Relevant bibliographies by topics / Infectious disease research

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Infectious disease research'

Author: Grafiati
Published: 4 June 2021
Last updated: 11 February 2022

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Journal articles on the topic "Infectious disease research"

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Head, M. "Infectious Disease Research Network." Journal of Antimicrobial Chemotherapy 64, Supplement 1 (August 12, 2009): i25—i27. http://dx.doi.org/10.1093/jac/dkp259.

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Lou, Zhiyong. "Infectious Disease Research in China." ACS Infectious Diseases 6, no. 5 (April 24, 2020): 760. http://dx.doi.org/10.1021/acsinfecdis.0c00220.

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Dove, Alan. "New infectious disease research funding." Nature Medicine 4, no. 12 (December 1998): 1350. http://dx.doi.org/10.1038/3932.

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ICHINOSE, Yoshio. "Kenya Research Station and Viral Infectious Disease Research." Uirusu 63, no. 1 (2013): 75–78. http://dx.doi.org/10.2222/jsv.63.75.

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Dorrell, Sharon. "International grants for infectious disease research." Molecular Medicine Today 5, no. 8 (August 1999): 327. http://dx.doi.org/10.1016/s1357-4310(99)01533-6.

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Tramont, Edmund C., and Arthur L. Kellermann. "The Infectious Disease Clinical Research Program." Military Medicine 184, Supplement_2 (November 1, 2019): 1–2. http://dx.doi.org/10.1093/milmed/usz344.

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Layne, Scott P., and Tony J. Beugelsdijk. "Laboratory firepower for infectious disease research." Nature Biotechnology 16, no. 9 (September 1998): 825–29. http://dx.doi.org/10.1038/nbt0998-825.

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Sarkar, Anjali A. "Top Protocols in Infectious Disease Research." Genetic Engineering & Biotechnology News 41, P1 (September 1, 2021): P40—P42. http://dx.doi.org/10.1089/gen.41.p1.14.

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Shinoda, Sumio. "Special Issue on Infectious Disease Control in SATREPS Projects." Journal of Disaster Research 13, no. 4 (August 1, 2018): 733–34. http://dx.doi.org/10.20965/jdr.2018.p0733.

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The Science and Technology Research Partnership for Sustainable Development (SATREPS) is a Japanese government program that promotes international joint research. The program is structured as a collaboration between the Japan Science and Technology Agency (JST) and the Japan International Cooperation Agency (JICA). The program includes various fields, such as Environment and Energy, Bioresources, Disaster Prevention and Mitigation, and Infectious Disease Control, and a total 52 projects were currently in progress as of May, 2018. It is expected that the promotion of international joint research under this program will enable Japanese research institutions to conduct research more effectively in fields and having targets that make it advantageous to do that research in developing countries, including countries in Latin America and the Caribbean, Asia, and Africa. Recently, SATREPS projects in the field of Infectious Disease have been but under the control of the Japan Agency for Medical Research and Development (AMED). Although adult maladies, such as malignant tumors, heart disease, and cerebral apoplexy, are major causes of death in the developed countries including Japan, infectious diseases are still responsible for the high mortality rates in developing countries. Therefore, Infectious Disease Control is the important field of SATREPS. Infectious Disease Control projects are progressing in several countries, including Kenya, Zambia, Bangladesh, the Philippines, and Brazil, and various infectious diseases and pathogens have been targeted. In this special issue on Infectious Disease Control, the following reports from three projects have been selected: “The JICA-AMED SATREPS Project to Control Outbreaks of Yellow Fever and Rift Valley Fever in Kenya” by Nagasaki University, “Comprehensive Etiological and Epidemiological Study on Acute Respiratory Infections in Children in the Philippines” by Tohoku University, and “International Joint Research on Antifungal Resistant Fungi in Brazil” by Chiba University. These projects include viral, bacterial, and fungal infections. If they become available, further supplementary reports from other projects in this field will be published in a future issue.
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Hamburg, Margaret A. "Considerations for infectious disease research and practice." Technology in Society 30, no. 3-4 (August 2008): 383–87. http://dx.doi.org/10.1016/j.techsoc.2008.04.002.

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Dissertations / Theses on the topic "Infectious disease research"

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Rojek, Amanda. "Improving patient centred research during infectious disease outbreaks." Thesis, University of Oxford, 2017. https://ora.ox.ac.uk/objects/uuid:8a53052f-9585-4709-a06e-15586826efce.

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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.
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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.

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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.
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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.

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Thesis (S.M.)--Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2012.
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (p. 85-88).
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.
by Jane A. Evans.
S.M.
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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.

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Thesis advisor: Kenneth Williams
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
Thesis (PhD) — Boston College, 2020
Submitted to: Boston College. Graduate School of Arts and Sciences
Discipline: Biology
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Sattar, Shahra. "Influence of HIV, smoking and hyperglycaemia on the reporting of TB symptoms in a TB prevalence survey." Master's thesis, University of Cape Town, 2013. http://hdl.handle.net/11427/3065.

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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.
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Ye, X., J. N. Van, F. M. Munoz, P. A. Revell, Claudia A. Korinetz, R. A. Krance, R. L. Atmar, M. K. Estes, and H. L. Koo. "Noroviruses as a Cause of Diarrhea in Immunocompromised Pediatric Hematopoietic Stem Cell and Solid Organ Transplant Recipients." Digital Commons @ East Tennessee State University, 2015. https://dc.etsu.edu/etsu-works/1490.

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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.
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Kim, Louis Y. (Louis Yongchul). "Estimating network structure and propagation dynamics for an infectious disease : towards effective vaccine allocation." Thesis, Massachusetts Institute of Technology, 2014. http://hdl.handle.net/1721.1/91397.

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Thesis: S.M., Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2014.
76
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 67-72).
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.
by Louis Y. Kim.
S.M.
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Marmara, Vincent Anthony. "Prediction of Infectious Disease outbreaks based on limited information." Thesis, University of Stirling, 2016. http://hdl.handle.net/1893/24624.

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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.
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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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Hardison, Rachael Lake. "Haemophilus pathogenesis during otitis media: Influence of nutritional immunity on bacterial persistence and intracellular lifestyles." The Ohio State University, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=osu1540483623343597.

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Books on the topic "Infectious disease research"

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Infectious disease research. San Diego, CA: ReferencePoint Press, 2012.

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Alper, Joe, ed. Big Data and Analytics for Infectious Disease Research, Operations, and Policy. Washington, D.C.: National Academies Press, 2016. http://dx.doi.org/10.17226/23654.

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Emil von Behring: Infectious disease, immunology, serum therapy. Philadelphia: American Philosophical Society, 2005.

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Union, European. Global report for research on infectious diseases of poverty. Geneva, Switzerland: TDR/World Health Organization, 2012.

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(Canada), International Centre for Infectious Diseases Task Force. Fighting disease, fostering innovation: The report of the International Centre for Infectious Diseases Task Force. [Ottawa]: International Centre for Infectious Diseases Task Force, 2003.

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State Research Center of Virology and Biotechnology (Russia), ed. Development of international collaboration in infectious disease research: International conference : abstracts : "Sosnovka," Novosibirsk Region, Russia 8-10, September 2004. Novosibirsk: CERIS, 2004.

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Parker, Philip M., and James N. Parker. Lyme disease: A medical dictionary, bibliography, and annotated research guide to internet references. San Diego, CA: ICON Health Publications, 2004.

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Johnson, Anne F., Andrew Bremer, Julie Liao, and Audrey Thévenon, eds. Pivotal Interfaces of Environmental Health and Infectious Disease Research to Inform Responses to Outbreaks, Epidemics, and Pandemics. Washington, D.C.: National Academies Press, 2021. http://dx.doi.org/10.17226/26270.

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Office, General Accounting. Global health: Challenges in improving infectious disease surveillance systems : report to Congressional requesters. Washington, D.C: The Office, 2001.

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United States. Veterans Health Services and Research Administration. Medical Service. Infectious diseases. 6th ed. [Washington, D.C.?]: Dept. of Veterans Affairs, Veterans Health Services and Research Administration, Medical Service, 1989.

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Book chapters on the topic "Infectious disease research"

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Sintchenko, Vitali. "Informatics for Infectious Disease Research and Control." In Infectious Disease Informatics, 1–26. New York, NY: Springer New York, 2009. http://dx.doi.org/10.1007/978-1-4419-1327-2_1.

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Selgelid, Michael J. "Dual-Use Research Codes of Conduct: Lessons from the Life Sciences." In Infectious Disease Ethics, 135–43. Dordrecht: Springer Netherlands, 2009. http://dx.doi.org/10.1007/978-94-007-0564-7_13.

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Sobral, Bruno, Chunhong Mao, Maulik Shukla, Dan Sullivan, and Chengdong Zhang. "Informatics-Driven Infectious Disease Research." In Biomedical Engineering Systems and Technologies, 3–11. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-29752-6_1.

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Deem, Sharon L., Vanessa O. Ezenwa, Jessica R. Ward, and Bruce A. Wilcox. "Chapter Fourteen.. Research Frontiers in Ecological Systems: Evaluating the Impacts of Infectious Disease on Ecosystems." In Infectious Disease Ecology, edited by Richard S. Ostfeld, Felicia Keesing, and Valerie T. Eviner, 304–18. Princeton: Princeton University Press, 2010. http://dx.doi.org/10.1515/9781400837885.304.

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Evans, Nicholas G. "Dual-Use and Infectious Disease Research." In Infectious Diseases in the New Millennium, 193–215. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-39819-4_9.

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Macintyre, C. Raina, James G. Wood, Rochelle Watkins, and Zhanhai Gao. "Modeling in Immunization and Biosurveillance Research." In Infectious Disease Informatics and Biosurveillance, 259–78. Boston, MA: Springer US, 2010. http://dx.doi.org/10.1007/978-1-4419-6892-0_12.

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Morales, Rodrigo, Baian Chen, and Claudio Soto. "Are Amyloids Infectious?" In Current Hypotheses and Research Milestones in Alzheimer's Disease, 171–80. Boston, MA: Springer US, 2009. http://dx.doi.org/10.1007/978-0-387-87995-6_14.

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Gunning, Paul A., and Bärbel Hauröder. "Public Health/Pharmaceutical Research - Pathology and Infectious Disease." In Biological Field Emission Scanning Electron Microscopy, 311–42. Chichester, UK: John Wiley & Sons, Ltd, 2019. http://dx.doi.org/10.1002/9781118663233.ch14.

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Konrad, Andreas, Ramona Jochmann, Elisabeth Kuhn, Elisabeth Naschberger, Priya Chudasama, and Michael Stürzl. "Reverse Transfected Cell Microarrays in Infectious Disease Research." In Methods in Molecular Biology, 107–18. Totowa, NJ: Humana Press, 2010. http://dx.doi.org/10.1007/978-1-61737-970-3_9.

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Dantzer, R., A. Aubert, R. M. Bluthe, J. R. Konsman, S. Laye, P. Parnet, and K. W. Kelley. "Sickness Behavior: A Neuroimmune-Based Response to Infectious Disease." In Research and Perspectives in Neurosciences, 169–84. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/978-3-642-59643-8_15.

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Conference papers on the topic "Infectious disease research"

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Bogdanova, E. N. "POPULATION OF IXODID TICKS IN CITIES OF THE EUROPEAN PART OF THE RUSSIAN FEDERATION AND THEIR EPIDEMIOLOGICAL SIGNIFICANCE." In V International Scientific Conference CONCEPTUAL AND APPLIED ASPECTS OF INVERTEBRATE SCIENTIFIC RESEARCH AND BIOLOGICAL EDUCATION. Tomsk State University Press, 2020. http://dx.doi.org/10.17223/978-5-94621-931-0-2020-64.

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On the territory of Russia there are more than 70 species of ticks of the family Ixodidae. Many of them are vectors of infectious diseases. The most common tick-borne infections are tick-borne encephalitis and Lyme disease. In recent decades, in many countries there has been a process of tick occupation of the territories of settlements, including large cities and megapolices. This leads to an increase in the number of tick attacks on peoples, as well as the cases of tick-borne infections.
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Fan, Feng-Hua, and Yong-Chang Huang. "Research on Financial Crises with Infectious Disease Model." In International Conference on Humanity and Social Science (ICHSS2016). WORLD SCIENTIFIC, 2017. http://dx.doi.org/10.1142/9789813208506_0052.

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Zughaier, Susu. "High Vaccine Coverage is Crucial for Preventing the Spread of Infectious Diseases During Mass Gathering." In Qatar University Annual Research Forum & Exhibition. Qatar University Press, 2020. http://dx.doi.org/10.29117/quarfe.2020.0138.

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Background: Vaccines are the most cost-effective intervention in public health as they prevent the spread of highly contagious infectious diseases. Because of vaccine implementation and high coverage, Measles was eradicated in 2000, however the recent reappearance of measles in the United States, Europe and globally is alarming. The resurgence of Measles, Diphtheria and Mumps is due to a reduction in vaccine coverage and herd immunity. Vaccine hesitant parents, antivaxxers, and fake news on vaccines are driving the surge in those infectious diseases. The World Health Organization issued the Global Vaccine and Immunization Action Plan to reiterate the importance of vaccine implementation and coverage for several vaccine-preventable infectious diseases in the world. Qatar is preparing for the upcoming FIFA World Cup 2022 therefore maintaining high vaccine coverage, which is critical in preventing infectious diseases spreading during such mass gathering. Methods: Literature search for vaccine coverage rates, resurgence of vaccine preventable infectious diseases and risks of mass gatherings. Results: Seventeen infectious diseases are currently vaccine-preventable. The cost-effectiveness of vaccine is documented as it is estimated for each dollar spent on vaccines, 10 dollars are saved in disease treatment. A drop in vaccine coverage rates to under 90% lead to the resurgence of measles. Vaccine coverage rate in Qatar is currently at 95% which is one of the highest in the world. Qatar must maintain this high coverage rate to prevent any measles outbreaks during mass gatherings. The planned World Cup event will take place from November 21 till December 18 2022, which is the peak for seasonal influenza. In preparedness for this major event, Qatar should encourage residents and visitors to be vaccinated not just against measles and seasonal influenza, but also hepatitis and meningitis. Conclusion: Maintaining 95% vaccine coverage rate is critical for preventing the resurgence of vaccine-preventable infectious diseases during the World Cup mass gathering in Qatar.
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Tung, Thai Quang, Youngmahn Han, and Insung Ahn. "SEEM: A simulation platform for modeling of infectious disease spreading." In Bioscience and Medical Research 2015. Science & Engineering Research Support soCiety, 2015. http://dx.doi.org/10.14257/astl.2015.105.02.

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Kurahashi, Setsuya. "An Infectious Disease Medical Policy Simulation and Gaming." In 2019 International Research Conference on Smart Computing and Systems Engineering (SCSE). IEEE, 2019. http://dx.doi.org/10.23919/scse.2019.8842733.

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Tirado-Ramos, Alfredo, Alexandra Anghelescu, Jingjing Gao, and Minh L. T. Nguyen. "Distributed decision support for cancer research associated to Infectious Disease." In 2012 25th IEEE International Symposium on Computer-Based Medical Systems (CBMS). IEEE, 2012. http://dx.doi.org/10.1109/cbms.2012.6266398.

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Paris, Grigori, Jasmin Heidepriem, Alexandra Tsouka, Marco Mende, Stephan Eickelmann, and Felix F. Loeffler. "Automated laser-assisted synthesis of microarrays for infectious disease research." In Microfluidics, BioMEMS, and Medical Microsystems XVII, edited by Bonnie L. Gray and Holger Becker. SPIE, 2019. http://dx.doi.org/10.1117/12.2516781.

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Elkin, Magdalyn E., Whitney A. Andrews, and Xingquan Zhu. "Network Analysis and Recommendation for Infectious Disease Clinical Trial Research." In BCB '19: 10th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics. New York, NY, USA: ACM, 2019. http://dx.doi.org/10.1145/3307339.3342156.

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Wang, Donghui. "Prediction of infectious disease spread based on cellular automata." In 2016 2nd Workshop on Advanced Research and Technology in Industry Applications (WARTIA-16). Paris, France: Atlantis Press, 2016. http://dx.doi.org/10.2991/wartia-16.2016.191.

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MCDERMOTT, JASON E., PASCAL BRAUN, RICHARD BONNEAU, and DANIEL R. HYDUKE. "MODELING HOST-PATHOGEN INTERACTIONS: COMPUTATIONAL BIOLOGY AND BIOINFORMATICS FOR INFECTIOUS DISEASE RESEARCH." In Proceedings of the Pacific Symposium. WORLD SCIENTIFIC, 2011. http://dx.doi.org/10.1142/9789814366496_0027.

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Reports on the topic "Infectious disease research"

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Amano, K. I., A. O. Anderson, C. L. Bailey, M. Balady, and R. F. Berendt. U.S. Army Medical Research Institute of Infectious Disease Annual Progress Report, Fiscal Year 1985. Fort Belvoir, VA: Defense Technical Information Center, October 1985. http://dx.doi.org/10.21236/ada230449.

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Holland, Darren, and Nazmina Mahmoudzadeh. Foodborne Disease Estimates for the United Kingdom in 2018. Food Standards Agency, January 2020. http://dx.doi.org/10.46756/sci.fsa.squ824.

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In February 2020 the FSA published two reports which produced new estimates of foodborne norovirus cases. These were the ‘Norovirus Attribution Study’ (NoVAS study) (O’Brien et al., 2020) and the accompanying internal FSA technical review ‘Technical Report: Review of Quantitative Risk Assessment of foodborne norovirus transmission’ (NoVAS model review), (Food Standards Agency, 2020). The NoVAS study produced a Quantitative Microbiological Risk Assessment model (QMRA) to estimate foodborne norovirus. The NoVAS model review considered the impact of using alternative assumptions and other data sources on these estimates. From these two pieces of work, a revised estimate of foodborne norovirus was produced. The FSA has therefore updated its estimates of annual foodborne disease to include these new results and also to take account of more recent data related to other pathogens. The estimates produced include: •Estimates of GP presentations and hospital admissions for foodbornenorovirus based on the new estimates of cases. The NoVAS study onlyproduced estimates for cases. •Estimates of foodborne cases, GP presentations and hospital admissions for12 other pathogens •Estimates of unattributed cases of foodborne disease •Estimates of total foodborne disease from all pathogens Previous estimates An FSA funded research project ‘The second study of infectious intestinal disease in the community’, published in 2012 and referred to as the IID2 study (Tam et al., 2012), estimated that there were 17 million cases of infectious intestinal disease (IID) in 2009. These include illness caused by all sources, not just food. Of these 17 million cases, around 40% (around 7 million) could be attributed to 13 known pathogens. These pathogens included norovirus. The remaining 60% of cases (equivalent to 10 million cases) were unattributed cases. These are cases where the causal pathogen is unknown. Reasons for this include the causal pathogen was not tested for, the test was not sensitive enough to detect the causal pathogen or the pathogen is unknown to science. A second project ‘Costed extension to the second study of infectious intestinal disease in the community’, published in 2014 and known as IID2 extension (Tam, Larose and O’Brien, 2014), estimated that there were 566,000 cases of foodborne disease per year caused by the same 13 known pathogens. Although a proportion of the unattributed cases would also be due to food, no estimate was provided for this in the IID2 extension. New estimates We estimate that there were 2.4 million cases of foodborne disease in the UK in 2018 (95% credible intervals 1.8 million to 3.1 million), with 222,000 GP presentations (95% Cred. Int. 150,000 to 322,000) and 16,400 hospital admissions (95% Cred. Int. 11,200 to 26,000). Of the estimated 2.4 million cases, 0.9 million (95% Cred. Int. 0.7 million to 1.2 million) were from the 13 known pathogens included in the IID2 extension and 1.4 million1 (95% Cred. Int. 1.0 million to 2.0 million) for unattributed cases. Norovirus was the pathogen with the largest estimate with 383,000 cases a year. However, this estimate is within the 95% credible interval for Campylobacter of 127,000 to 571,000. The pathogen with the next highest number of cases was Clostridium perfringens with 85,000 (95% Cred. Int. 32,000 to 225,000). While the methodology used in the NoVAS study does not lend itself to producing credible intervals for cases of norovirus, this does not mean that there is no uncertainty in these estimates. There were a number of parameters used in the NoVAS study which, while based on the best science currently available, were acknowledged to have uncertain values. Sensitivity analysis undertaken as part of the study showed that changes to the values of these parameters could make big differences to the overall estimates. Campylobacter was estimated to have the most GP presentations with 43,000 (95% Cred. Int. 19,000 to 76,000) followed by norovirus with 17,000 (95% Cred. Int. 11,000 to 26,000) and Clostridium perfringens with 13,000 (95% Cred. Int. 6,000 to 29,000). For hospital admissions Campylobacter was estimated to have 3,500 (95% Cred. Int. 1,400 to 7,600), followed by norovirus 2,200 (95% Cred. Int. 1,500 to 3,100) and Salmonella with 2,100 admissions (95% Cred. Int. 400 to 9,900). As many of these credible intervals overlap, any ranking needs to be undertaken with caution. While the estimates provided in this report are for 2018 the methodology described can be applied to future years.
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Redington, Bryce C., Jose A. Lopez, Llewellyn J. Legters, and Richard E. Krieg. Research Program in Tropical Infectious Diseases. Fort Belvoir, VA: Defense Technical Information Center, December 1990. http://dx.doi.org/10.21236/ada236917.

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Redington, Bryce C. Research Program in Tropical Infectious Diseases. Fort Belvoir, VA: Defense Technical Information Center, February 1994. http://dx.doi.org/10.21236/ada285350.

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Sánchez-Páez, David A. Effects of income inequality on COVID-19 infections and deaths during the first wave of the pandemic: Evidence from European countries. Verlag der Österreichischen Akademie der Wissenschaften, August 2021. http://dx.doi.org/10.1553/populationyearbook2022.res1.1.

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Evidence from research on infectious diseases suggests that income inequality is related to higher rates of infection and death in disadvantaged population groups. Our objective is to examine whether there was an association between income inequality and the numbers of cases and deaths during the first wave of the COVID- 19 pandemic in European countries. We determined the duration of the first wave by first smoothing the number of daily cases, and then using a LOESS regression to fit the smoothed trend. Next, we estimated quasi-Poisson regressions. Results from the bivariate models suggest there was a moderate positive association between the Gini index values and the cumulated number of infections and deaths during the first wave, although the statistical significance of this association disappeared when controls were included. Results from multivariate models suggest that higher numbers of infections and deaths from COVID-19 were associated with countries having more essential workers, larger elderly populations and lower health care capacities.
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Dietze, Reynaldo. Research and Training in Tropical and Emerging Infectious Diseases in Brazil. Fort Belvoir, VA: Defense Technical Information Center, January 2000. http://dx.doi.org/10.21236/ada382544.

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Alcaide, C., A. O. Anderson, C. L. Bailey, K. Baksi, and M. A. Balady. U.S. Army Medical Research Institute of Infectious Diseases Annual Report, Fiscal Year 1986. Fort Belvoir, VA: Defense Technical Information Center, October 1986. http://dx.doi.org/10.21236/ada230324.

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Sangkharomaya, Suebpong, and Sorachai Nitayaphan. Research and Operational Support for the Study of Militarily Relevant Infectious Diseases of Interest to United States and Royal Thai Governments. Fort Belvoir, VA: Defense Technical Information Center, January 2004. http://dx.doi.org/10.21236/ada421356.

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Sangkharomaya, Suebpong, and Sorachai Nitayaphan. Research and Operational Support for the Study of Militarily Relevant Infectious Diseases of Interest in Both United States and Royal Thai Government. Fort Belvoir, VA: Defense Technical Information Center, January 2003. http://dx.doi.org/10.21236/ada412783.

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Nobithai, Anont, and Sorachai Nitayaphan. Research and Operational Support for the Study of Militarily Relevant Infectious Diseases of Interest to the United States Army and the Royal Thai Army. Fort Belvoir, VA: Defense Technical Information Center, January 2007. http://dx.doi.org/10.21236/ada466146.

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