Search NASA⌕ Search

SEARCH · Search NASA

Results for “INFECTIOUS DISEASES”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Cooperative Research and Infectious Disease Surveillance: A 2021 Epilogue

As the world looks forward to turning a corner in the face of the COVID-19 pandemic, it becomes increasingly evident that international research cooperation and dialogue is necessary to end this global catastrophe. Last year, we initiated a research topic: “Infectious Disease Surveillance: Cooperative Research in Response to Recent Outbreaks, Including COVID-19,” which aimed at featuring manuscripts focused on the essential link between surveillance and cooperative research for emerging and endemic diseases, and highlighting scientific partnerships in countries under-represented in the scientific literature. Here we recognize the body of work published from our manuscript call that resulted in over 50 published papers. This current analysis describes articles and authors from a variety of funded and unfunded international sources. The work exemplifies successful research and publications which are frequently cooperative, and may serve as a basis to model further global scientific engagements.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2017

Nature and Consequences of Biological Reductionism for the Immunological Study of Infectious Diseases

Evolution has conserved “economic” systems that perform many functions, faster or better, with less. For example, three to five leukocyte types protect from thousands of pathogens. In order to achieve so much with so little, biological systems combine their limited elements, creating complex structures. Yet, the prevalent research paradigm is reductionist. Focusing on infectious diseases, reductionist and non-reductionist views are here described. Furthermore, the literature indicates that reductionism is associated with information loss and errors, while non-reductionist operations can extract more information from the same data. When designed to capture one-to-many/many-to-one interactions—including the use of arrows that connect pairs of consecutive observations—non-reductionist (spatial–temporal) constructs eliminate data variability from all dimensions, except along one line, while arrows describe the directionality of temporal changes that occur along the line. To validate the patterns detected by non-reductionist operations, reductionist procedures are needed. Integrated (non-reductionist and reductionist) methods can (i) distinguish data subsets that differ immunologically and statistically; (ii) differentiate false-negative from -positive errors; (iii) discriminate disease stages; (iv) capture in vivo, multilevel interactions that consider the patient, the microbe, and antibiotic-mediated responses; and (v) assess dynamics. Integrated methods provide repeatable and biologically interpretable information.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS1976

Applications of luminescent systems to infectious disease methodology

The characterization of a clinical sample by a simple, fast, accurate, automatable analytical measurement is important in the management of infectious disease. Luminescence assays offer methods rich with options for these measurements. The instrumentation is common to each assay, and the investment is reasonable. Three general procedures were developed to varying degrees of completeness which measure bacterial levels by measuring their ATP, FMN and iron porphyrins. Bacteriuria detection and antibiograms can be determined within half a day. The characterization of the sample for its soluble ATP, FMN or prophyrins was also performed.

Picciolo, G. L.↗
DOE OSTI2022

Alternative SIAR models for infectious diseases and applications in the study of non-compliance

In this paper, we use modified versions of the SIAR model for epidemics to propose two ways of understanding and quantifying the effect of non-compliance to non-pharmaceutical intervention measures on the spread of an infectious disease. The SIAR model distinguishes between symptomatic infected (I) and asymptomatic infected (A) populations. One modification, which is simpler, assumes a known proportion of the population does not comply with government mandates such as quarantining and social-distancing. In a more sophisticated approach, the modified model treats non-compliant behavior as a social contagion. We theoretically explore different scenarios such as the occurrence of multiple waves of infections. Local and asymptotic analyses for both models are also provided.

Mathematics↗
DOE OSTI2015

Resource Requirements Planning for Hospitals Treating Serious Infectious Disease Cases

This report presents a mathematical model of the way in which a hospital uses a variety of resources, utilities and consumables to provide care to a set of in-patients, and how that hospital might adapt to provide treatment to a few patients with a serious infectious disease, like the Ebola virus. The intended purpose of the model is to support requirements planning studies, so that hospitals may be better prepared for situations that are likely to strain their available resources. The current model is a prototype designed to present the basic structural elements of a requirements planning analysis. Some simple illustrati ve experiments establish the mo del's general capabilities. With additional inve stment in model enhancement a nd calibration, this prototype could be developed into a useful planning tool for ho spital administrators and health care policy makers.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2019

Within-host infectious disease models accommodating cellular coinfection, with an application to influenza

Within-host models are useful tools for understanding the processes regulating viral load dynamics. While existing models have considered a wide range of within-host processes, at their core these models have shown remarkable structural similarity. Specifically, the structure of these models generally consider target cells to be either uninfected or infected, with the possibility of accommodating further resolution (e.g. cells that are in an eclipse phase). Recent findings, however, indicate that cellular coinfection is the norm rather than the exception for many viral infectious diseases, and that cells with high multiplicity of infection are present over at least some duration of an infection. The reality of these cellular coinfection dynamics is not accommodated in current within-host models although it may be critical for understanding within-host dynamics. This is particularly the case if multiplicity of infection impacts infected cell phenotypes such as their death rate and their viral production rates. Here, we present a new class of within-host disease models that allow for cellular coinfection in a scalable manner by retaining the low-dimensionality that is a desirable feature of many current within-host models. The models we propose adopt the general structure of epidemiological ‘macroparasite’ models that allow hosts to be variably infected by parasites such as nematodes and host phenotypes to flexibly depend on parasite burden. Specifically, our within-host models consider target cells as ‘hosts’ and viral particles as ‘macroparasites’, and allow viral output and infected cell lifespans, among other phenotypes, to depend on a cell’s multiplicity of infection. We show with an application to influenza that these models can be statistically fit to viral load and other within-host data, and demonstrate using model selection approaches that they have the ability to outperform traditional within-host viral dynamic models. Important in vivo quantities such as the mean multiplicity of cellular infection and time-evolving reassortant frequencies can also be quantified in a straightforward manner once these macroparasite models have been parameterized. The within-host model structure we develop here provides a mathematical way forward to address questions related to the roles of cellular coinfection, collective viral interactions, and viral complementation in within-host viral dynamics and evolution.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS2006

Studying Host-Pathogen Interactions In 3-D: Organotypic Models For Infectious Disease And Drug Development

Representative, reproducible and high-throughput models of human cells and tissues are critical for a meaningful evaluation of host-pathogen interactions and are an essential component of the research developmental pipeline. The most informative infection models - animals, organ explants and human trials - are not suited for extensive evaluation of pathogenesis mechanisms and screening of candidate drugs. At the other extreme, more cost effective and accessible infection models such as conventional cell culture and static co-culture may not capture physiological and three-dimensional aspects of tissue biology that are important in assessing pathogenesis, and effectiveness and cytotoxicity of therapeutics. Our lab has used innovative bioengineering technology to establish biologically meaningful 3-D models of human tissues that recapitulate many aspects of the differentiated structure and function of the parental tissue in vivo, and we have applied these models to study infectious disease. We have established a variety of different 3-D models that are currently being used in infection studies - including small intestine, colon, lung, placenta, bladder, periodontal ligament, and neuronal models. Published work from our lab has shown that our 3-D models respond to infection with bacterial and viral pathogens in ways that reflect the infection process in vivo. By virtue of their physiological relevance, 3-D cell cultures may also hold significant potential as models to provide insight into the neuropathogenesis of HIV infection. Furthermore, the experimental flexibility, reproducibility, cost-efficiency, and high throughput platform afforded by these 3-D models may have important implications for the design and development of drugs with which to effectively treat neurological complications of HIV infection.

Nickerson, Cheryl A.↗
DOE OSTI2021

Multiscale Airborne Infectious Disease Transmission

Airborne disease transmission is central to many scientific disciplines, including agriculture, veterinary biosafety, medicine, and public health. Legal and regulatory standards are in place to prevent agricultural, nosocomial, and community airborne disease transmission. However, the overall importance of the airborne pathway is underappreciated; e.g., the U.S. National Library of Medicine’s Medical Subjects Headings (MESH) thesaurus lacks an airborne disease transmission indexing term. This has practical consequences, as airborne precautions to control epidemic disease spread may not be taken when airborne transmission is important but unrecognized. Publishing clearer practical methodological guidelines for surveillance studies and disease outbreak evaluations could help address this situation. To inform future work, this paper highlights selected, well-established airborne transmission events, largely cases replicated in multiple, independently conducted scientific studies. Methodologies include field experiments, modeling, epidemiology studies, disease outbreak investigations, and mitigation studies. Collectively, this literature demonstrates that airborne viruses, bacteria, and fungal pathogens have the ability to cause disease in plants, animals, and humans over multiple distances, from near range (<5 m) to continental (>500 km) in scale. The plausibility and implications of undetected airborne disease transmission are discussed, including the notable underreporting of disease burdens for several airborne-transmitted diseases.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2017

A Universal Biosensor for Infectious Disease

With increased travel and globalization, the spread of new diseases has become a threat to global health—and global security. Whether in a rural village or an urban medical clinic, healthcare workers need diagnostics that provide answers then and there, for any disease, in order to effectively treat individual patients or widespread outbreaks. That’s why Harshini Mukundan and her team at Los Alamos National Laboratory are working to develop a universal biosensor. “If we are able to mimic the body’s immune recognition in the laboratory, we could have a universal strategy for the early diagnosis of all infections,” said Mukundan. Our immune system recognizes pathogens, regardless of their origin, by identifying discrete signatures in the human host. Mukundan's team is working to imitate this ability in the laboratory, which could lead to a simple solution to diagnose all diseases and improve lives across the world.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2012

Developing Tools to Combat Emerging Infectious Diseases

Strategies for pre-symptomatic detection of pathogens, Inspired by innate immune recognition; Strategies for selective interrogation of bacterial viability, Inspired by siderophore-based iron sequestration mechanisms.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS1993

Infectious Disease risks associated with exposure to stressful environments

Multiple environmental factors asociated with space flight can increase the risk of infectious illness among crewmembers thereby adversely affecting crew health and mission success. Host defences can be impaired by multiple physiological and psychological stressors including: sleep deprivation, disrupted circadian rhythms, separation from family, perceived danger, radiation exposure, and possibly also by the direct and indirect effects of microgravity. Relevant human immunological data from isolated or stressful environments including spaceflight will be reviewed. Long-duration missions should include reliable hardware which supports sophisticated immunodiagnostic capabilities. Future advances in immunology and molecular biology will continue to provide therapeutic agents and biologic response modifiers which should effectively and selectively restore immune function which has been depressed by exposure to environmental stressors.

Meehan, Ichard T.↗
DOE OSTI2021

Control of airborne infectious disease in buildings: Evidence and research priorities

The evolution of SARS-CoV-2 virus has resulted in variants likely to be more readily transmitted through respiratory aerosols, underscoring the increased potential for indoor environmental controls to mitigate risk. Use of tight-fitting face masks to trap infectious aerosol in exhaled breath and reduce inhalation exposure to contaminated air is of critical importance for disease control. Administrative controls including the regulation of occupancy and interpersonal spacing are also important, while presenting social and economic challenges. Indoor engineering controls including ventilation, exhaust, air flow control, filtration, and disinfection by germicidal ultraviolet irradiation can reduce reliance on stringent occupancy restrictions. However, the effects of controls-individually and in combination-on reducing infectious aerosol transfer indoors remain to be clearly characterized to the extent needed to support widespread implementation by building operators. We review aerobiologic and epidemiologic evidence of indoor environmental controls against transmission and present a quantitative aerosol transfer scenario illustrating relative differences in exposure at close-interactive, room, and building scales. We identify an overarching need for investment to implement building controls and evaluate their effectiveness on infection in well-characterized and real-world settings, supported by specific, methodological advances. Finally, improved understanding of engineering control effectiveness guides implementation at scale while considering occupant comfort, operational challenges, and energy costs.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Mechanistic modelling of SARS-CoV-2 and other infectious diseases and the effects of therapeutics

Modern viral kinetic modeling and its application to therapeutics is a field that attracted the attention of the medical, pharmaceutical and modeling communities during the early days of the AIDS epidemic. Its successes lead to applications of modeling methods not only to HIV but a plethora of other viruses, such as hepatitis C virus (HCV), hepatitis B virus (HBV) and cytomegalovirus (CMV), which along with HIV cause chronic diseases, and viruses such as influenza, respiratory syncytial virus (RSV), West Nile virus (WNV), Zika virus, and SAR-CoV-2, which generally cause acute infections. Here we first review the historical development of mathematical models to understand HIV and HCV infections and the effects of treatment by fitting the models to clinical data. Furthermore, we then focus on recent efforts and contributions of applying these models towards understanding SARS-CoV-2 infection and highlight outstanding questions where modeling can provide crucial insights and help to optimize non-pharmaceutical and pharmaceutical interventions of the COVID-19 pandemic. The review is written from our personal perspective emphasizing the power of simple target cell limited models that provided important insights and then their evolution into more complex models that captured more of the virology and immunology. To quote Albert Einstein “Everything should be made as simple as possible, but not simpler”, and this idea underlies the modeling we describe below.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2017

EpiK: A Knowledge Base for Epidemiological Modeling and Analytics of Infectious Diseases

Computational epidemiology seeks to develop computational methods to study the distribution and determinants of health-related states or events (including disease), and the application of this study to the control of diseases and other health problems. Recent advances in computing and data sciences have led to the development of innovative modeling environments to support this important goal. The datasets used to drive the dynamic models as well as the data produced by these models presents unique challenges owing to their size, heterogeneity and diversity. These datasets form the basis of effective and easy to use decision support and analytical environments. As a result, it is important to develop scalable data management systems to store, manage and integrate these datasets. In this paper, we develop EpiK —a knowledge base that facilitates the development of decision support and analytical environments to support epidemic science. An important goal is to develop a framework that links the input as well as output datasets to facilitate effective spatio-temporal and social reasoning that is critical in planning and intervention analysis before and during an epidemic. The data management framework links modeling workflow data and its metadata using a controlled vocabulary. The metadata captures information about storage, the mapping between the linked model and the physical layout, and relationships to support services. EpiK is designed to support agent-based modeling and analytics frameworks—aggregate models can be seen as special cases and are thus supported. We use semantic web technologies to create a representation of the datasets that encapsulates both the location and the schema heterogeneity. The choice of RDF as a representation language is motivated by the diversity and growth of the datasets that need to be integrated. A query bank is developed—the queries capture a broad range of questions that can be posed and answered during a typical case study pertaining to disease outbreaks. The queries are constructed using SPARQL Protocol and RDF Query Language (SPARQL) over the EpiK. EpiK can hide schema and location heterogeneity while efficiently supporting queries that span the computational epidemiology modeling pipeline: from model construction to simulation output. As a result, we show that the performance of benchmark queries varies significantly with respect to the choice of hardware underlying the database and resource description framework (RDF) engine.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Verifying infectious disease scenario planning for geographically diverse populations

In the face of the COVID-19 pandemic, the literature saw a spike in publications for epidemic models, and a renewed interest in capturing contact networks and geographic movement of populations. There remains a general lack of consensus in the modeling community around best practices for spatiotemporal epi-modeling, specifically as it pertains to the infection rate formulation and the underlying contact or mixing model. We mathematically verify several common modeling assumptions in the literature, to prove when certain choices can provide consistent results across different geographic resolutions, population densities and patterns, and mixing assumptions. The most common infection rate formulation, a computationally low cost per capita infection rate assumption, fails the consistency tests for heterogeneous populations and gravity-weighting assumptions. Future modeling efforts in spatiotemporal disease modeling should be wary of this limitation, particularly when working with more heterogeneous or sparse populations. Our results provide guidance for testing that a model preserves desirable properties even when model inputs mask potential problems due to symmetry or homogeneity. We also provide a recipe for performing this type of verification, strengthening decision support tools.

59 BASIC BIOLOGICAL SCIENCES↗