Academic literature on the topic 'Infectious disease research'

Emerging infectious diseases (EIDs) constitute an important global health security problem. During EID outbreaks, patient centred research can play a significant role in informing evidence-based care for patients, in calibrating public health responses, and in directing effective policy and research. However, to date, this type of research has been limited in impact. This thesis sets out to improve the value of patient centred research in combating EID outbreaks. It provides a structured analysis of what has previously constrained efforts to rapidly accumulate high-quality evidence. It provides primary data from research conducted during an outbreak, and conducted in an outbreak vulnerable setting. And it provides recommendations that aim to facilitate high-quality data collection in future events. This thesis contains four results chapters. Chapter 2 systematically reviews elements of the research response to two EID outbreaks of public health importance. Chapter 3 provides findings of a phase II clinical trial of an investigational therapy for Ebola virus disease (EVD), contextualises the utility of this and comparable work in improving patient care, and discusses the operational feasibility of such work during an epidemic. Chapter 4 focuses specifically on improving one element - disease characterisation - during EID outbreaks. It achieves this through presenting a systematic analysis of bias in the characterisation of EVD and recommends how to prioritise data gathering for high-risk pathogens. Chapter 5 exemplifies how clinical data collection practices can progress between outbreaks. It is the first stage of work undertaken to improve the clinical characterisation of communicable diseases in the vulnerable environment of refugee camps. This thesis demonstrates progress towards having higher quality clinical research conducted during the time frame of an epidemic. Future work can focus on the most important barriers to accelerating research, now that these have been more clearly defined.

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

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.

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

Includes abstract.<br>Includes bibliographical references.<br>Finding and treating cases [of tuberculosis] in the community before they present to health facilities, a strategy known as active-case-finding is gaining momentum as a way to decrease the infectious pool. This can be achieved through door-to-door community surveys using a TB symptom-screening questionnaire, and is an economical and practical tool to employ in poor, high burden areas. However, unlike for the high risk group of people infected with HIV, there is a lack of evidence supporting the adaptation of a symptom screening tool in the other high risk groups. In 2010, a TB prevalence survey was conduceted in 24 high TB and HIV burden communities in Zambia and the Western Cape, South Africa. This prevalence survey served as the endpoint for the Zambia and South Africa TB and AIDS Reduction study (ZAMSTAR). This survey made use of a questionnaire the collected, among other information, data regarding individual TB symptom reporting, HIV status, diabetes mellitus status and cigarette smoking.

Case reports describe significant norovirus gastroenteritis morbidity in immunocompromised patients. We evaluated norovirus pathogenesis in prospectively enrolled solid organ (SOT) and hematopoietic stem cell transplant (HSCT) patients with diarrhea who presented to Texas Children's Hospital and submitted stool for enteric testing. Noroviruses were detected by real-time reverse transcription polymerase chain reaction. Clinical outcomes of norovirus diarrhea and non-norovirus diarrhea patients, matched by transplanted organ type, were compared. Norovirus infection was identified in 25 (22%) of 116 patients, more frequently than other enteropathogens. Fifty percent of norovirus patients experienced diarrhea lasting ≥14 days, with median duration of 12.5 days (range 1–324 days); 29% developed diarrhea recurrence. Fifty-five percent of norovirus patients were hospitalized for diarrhea, with 27% requiring intensive care unit (ICU) admission. One HSCT recipient developed pneumatosis intestinalis. Three HSCT patients expired ≤6 months of norovirus diarrhea onset. Compared to non-norovirus diarrhea patients, norovirus patients experienced significantly more frequent ICU admission (27% vs. 0%, p = 0.02), greater serum creatinine rise (median 0.3 vs. 0.2 mg/dL, p = 0.01), and more weight loss (median 1.6 vs. 0.6 kg, p < 0.01). Noroviruses are an important cause of diarrhea in pediatric transplant patients and are associated with significant clinical complications.

Thesis: S.M., Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2014.<br>76<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (pages 67-72).<br>In the event of a pandemic influenza outbreak, such as the 2009-2010 H1N1 "Swine Flu" episode, it is crucial to effectively allocate limited resources in order to minimize the casualties. Design of effective resource allocation strategies requires good understanding of the underlying contact network and of the propagation dynamics. In this thesis we develop a parameter estimation method that learns the network structure, among a family of graphs, and disease dynamics from the recorded infection curve, assuming that the disease dynamics follow an SIR process. We apply the method to data collected during the 2009-2010 H1N1 epidemic and show that the best-fit model, among a scale-free network and a small-world network, indicates the scale-free network. Given the knowledge of the network structure we evaluate different vaccination strategies. As a benchmark, we allow the vaccination decisions to depend on the state of the epidemic and we show that random vaccination (which is the current practice), does not efficiently halt the spread of influenza. Instead, we propose vaccine allocation strategies that exploit the underlying network structure and provide a reduction in the number of infections by over 6 times compared to the current practice. In addition, more realistic scenario involves random encounters between agents. To test this hypothesis, we introduced a dynamic network formation on top of the static network model. We apply the estimation method to the dynamic network model and show a small improvement in estimating the infection dynamics of the 2009-2010 H1N1 influenza.<br>by Louis Y. Kim.<br>S.M.

The last two decades have seen several large-scale epidemics of international impact, including human, animal and plant epidemics. Policy makers face health challenges that require epidemic predictions based on limited information. There is therefore a pressing need to construct models that allow us to frame all available information to predict an emerging outbreak and to control it in a timely manner. The aim of this thesis is to develop an early-warning modelling approach that can predict emerging disease outbreaks. Based on Bayesian techniques ideally suited to combine information from different sources into a single modelling and estimation framework, I developed a suite of approaches to epidemiological data that can deal with data from different sources and of varying quality. The SEIR model, particle filter algorithm and a number of influenza-related datasets were utilised to examine various models and methodologies to predict influenza outbreaks. The data included a combination of consultations and diagnosed influenza-like illness (ILI) cases for five influenza seasons. I showed that for the pandemic season, different proxies lead to similar behaviour of the effective reproduction number. For influenza datasets, there exists a strong relationship between consultations and diagnosed datasets, especially when considering time-dependent models. Individual parameters for different influenza seasons provided similar values, thereby offering an opportunity to utilise such information in future outbreaks. Moreover, my findings showed that when the temperature drops below 14°C, this triggers the first substantial rise in the number of ILI cases, highlighting that temperature data is an important signal to trigger the start of the influenza epidemic. Further probing was carried out among Maltese citizens and estimates on the under-reporting rate of the seasonal influenza were established. Based on these findings, a new epidemiological model and framework were developed, providing accurate real-time forecasts with a clear early warning signal to the influenza outbreak. This research utilised a combination of novel data sources to predict influenza outbreaks. Such information is beneficial for health authorities to plan health strategies and control epidemics.