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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.

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At least 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Analytics for Investigation of Disease Outbreaks (AIDO)

Objective: Analytics for the Investigation of Disease Outbreaks (AIDO) is a web-based tool designed to enhance a user’s understanding of unfolding infectious disease events. A representative library of over 650 outbreaks across a wide selection of diseases allows similar outbreaks to be matched to the conditions entered by the user. These historic outbreaks contain detailed information on how the disease progressed as well as what measures were implemented to control its spread, allowing for a better understanding within the context of other outbreaks.Introduction: Situational awareness, or the understanding of elemental components of an event with respect to both time and space, is critical for public health decision-makers during an infectious disease outbreak. AIDO is a web-based tool designed to contextualize incoming infectious disease information during an unfolding event for decision-making purposes.Methods: Public health analysts of the Biology Division at Los Alamos National Laboratory curated a diverse library of historic disease outbreaks from publicly available official reports and peer reviewed literature to serve as a representation of the range of potential outbreak scenarios for a given disease. Available outbreak metadata are used to identify properties that relate to the magnitude and/or duration of the outbreak. Properties vary by disease, as they are related to disease-specific characteristics like transmission, disease manifestation, risk factors related to disease severity, and environmental factors specific to the given location. These properties are then incorporated into a similarity algorithm (s in Figure 1) to identify outbreaks that are similar to user inputs.Results: AIDO currently includes libraries for 39 diseases that are diverse across pathogen type (viral, bacterial and parasitic) as well as transmission type (vectorborne (e.g., Dengue, Malaria), foodborne (e.g., Salmonella, Campylobacteriosis), waterborne (e.g., Cholera), and person-to-person transmitted (e.g., Measles)). In addition to providing a similarity score to the user’s outbreak, we provide aggregated comparisons to multiple historical outbreaks, descriptive statistics to show the distribution of property values for each disease, and extensive contextual information about each outbreak.Conclusions: The analytics provided by AIDO allow users to interact with a unique data set of historic outbreaks and the associated metadata to contextualize incoming information and generate hypotheses about appropriate decisions. The tool is continually updated with new functionalities and additional data.

Daughton, Ashlynn↗
DOE OSTI2016

A meta-analysis of the association between gender and protective behaviors in response to respiratory epidemics

Respiratory infectious disease epidemics and pandemics are recurring events that levy a high cost on individuals and society. The health-protective behavioral response of the public plays an important role in limiting respiratory infectious disease spread. Health-protective behaviors take several forms. Behaviors can be categorized as pharmaceutical (e.g., vaccination uptake, antiviral use) or non-pharmaceutical (e.g., hand washing, face mask use, avoidance of public transport). Due to the limitations of pharmaceutical interventions during respiratory epidemics and pandemics, public health campaigns aimed at limiting disease spread often emphasize both non-pharmaceutical and pharmaceutical behavioral interventions. Understanding the determinants of the public’s behavioral response is crucial for devising public health campaigns, providing information to parametrize mathematical models, and ultimately limiting disease spread. While other reviews have qualitatively analyzed the body of work on demographic determinants of health-protective behavior, this meta-analysis quantitatively combines the results from 85 publications to determine the global relationship between gender and health-protective behavioral response. The results show that women in the general population are about 50% more likely than men to adopt/practice non-pharmaceutical behaviors. Conversely, men in the general population are marginally (about 12%) more likely than women to adopt/practice pharmaceutical behaviors. It is possible that factors other than pharmaceutical/non-pharmaceutical status not included in this analysis act as moderators of this relationship. We find these results suggest an inherent difference in how men and women respond to epidemic and pandemic respiratory infectious diseases. In conclusion, this information can be used to target specific groups when developing non-pharmaceutical public health campaigns and to parameterize epidemic models incorporating demographic information.

60 APPLIED LIFE SCIENCES↗
NASA NTRSDate not supplied

Effects of Spaceflight Relevant Carbon Dioxide Levels on Pathogenesis Related Microbial Characteristics

Multiple stressors in the spaceflight environment, including microgravity and radiation, have the potential to alter microbial pathogenesis and virulence characteristics. Understanding how these and other factors may exacerbate the risk of infectious disease for astronauts is important to ensure the success of the mission. One understudied question in our assessment of infectious disease risk is the contribution carbon dioxide (CO2) at atmospheric concentrations higher than would be observed in terrestrial settings. Previous terrestrial studies have shown altered gene expression, selective bacterial inhibition, increased growth and diversity, and increased antibiotic resistance of bacterial communities individually and in biofilms when exposed to increased levels of CO2. While many of these findings are based on levels of CO2 higher than would be found during spaceflight exploration missions, the potential for subtle changes in CO2 levels to exacerbate infectious disease risks warrants an evaluation of pathogen responses to spaceflight CO2 conditions. To address this question, we are evaluating the effects of elevated atmospheric CO2 on microbial concentration, diversity, and pathogenesis-related characteristics using medically significant microorganisms cultured at CO2 levels representing terrestrial atmospheric concentration (≈0.04 %), ISS elevated concentration (0.4 %), and an unexpectedly elevated concentration (1 %) to represent a worst-case scenario. To determine if microbial responses to these CO2 concentrations may be exacerbated in response to the spaceflight environment, microorganisms are being cultured under static conditions and both spaceflight analogue and control conditions using the Rotating Wall Vessel (RWV) bioreactor (Synthecon, Houston, TX). For these studies, we will evaluate the response of Staphylococcus aureus, Streptococcus pneumoniae, and Enterobacter aerogenes, which are opportunistic bacterial pathogens that are of medical significance and have been or are likely to be found aboard human-inhabited spacecraft. Bacteria will be evaluated individually and together as a consortium. The following Specific Aims are be investigated: Aim 1. Characterize the growth, diversity, and pathogenesis-related stress responses of medically significant microorganisms grown under multiple CO2 concentrations under static growth in a liquid medium. Stress responses include acid stress, oxidative stress, and thermal stress. Aim 2. Characterize the growth, diversity, and pathogenesis-related stress responses of medically significant microorganisms grown under multiple CO2 concentrations in both spaceflight analogue and control conditions. Stress responses include acid stress, oxidative stress, and thermal stress.

S G Thornhill↗
NASA NTRS2012

Use of Giovanni System in Public Health Application

The role of environment and climate in propagating infectious disease has long been recognized since the 5th century. The effect is particularly evident in vector-borne diseases such as malaria where temperature, precipitation and humidity influence the lifecycle of the pathogens and mosquitoes. Likewise, the transmission of respiratory diseases is also often associated with climatic factors. For example, a recent study showed that low humidity and temperature provides efficient condition for seasonal influenza transmission. Understanding of how environment and climate affect infectious diseases would essentially provide guides to prevent and control the spread of disease. Toward this end, our group has developed models for infectious disease risk such as for malaria, dengue and influenza that are driven by climatic and environmental inputs. Results will be presented, especially those that used TRMM data from GIOVANNI.

Soebiyanto, Radina↗
DOE OSTI2017

Control and Cost-benefit Analysis of Fast Spreading Diseases: The case of Ebola

Mitigating the spread of infectious disease is of great importance for policy makers. Taking the recent outbreak of Ebola as an example, it was difficult for policy makers to identify the best course of action based on the cost-effectiveness of what was available. In effort to address the needs of policy makers to mitigate the spread of infectious disease before an outbreak becomes uncontrollable, we have devised a cost-benefit disease control model to simulate the effect of various control methods on disease incidence and the cost associated with each of the scenarios. Here, we present a case study of Ebola used to quantify the cost effectiveness of vaccination and isolation methods to minimize the spread of the disease. We evaluate the impact of changing strategy levels on the incidence of the disease and address the benefits of choosing one strategy over the other with regards to cost of vaccine and isolation. Disease. We use a general SEIRJ model for disease transmission. Here, S-Susceptible, E- Exposed (latent), IA– Infected (asymptomatic),IM– Infected (mild symptoms), IS– Infected (severe symptoms),JM– Isolated (mild symptoms at home), JS– Isolated (severe symptoms in hospital), and R- Recovered individuals. In this model, we consider the dynamics of the system and the effect of the relative transmissibility of isolated individuals (L) compared to other infected individuals. Cost. Ebola vaccination and treatment are very expensive and not widely available. Some preliminary data shows that it will take$73 million (M) to produce 27 M vaccines plus the cost for vaccine delivery and health care professionals (not included here). On the other hand, the treatment for Ebola in the U.S. would cost $25,000dollars a day per person to ensure proper isolation and adequate care(treatment, health care professionals, facilities and special equipment).Although not included in this research, the proper isolation of Ebola patients would also lead to a loss in hospital revenue of $148,000per day due to reduced patient capacity3. Here, we use $27,000 per individual hospitalized per day and $2.70 per person vaccinated. Model. To evaluate the cost-effectiveness of control methods on disease transmission, we assessed the affect of different levels of vaccination coverage on the resulting number of infected individuals. Then, we calculated the overall estimated cost of vaccination and resulting hospitalization for each scenario to identify the lowest cost-benefit ratio. Using a base population of 10 M individuals, we ran scenarios for different levels of vaccination (μ= 0.01, 0.05, 0.1) while varying the relative transmissibility of isolated individuals (L = 0.5, 0.6, 0.65).For each combination, we calculated the incidence, vaccination and hospitalization cost per individual per day (Fig 1). We note that an increase in the relative transmissibility of isolated individuals leads to a higher number of infected people and, therefore, a reduced number of candidates for vaccination and an overall increase in cost. Since the cost of vaccination is 1 ten-thousandth of the cost of hospitalization, our results clearly show the cost-benefit of vaccinating over hospital treatment. In every scenario studied, we observed a measurable reduction in disease incidence when vaccinating a higher fraction of the population compared to isolating individuals post infection. Given these preliminary results, we plan to extend the framework of our model to a dynamic control system where we consider the cost of vaccination and isolation embedded in the system of differential equations. This approach will allow us see the best available control implementation while minimizing the cost of treatment and vaccination.

EBOLA↗
DOE OSTI2023

Predictive performance of multi-model ensemble forecasts of COVID-19 across European nations

Background: Short-term forecasts of infectious disease burden can contribute to situational awareness and aid capacity planning. Based on best practice in other fields and recent insights in infectious disease epidemiology, one can maximise the predictive performance of such forecasts if multiple models are combined into an ensemble. Here, we report on the performance of ensembles in predicting COVID-19 cases and deaths across Europe between 08 March 2021 and 07 March 2022. Methods: We used open-source tools to develop a public European COVID-19 Forecast Hub. We invited groups globally to contribute weekly forecasts for COVID-19 cases and deaths reported by a standardised source for 32 countries over the next 1–4 weeks. Teams submitted forecasts from March 2021 using standardised quantiles of the predictive distribution. Each week we created an ensemble forecast, where each predictive quantile was calculated as the equally-weighted average (initially the mean and then from 26th July the median) of all individual models’ predictive quantiles. We measured the performance of each model using the relative Weighted Interval Score (WIS), comparing models’ forecast accuracy relative to all other models. We retrospectively explored alternative methods for ensemble forecasts, including weighted averages based on models’ past predictive performance. Results: Over 52 weeks, we collected forecasts from 48 unique models. We evaluated 29 models’ forecast scores in comparison to the ensemble model. We found a weekly ensemble had a consistently strong performance across countries over time. Across all horizons and locations, the ensemble performed better on relative WIS than 83% of participating models’ forecasts of incident cases (with a total N=886 predictions from 23 unique models), and 91% of participating models’ forecasts of deaths (N=763 predictions from 20 models). Across a 1–4 week time horizon, ensemble performance declined with longer forecast periods when forecasting cases, but remained stable over 4 weeks for incident death forecasts. In every forecast across 32 countries, the ensemble outperformed most contributing models when forecasting either cases or deaths, frequently outperforming all of its individual component models. Among several choices of ensemble methods we found that the most influential and best choice was to use a median average of models instead of using the mean, regardless of methods of weighting component forecast models. Conclusions: Our results support the use of combining forecasts from individual models into an ensemble in order to improve predictive performance across epidemiological targets and populations during infectious disease epidemics. Our findings further suggest that median ensemble methods yield better predictive performance more than ones based on means. Our findings also highlight that forecast consumers should place more weight on incident death forecasts than incident case forecasts at forecast horizons greater than 2 weeks. Funding: AA, BH, BL, LWa, MMa, PP, SV funded by National Institutes of Health (NIH) Grant 1R01GM109718, NSF BIG DATA Grant IIS-1633028, NSF Grant No.: OAC-1916805, NSF Expeditions in Computing Grant CCF-1918656, CCF-1917819, NSF RAPID CNS-2028004, NSF RAPID OAC-2027541, US Centers for Disease Control and Prevention 75D30119C05935, a grant from Google, University of Virginia Strategic Investment Fund award number SIF160, Defense Threat Reduction Agency (DTRA) under Contract No. HDTRA1-19-D-0007, and respectively Virginia Dept of Health Grant VDH-21-501-0141, VDH-21-501-0143, VDH-21-501-0147, VDH-21-501-0145, VDH-21-501-0146, VDH-21-501-0142, VDH-21-501-0148. AF, AMa, GL funded by SMIGE - Modelli statistici inferenziali per governare l'epidemia, FISR 2020-Covid-19 I Fase, FISR2020IP-00156, Codice Progetto: PRJ-0695. AM, BK, FD, FR, JK, JN, JZ, KN, MG, MR, MS, RB funded by Ministry of Science and Higher Education of Poland with grant 28/WFSN/2021 to the University of Warsaw. BRe, CPe, JLAz funded by Ministerio de Sanidad/ISCIII. BT, PG funded by PERISCOPE European H2020 project, contract number 101016233. CP, DL, EA, MC, SA funded by European Commission - Directorate-General for Communications Networks, Content and Technology through the contract LC-01485746, and Ministerio de Ciencia, Innovacion y Universidades and FEDER, with the project PGC2018-095456-B-I00. DE., MGu funded by Spanish Ministry of Health / REACT-UE (FEDER). DO, GF, IMi, LC funded by Laboratory Directed Research and Development program of Los Alamos National Laboratory (LANL) under project number 20200700ER. DS, ELR, GG, NGR, NW, YW funded by National Institutes of General Medical Sciences (R35GM119582; the content is solely the responsibility of the authors and does not necessarily represent the official views of NIGMS or the National Institutes of Health). FB, FP funded by InPresa, Lombardy Region, Italy. HG, KS funded by European Centre for Disease Prevention and Control. IV funded by Agencia de Qualitat i Avaluacio Sanitaries de Catalunya (AQuAS) through contract 2021-021OE. JDe, SMo, VP funded by Netzwerk Universitatsmedizin (NUM) project egePan (01KX2021). JPB, SH, TH funded by Federal Ministry of Education and Research (BMBF; grant 05M18SIA). KH, MSc, YKh funded by Project SaxoCOV, funded by the German Free State of Saxony. Presentation of data, model results and simulations also funded by the NFDI4Health Task Force COVID-19 ( https://www.nfdi4health.de/task-force-covid-19-2 ) within the framework of a DFG-project (LO-342/17-1). LP, VE funded by Mathematical and Statistical modelling project (MUNI/A/1615/2020), Online platform for real-time monitoring, analysis and management of epidemic situations (MUNI/11/02202001/2020); VE also supported by RECETOX research infrastructure (Ministry of Education, Youth and Sports of the Czech Republic: LM2018121), the CETOCOEN EXCELLENCE (CZ.02.1.01/0.0/0.0/17-043/0009632), RECETOX RI project (CZ.02.1.01/0.0/0.0/16-013/0001761). NIB funded by Health Protection Research Unit (grant code NIHR200908). SAb, SF funded by Wellcome Trust (210758/Z/18/Z).

60 APPLIED LIFE SCIENCES↗
DOE OSTI2014

Global disease monitoring and forecasting with Wikipedia

Infectious disease is a leading threat to public health, economic stability, and other key social structures. Efforts to mitigate these impacts depend on accurate and timely monitoring to measure the risk and progress of disease. Traditional, biologically-focused monitoring techniques are accurate but costly and slow; in response, new techniques based on social internet data, such as social media and search queries, are emerging. These efforts are promising, but important challenges in the areas of scientific peer review, breadth of diseases and countries, and forecasting hamper their operational usefulness. We examine a freely available, open data source for this use: access logs from the online encyclopedia Wikipedia. Using linear models, language as a proxy for location, and a systematic yet simple article selection procedure, we tested 14 location-disease combinations and demonstrate that these data feasibly support an approach that overcomes these challenges. Specifically, our proof-of-concept yields models with up to 0.92, forecasting value up to the 28 days tested, and several pairs of models similar enough to suggest that transferring models from one location to another without re-training is feasible. Based on these preliminary results, we close with a research agenda designed to overcome these challenges and produce a disease monitoring and forecasting system that is significantly more effective, robust, and globally comprehensive than the current state of the art.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2011

Infectious bursal disease virus activates the phosphatidylinositol 3-kinase (PI3K)/Akt signaling pathway by interaction of VP5 protein with the p85{alpha} subunit of PI3K

Phosphatidylinositol 3-kinase (PI3K)/Akt signaling is commonly activated upon virus infection and has been implicated in the regulation of diverse cellular functions such as proliferation and apoptosis. The present study demonstrated for the first time that infectious bursal disease virus (IBDV), the causative agent of a highly contagious disease in chickens, can induce Akt phosphorylation in cultured cells, by a mechanism that is dependent on PI3K. Inhibition of PI3K activation greatly enhanced virus-induced cytopathic effect and apoptotic cell death as evidenced by cleavage of poly-ADP ribose polymerase and activation of caspase-3. Investigations into the mechanism of PI3K/Akt activation revealed that IBDV activates PI3K/Akt signaling through binding of the non-structural protein VP5 to regulatory subunit p85{alpha} of PI3K resulting in the suppression of premature apoptosis and improved virus growth after infection. The results presented here provide a basis for understanding molecular mechanism of IBDV infection.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Improving Detection of Disease Re-emergence Using a Web-Based Tool (RED Alert): Design and Case Analysis Study

Background: Currently, the identification of infectious disease re-emergence is performed without describing specific quantitative criteria that can be used to identify re-emergence events consistently. This practice may lead to ineffective mitigation. In addition, identification of factors contributing to local disease re-emergence and assessment of global disease re-emergence require access to data about disease incidence and a large number of factors at the local level for the entire world. This paper presents Re-emerging Disease Alert (RED Alert), a web-based tool designed to help public health officials detect and understand infectious disease re-emergence. Objective: Our objective is to bring together a variety of disease-related data and analytics needed to help public health analysts answer the following 3 primary questions for detecting and understanding disease re-emergence: Is there a potential disease re-emergence at the local (country) level? What are the potential contributing factors for this re-emergence? Is there a potential for global re-emergence? Methods: We collected and cleaned disease-related data (eg, case counts, vaccination rates, and indicators related to disease transmission) from several data sources including the World Health Organization (WHO), Pan American Health Organization (PAHO), World Bank, and Gideon. We combined these data with machine learning and visual analytics into a tool called RED Alert to detect re-emergence for the following 4 diseases: measles, cholera, dengue, and yellow fever. We evaluated the performance of the machine learning models for re-emergence detection and reviewed the output of the tool through a number of case studies. Results: Our supervised learning models were able to identify 82%-90% of the local re-emergence events, although with 18%-31% (except 46% for dengue) false positives. This is consistent with our goal of identifying all possible re-emergences while allowing some false positives. The review of the web-based tool through case studies showed that local re-emergence detection was possible and that the tool provided actionable information about potential factors contributing to the local disease re-emergence and trends in global disease re-emergence. Conclusions: To the best of our knowledge, this is the first tool that focuses specifically on disease re-emergence and addresses the important challenges mentioned above.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

19-LW-045 Full Length Final Report. Molecular Mechanisms of Bacterial Pathogenesis: Waging the Arms Race with Superbugs

As the current global pandemic makes abundantly clear, we need a better understanding of infectious disease to safeguard human health, the economy and global security. Modern omics techniques hold the promise of providing a comprehensive understanding of the molecular mechanisms of life, including causes of pathogenesis from infectious disease at the molecular level, but we there is a serious gap in annotation of gene function. For as much as half of the genes and gene products encoded in genomes the molecular and/or cellular function is unknown or only partially understood. Recent innovations in fluorescence microscopy for live cell imaging and genetic engineering make it possible to determine the temporal correlation between molecular events, such as a gene being expressed due to host-pathogen interaction, and cellular events, such as bacterial invasion of immune cells. This is turn allows us to gain new insight as to the molecular and cellular role of individual genes and will enable the discovery and validation of new molecular mechanisms essential for infectious disease. Knowing the molecular mechanisms of disease processes will provide new therapeutic targets or novel countermeasure strategies. We aimed to develop a lattice light sheet fluorescence microscope as a unique resource at LLNL for long time course live cell imaging experiments; to develop the reagents and cell lines needed to monitor molecular events during the course pathogenic bacteria infecting mammalian immune cells; and to demonstrate that we could capture molecular events during an infection. We fully commissioned the LLNL lattice light sheet microscope and conducted initial proof of principle imaging experiments on mammalian immune cells and pathogenic bacteria. It is clear from the experience gained that long time course live cell imaging has tremendous potential to help elucidate molecular mechanisms of host-pathogen interactions and to help annotate gene function, which would establish a basis for new countermeasures. It is also clear that if live cell imaging is to realize its full potential new data processing and analysis tools will need to be developed to facilitate analysis of molecular events within cells; new sample chambers and stages could facilitate studies with a wider range of cell and tissue types; and alternative molecular tagging methods need to be explored to enable more facile engineering of cells labeled with molecular specificity.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2015

Advanced Diagnostic and Sample Preparation Platform for Early Threat Surveillance

Emerging infectious diseases present a profound threat to global health, economic development, and political stability, and therefore represent a significant national security concern for the United States. The increased prevalence of international travel and globalized trade further amplify the threat of infectious disease outbreaks of catastrophic effect. The key to containing and eradicating an outbreak before it goes global is rapid identification of index cases and initial clusters of affected individuals. This depends upon establishment of a biosurveillance network that effectively reaches infectious disease hotspots in even the most remote regions of the world and provides a network-integrated, location-appropriate diagnostic capability. At present, there are two critical needs which must be addressed in order to extend biosurveillance activities beyond centralized laboratory facilities: 1) A simple, reliable, and safe method for immediate stabilization of clinical specimens in the field; and 2) A flexible sample processing platform that enables in-field preparation of clinical specimens for rapid, on-site analysis using a variety of diagnostic assay platforms. These needs are not necessarily mutually exclusive; in fact, we propose that they are most efficiently addressed by a deployable sample processing platform that immediately stabilizes the information content of clinical specimens through transformation of the inherently unstable analytes of interest into stable equivalents that are appropriately formatted for downstream analysis. In order to address this problem, we have developed a sample processing pipeline and microfluidics-based platform modules enabling: 1) Extraction of total RNA from finger-stick quantities of human whole blood; and 2) Microscale synthesis of appropriately-formatted cDNA products that capture the information content of blood RNA in a stable form that supports pathogen detection and/or characterization via PCR and/or Second Generation Sequencing (SGS). Through this research we have discovered new, effective solutions for problems that thus far have hindered use of digital microfluidics (DMF) in biomedical applications. Our work reveals a clear path forward to fieldable, automated sample processing systems that will enable rapid, on-site identification of usual-suspect and novel pathogens in clinical specimens for improved biosurveillance.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2012

Connecting Network Properties of Rapidly Disseminating Epizoonotics

Background: To effectively control the geographical dissemination of infectious diseases, their properties need to be determined. To test that rapid microbial dispersal requires not only susceptible hosts but also a pre-existing, connecting network, we explored constructs meant to reveal the network properties associated with disease spread, which included the road structure. Methods: Using geo-temporal data collected from epizoonotics in which all hosts were susceptible (mammals infected by Foot-and-mouth disease virus, Uruguay, 2001; birds infected by Avian Influenza virus H5N1, Nigeria, 2006), two models were compared: 1) ‘connectivity’, a model that integrated bio-physical concepts (the agent’s transmission cycle, road topology) into indicators designed to measure networks (‘nodes’ or infected sites with short- and long-range links), and 2) ‘contacts’, which focused on infected individuals but did not assess connectivity. Results: The connectivity model showed five network properties: 1) spatial aggregation of cases (disease clusters), 2) links among similar ‘nodes’ (assortativity), 3) simultaneous activation of similar nodes (synchronicity), 4) disease flows moving from highly to poorly connected nodes (directionality), and 5) a few nodes accounting for most cases (a ‘‘20:800 pattern). In both epizoonotics, 1) not all primary cases were connected but at least one primary case was connected, 2) highly connected, small areas (nodes) accounted for most cases, 3) several classes of nodes were distinguished, and 4) the contact model, which assumed all primary cases were identical, captured half the number of cases identified by the connectivity model. When assessed together, the synchronicity and directionality properties explained when and where an infectious disease spreads. Conclusions: Geo-temporal constructs of Network Theory’s nodes and links were retrospectively validated in rapidly disseminating infectious diseases. They distinguished classes of cases, nodes, and networks, generating information usable to revise theory and optimize control measures. Prospective studies that consider pre-outbreak predictors, such as connecting networks, are recommended.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2016

Science in 60 - The Forecast Calls for Flu

What if we could forecast infectious diseases the same way we forecast the weather, and predict how diseases like Dengue, Typhus, or Zika were going to spread? Using real-time data from Wikipedia and social media, Sara Del Valle and her team from Los Alamos National Laboratory have developed a global disease-forecasting system that will improve the way we respond to epidemics. Using this model, individuals and public health officials can monitor disease incidence and implement strategies — such as vaccination campaigns, communicating to the public and allocating resources — to stay one step ahead of infectious disease spread.

60 APPLIED LIFE SCIENCES↗
NASA NTRSDate not supplied

SPACEFLIGHT-INDUCED CHANGES IN MICROBIAL VIRULENCE AND THE IMPACT TO THE HOST IMMUNE RESPONSE

INTRODUCTION Over the past 50 years, microorganisms have displayed unexpected responses relevant to infectious disease when grown in microgravity and microgravity analogue environments, including changes in final cell concentration, biofilm production, stress resistance, antibiotic sensitivity, gene expression, and virulence. In parallel, astronaut studies have characterized a persistent spaceflight-induced dysregulation of the human immune system; consisting of altered leukocyte distribution, reductions in T and NK cell function, altered cytokine profiles, and reactivation of latent herpesviruses. Further, astronauts have some degree of clinical incidence, primarily infectious disease episodes and atopic dermatitis. The impact of the microgravity environment on host-pathogen interactions and potential for clinical disease remains understudied and poorly characterized. SPECIFIC AIMS In the Host-Microorganism, Microbial Response aspect of this study, the following Specific Aims are being investigated, using the microbial pathogens, Salmonella enterica Enteritidis, Pseudomonas aeruginosa, Burkholderia cepacia, Streptococcus pneumoniae, and enterohemorrhagic Escherichia coli. Aim 1: Characterize the effect of spaceflight analogue culture on microbial pathogenesis-related stress responses and in vitro host-pathogen interactions. Analysis will include microbial stress responses as well as colonization and viability following pathogen challenge of three-dimensional (3-D) tissue co-culture models containing immune cells. We specifically selected obligate and opportunistic pathogens that are medically important and have been or are likely to be found aboard spacecraft. Aim 2: Characterize the effect of spaceflight analogue culture on the virulence potential of pathogenic microorganisms. Changes in virulence will be assessed using a mouse model of infection. SIGNIFICANCE The goal of this study is to use spaceflight analogue conditions to gain insight into the breadth of medically-significant International Space Station microorganisms that have altered virulence and the impact of those changes on the immune response of the host, including astronaut immune cells. This information will provide critical understanding into the impact of microgravity on potential alterations in microbial virulence and associated infectious disease risk to crew health during spaceflight missions.

C. M. Ott↗
NASA NTRSDate not supplied

Spaceflight-induced Changes in Microbial Virulence and the Impact to the Host Immune Response

INTRODUCTION Over the past 50 years, many microorganisms have displayed unexpected responses relevant to infectious disease when grown in microgravity and microgravity analogue environments, including changes in stress resistance, biofilm production, antibiotic sensitivity, final cell concentration, gene expression, enhanced host-pathogen interaction, and virulence. In parallel, astronaut studies have characterized a persistent spaceflight-induced dysregulation of the human immune system; consisting of altered leukocyte distribution, reductions in T and NK cell function, altered cytokine profiles, and reactivation of latent herpesviruses. Further, astronauts have some degree of clinical incidence, primarily infectious disease episodes and atopic dermatitis. The impact of the microgravity environment on host-pathogen interactions and potential for clinical disease remains understudied and poorly characterized. SPECIFIC AIMS In this study, the following Specific Aims are being investigated, using the microbial pathogens, Salmonella enterica Enteritidis, Pseudomonas aeruginosa, Burkholderia cepacia, Streptococcus pneumoniae, and enterohemorrhagic Escherichia coli (EHEC). Aim 1: Characterize the effect of spaceflight analogue culture on microbial pathogenesis-related stress responses and in vitro host-pathogen interactions. Analyses include microbial stress responses as well as colonization and viability following pathogen challenge of three-dimensional (3-D) tissue co-culture models containing immune cells. We specifically selected obligate and opportunistic pathogens that are medically important and have been or are likely to be found aboard spacecraft. Aim 2: Characterize the effect of spaceflight analogue culture on the virulence potential of pathogenic microorganisms. Virulence will be assessed using a mouse model of infection. SIGNIFICANCE The goal of this study is to use spaceflight analogue conditions to gain insight into the breadth of medically-significant International Space Station microorganisms that have altered virulence and the impact of those changes on the immune response of the host. This information will provide critical understanding into the impact of microgravity on potential alterations in microbial virulence and associated infectious disease risk to crew health during spaceflight missions.

C M Ott↗
DOE OSTI2019

Achieving Health Security and Threat Reduction through Sharing Sequence Data

With the rapid development and broad applications of next-generation sequencing platforms and bioinformatic analytical tools, genomics has become a popular area for biosurveillance and international scientific collaboration. Governments from countries including the United States (US), Canada, Germany, and the United Kingdom have leveraged these advancements to support international cooperative programs that aim to reduce biological threats and build scientific capacity worldwide. A recent conference panel addressed the impacts of the enhancement of genomic sequencing capabilities through three major US bioengagement programs on international scientific engagement and biosecurity risk reduction. Here, the panel contrasted the risks and benefits of supporting the enhancement of genomic sequencing capabilities through international scientific engagement to achieve biological threat reduction and global health security. The lower costs and new bioinformatic tools available have led to the greater application of sequencing to biosurveillance. Strengthening sequencing capabilities globally for the diagnosis and detection of infectious diseases through mutual collaborations has a high return on investment for increasing global health security. International collaborations based on genomics and shared sequence data can build and leverage scientific networks and improve the timeliness and accuracy of disease surveillance reporting needed to identify and mitigate infectious disease outbreaks and comply with international norms. Further efforts to promote scientific transparency within international collaboration will improve trust, reduce threats, and promote global health security.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

The Differential Equations That Model Diseases

In this brief tutorial, I will describe the simplest way of modeling a deadly infectious disease, such as COVID-19. I will show that at early times, the disease grows exponentially, but at late times, falls off like a Bell curve. I will solve the equations numerically with Python. A common mathematical model is the so-called compartmental model, where diseases can move people between "categories" such as healthy, infectious, or truly sick. This is a large, well established field and there are many good resources on this topic. I started with the SIAM review article The mathematics of infectious diseases. However, there are many excellent textbooks and more modern reviews as well.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2014

Disease mapping based on stochastic SIR-SI model for Dengue and Chikungunya in Malaysia

This paper describes and demonstrates a method for relative risk estimation which is based on the stochastic SIR-SI vector-borne infectious disease transmission model specifically for Dengue and Chikungunya diseases in Malaysia. Firstly, the common compartmental model for vector-borne infectious disease transmission called the SIR-SI model (susceptible-infective-recovered for human populations; susceptible-infective for vector populations) is presented. This is followed by the explanations on the stochastic SIR-SI model which involve the Bayesian description. This stochastic model then is used in the relative risk formulation in order to obtain the posterior relative risk estimation. Then, this relative estimation model is demonstrated using Dengue and Chikungunya data of Malaysia. The viruses of these diseases are transmitted by the same type of female vector mosquito named Aedes Aegypti and Aedes Albopictus. Finally, the findings of the analysis of relative risk estimation for both Dengue and Chikungunya diseases are presented, compared and displayed in graphs and maps. The distribution from risk maps show the high and low risk area of Dengue and Chikungunya diseases occurrence. This map can be used as a tool for the prevention and control strategies for both diseases.

60 APPLIED LIFE SCIENCES↗