic mathematical models of P. falciparum malaria were produced. Results: The modelling demonstrated that elimination of artemisinin resistant P. falciparum malaria would be achievable in Cambodia in the context of artemisinin resistance using high coverages with ACT treatment, ideally combined with LLITNs and adjunctive single dose primaquine. Sustained efforts would be necessary to achieve elimination and effective surveillance is essential, both to identify the baseline malaria burden and to monitor parasite prevalence as interventions are implemented. A modelled policy change to rectal and intravenous artesunate in the context of pre-existing artemisinin resistance would not compromise the efficacy of ACT for malaria elimination. Conclusions: By being developed rapidly in response to specific questions the models presented here are helping to inform planning efforts to combat artemisinin resistance. As further field data become available, their planned on-going development will produce increasingly realistic and informative models which can be expected to play a central role in planning efforts for years to come.
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Lega, Joceline, and Heidi E. Brown. "Data-driven outbreak forecasting with a simple nonlinear growth model." ELSEVIER SCIENCE BV, 2016. http://hdl.handle.net/10150/622814.
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Recent events have thrown the spotlight on infectious disease outbreak response. We developed a data-driven method, EpiGro, which can be applied to cumulative case reports to estimate the order of magnitude of the duration, peak and ultimate size of an ongoing outbreak. It is based on a surprisingly simple mathematical property of many epidemiological data sets, does not require knowledge or estimation of disease transmission parameters, is robust to noise and to small data sets, and runs quickly due to its mathematical simplicity. Using data from historic and ongoing epidemics, we present the model. We also provide modeling considerations that justify this approach and discuss its limitations. In the absence of other information or in conjunction with other models, EpiGro may be useful to public health responders. (C) 2016 The Authors. Published by Elsevier B.V.
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Brown, Grant Donald. "Application Of Heterogeneous Computing Techniques To Compartmental Spatiotemporal Epidemic Models." Diss., University of Iowa, 2015. https://ir.uiowa.edu/etd/1554.
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The application of spatial methods to epidemic estimation and prediction problems is a vibrant and active area of research. In many cases, however, well thought out and laboratory supported models for epidemic patterns may be easy to specify but extremely difficult to fit efficiently. While this problem exists in many scientific disciplines, epidemic modeling is particularly prone to this challenge due to the rate at which the problem scope grows as a function of the size of the spatial and temporal domains involved.
An additional barrier to widespread use of spatiotemporal epidemic models is the lack of user friendly software packages capable of fitting them. In particular, compartmental epidemic models are easy to understand, but in most cases difficult to fit. This class of epidemic models describes a set of states, or compartments, which captures the disease progression in a population.
This dissertation attempts to expand the problem scope to which spatio-temporal compartmental epidemic models are applicable both computationally and practically.
In particular, a general family of spatially heterogeneous SEIRS models is developed alongside a software library with the dual goals of high computational performance and ease of use in fitting models in this class. We emphasize the task of model specification, and develop a framework describing the components of epidemic behavior. In addition, we establish methods to estimate and interpret reproductive numbers, which are of fundamental importance to the study of infectious disease. Finally, we demonstrate the application of these techniques both under simulation, and in the context of a diverse set of real diseases, including Ebola Virus Disease, Smallpox, Methicillin-resistant Staphylococcus aureus, and Influenza.