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All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Moving Beyond Mortality: Development and Application of a Desirability of Outcome Ranking (DOOR) Endpoint for Hospital-Acquired Bacterial Pneumonia and Ventilator-Associated Bacterial Pneumonia

Abstract Background Hospital-acquired and ventilator-associated bacterial pneumonia (HABP/VABP) are frequently caused by multidrug-resistant organisms. Patient-centered endpoints in clinical trials are needed to develop new antibiotics for HABP/VABP. Desirability of outcome ranking (DOOR) is a paradigm for the design, analysis, and interpretation of clinical trials based on a patient-centered, benefit-risk evaluation. Methods A multidisciplinary committee created an infectious diseases DOOR endpoint customized for HABP/VABP, incorporating infectious complications, serious adverse events, and mortality. We applied this to 2 previously completed, large randomized controlled trials for HABP/VABP. ZEPHyR compared vancomycin to linezolid and VITAL compared linezolid to tedizolid. For each trial, we evaluated the DOOR distribution and probability, including DOOR component and partial credit analyses. We also applied DOOR in subgroup analyses. Results In both trials, the HABP/VABP DOOR demonstrated similar overall clinical outcomes between treatment groups. In ZEPHyR, the probability that a participant treated with linezolid would have a more desirable outcome than a participant treated with vancomycin was 50.2% (95% confidence interval [CI], 45.1%­−55.3%). In VITAL, the probability that a participant treated with tedizolid would have a more desirable outcome than a participant treated with linezolid was 48.7% (95% CI, 44.8%–52.6%). The DOOR component analysis revealed that participants treated with tedizolid had a less desirable outcome than those treated with linezolid when considering clinical response alone. However, participants with decreased renal function had improved overall outcomes with tedizolid. Conclusions The HABP/VABP DOOR provided more granular information about clinical outcomes than is typically presented in clinical trials. HABP/VABP trials would benefit from prospectively using DOOR.

Immunology↗
NASA NTRS2000

Integrated Orbiter/International Space Station Air Quality Analysis for Post-Mission 2A.1 Risk Mitigation

Even under the assumption that all Orbiter and ISS elements would be operating as designed, condensation control and crewmember comfort were paramount issues preceding each of the ISS Missions 2A and 2A.1. However, crewmember discomfort during Mission 2A.1, in addition to evidence provided by flight data and observations, suggested that countermeasures would be necessary for future flights where the Orbiter provides primary control. A review of Space Shuttle Program (SSP) and International Space Station Program (ISSP) assumptions and analytical methods that describe ECLS transport phenomena was accomplished in order to ensure the veracity of future analyses. Subsequently, various analyses and interpretations of Mission 2A.1 flight data provided the necessary information to help understand the probable causes of the flight environment. With cooperation between the SSP and ISSP, countermeasures were devised for Mission 2A.2 that would likely improve the delivery of conditioned air by the Orbiter to the ISS as well as within the ISS elements.

RISK MANAGEMENT↗
NASA NTRS2015

Reliability and Probabilistic Risk Assessment - How They Play Together

PRA methodology is one of the probabilistic analysis methods that NASA brought from the nuclear industry to assess the risk of LOM, LOV and LOC for launch vehicles. PRA is a system scenario based risk assessment that uses a combination of fault trees, event trees, event sequence diagrams, and probability and statistical data to analyze the risk of a system, a process, or an activity. It is a process designed to answer three basic questions: What can go wrong? How likely is it? What is the severity of the degradation? Since 1986, NASA, along with industry partners, has conducted a number of PRA studies to predict the overall launch vehicles risks. Planning Research Corporation conducted the first of these studies in 1988. In 1995, Science Applications International Corporation (SAIC) conducted a comprehensive PRA study. In July 1996, NASA conducted a two-year study (October 1996 - September 1998) to develop a model that provided the overall Space Shuttle risk and estimates of risk changes due to proposed Space Shuttle upgrades. After the Columbia accident, NASA conducted a PRA on the Shuttle External Tank (ET) foam. This study was the most focused and extensive risk assessment that NASA has conducted in recent years. It used a dynamic, physics-based, integrated system analysis approach to understand the integrated system risk due to ET foam loss in flight. Most recently, a PRA for Ares I launch vehicle has been performed in support of the Constellation program. Reliability, on the other hand, addresses the loss of functions. In a broader sense, reliability engineering is a discipline that involves the application of engineering principles to the design and processing of products, both hardware and software, for meeting product reliability requirements or goals. It is a very broad design-support discipline. It has important interfaces with many other engineering disciplines. Reliability as a figure of merit (i.e. the metric) is the probability that an item will perform its intended function(s) for a specified mission profile. In general, the reliability metric can be calculated through the analyses using reliability demonstration and reliability prediction methodologies. Reliability analysis is very critical for understanding component failure mechanisms and in identifying reliability critical design and process drivers. The following sections discuss the PRA process and reliability engineering in detail and provide an application where reliability analysis and PRA were jointly used in a complementary manner to support a Space Shuttle flight risk assessment.

Safie, Fayssal M.↗
DOE OSTI2023

In silico analyses of diversity and dissemination of antimicrobial resistance genes and mobile genetics elements, for plasmids of enteric pathogens

The antimicrobial resistance (AMR) mobilome plays a key role in the dissemination of resistance genes encoded by mobile genetics elements (MGEs) including plasmids, transposons (Tns), and insertion sequences (ISs). These MGEs contribute to the dissemination of multidrug resistance (MDR) in enteric bacterial pathogens which have been considered as a global public health risk. To further understand the diversity and distribution of AMR genes and MGEs across different plasmid types, we utilized multiple sequence-based computational approaches to evaluate AMR-associated plasmid genetics. A collection of 1,309 complete plasmid sequences from Gammaproteobacterial species, including 100 plasmids from each of the following 14 incompatibility (Inc) types: A/C, BO, FIA, FIB, FIC, FIIA, HI1, HI2, I1, K, M, N, P except W, where only 9 sequences were available, was extracted from the National Center for Biotechnology Information (NCBI) GenBank database using BLAST tools. The extracted FASTA files were analyzed using the AMRFinderPlus web-based tools to detect antimicrobial, disinfectant, biocide, and heavy metal resistance genes and ISFinder to identify IS/Tn MGEs within the plasmid sequences. In silico prediction based on plasmid replicon types showed that the resistance genes were diverse among plasmids, yet multiple genes were widely distributed across the plasmids from enteric bacterial species. These findings provide insights into the diversity of resistance genes and that MGEs mediate potential transmission of these genes across multiple plasmid replicon types. This notion was supported by the observation that many IS/Tn MGEs and resistance genes known to be associated with them were common across multiple different plasmid types. Our results provide critical insights about how the diverse population of resistance genes that are carried by the different plasmid types can allow for the dissemination of AMR across enteric bacteria. The results also highlight the value of computational-based approaches and in silico analyses for the assessment of AMR and MGEs, which are important elements of molecular epidemiology and public health outcomes.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Advancing Our Understanding of System Availability through the PV Fleet Performance Data Initiative

The PV Fleet Performance Data Initiative partners with photovoltaic (PV) fleet owners to collect time-series data of PV production data and publishes aggregated anonymized results of system performance metrics. With an extensive dataset drawn from over 2,200 PV systems across the United States, comprising 8.5 GW and 24,000 separate inverter data channels, this initiative aims to ensure that systemic risks in the US PV fleet are detected. The current work explores system availability, revealing a pronounced dependence on time, especially within the initial 6 months of system performance. Following this start-up period, the average system availability stabilizes. Statistical analyses illustrate a median (P5O) monthly availability of 0.991 and a dependence on system size with a negative trend in availability with increasing system size. This finding indicates that larger systems experience lower availability compared to their smaller counterparts.

inverter availability↗
NASA NTRS1979

Alsep data processing: How we processed Apollo Lunar Seismic Data

The Apollo lunar seismic station network gathered data continuously at a rate of 3 x 10 to the 8th power bits per day for nearly eight years until the termination in September, 1977. The data were processed and analyzed using a PDP-15 minicomputer. On the average, 1500 long-period seismic events were detected yearly. Automatic event detection and identification schemes proved unsuccessful because of occasional high noise levels and, above all, the risk of overlooking unusual natural events. The processing procedures finally settled on consist of first plotting all the data on a compressed time scale, visually picking events from the plots, transferring event data to separate sets of tapes and performing detailed analyses using the latter. Many problems remain especially for automatically processing extraterrestrial seismic signals.

Latham, G. V.↗
NASA NTRS1980

How we processed Apollo lunar seismic data

The Apollo lunar seismic station network gathered data continuously at a rate of 3 x 10 to the 8th bits per day for nearly eight years until termination in September, 1977. The data were processed and analyzed using a PDP-15 minicomputer. On average, 1500 long-period seismic events were detected yearly. Automatic event detection and identification schemes proved unsuccessful because of occasional high noise levels and, above all, the risk of overlooking unusual natural events. The processing procedures which were finally chosen consist of plotting all the data on a compressed time scale, visually picking events from the plots, transferring event data to separate sets of tapes and performing detailed analyses using the latter. Many problems remain, especially in the automatic processing of extraterrestrial seismic signals.

Nakamura, Y.↗
NASA NTRS1995

Propellant injection systems and processes

The previous 'Art of Injector Design' is maturing and merging with the more systematic 'Science of Combustion Device Analysis.' This technology can be based upon observation, correlation, experimentation and ultimately analytical modeling based upon basic engineering principles. This methodology is more systematic and far superior to the historical injector design process of 'Trial and Error' or blindly 'Copying Past Successes.' The benefit of such an approach is to be able to rank candidate design concepts for relative probability of success or technical risk in all the important combustion device design requirements and combustion process development risk categories before committing to an engine development program. Even if a single analytical design concept cannot be developed to predict satisfying all requirements simultaneously, a series of risk mitigation key enabling technologies can be identified for early resolution. Lower cost subscale or laboratory experimentation to demonstrate proof of principle, critical instrumentation requirements, and design discriminating test plans can be developed based on the physical insight provided by these analyses.

Ito, Jackson I.↗
NASA NTRS2023

Requirements Engineering Scorecard and the Next-Generation Space Suit

The objective for a NASA contractor, the performing organization in this case study, is to provide engineering services to develop and deliver the next generation space suit to NASA, the customer in this case study. A case study with qualitative and quantitative analyses regarding a new process and approach to requirements engineering is described, with the intent that if utilized, these tools may have contributed to improvements across the project in terms of meeting cost, scope, budget and quality while appropriately accounting for risk management. The procedure entails a research method in which the current state of the project, current state of the art, and the identified systems engineering challenges are evaluated. Iterative models are tempered through development by continual improvements by engineering evaluation of engineers on the project. The current results have produced a prototype of a requirements engineering scorecard with implementations of FMEA and quantitative analysis to (i) identify root cause of underdeveloped requirements and (ii) project management impacts with regards to project risk. Forward work includes customer, performing organization, acceptance against applicable INCOSE community accepted practices.

INCOSE↗
NASA NTRS2022

Lake Champlain Water Resources: Using Earth Observations to Identify Spatial and Seasonal Trends of Harmful Algal Events in Lake Champlain

Lake Champlain provides clean drinking water for 35% of the surrounding watershed and offers recreational opportunities to millions of tourists. However, current levels of cyanobacteria and phosphorus created breeding grounds for harmful algal blooms (HABs). The excess of phosphorus runoff into Lake Champlain over the past decade encouraged toxic cyanobacterial formations, thereby increasing the severity of HABs towards local economy and ecology. In partnership with the Natural Resources Conservation Service (NRCS) Northeast Region, this project utilized Earth observations to identify risk factors associated with toxic algal blooms. The team detected historic algal bloom trends with Sentinel-2 Multispectral Instrument (MSI), Sentinel-3 Ocean and Land Color Instrument (OLCI), Landsat 8 Operational Land Imager (OLI) and Landsat 9 OLI-2. The team also used Sentinel-3 OLCI and the German Aerospace Center’s Earth Sensing Imagery Spectrometer (DESIS) to visualize algal bloom patterns and Landsat 8 OLI, Landsat 9 OLI-2, and Shuttle Radar Topography Mission (SRTM) to identify phosphorus sources within the watershed. The team’s analyses indicated an increase in cyanobacteria blooms during summer months from 2016-2022, with Missisquoi and St. Albans Bay exhibiting the greatest concentrations of toxic events. Furthermore, 16% of the watershed was identified as posing an immediate threat to the lake’s hydrology. The area of greatest concern was the Missisquoi Bay sub-watershed, with 229,044 acres of land prone to excessive phosphorus runoff. Providing this information to the NRCS Northeast Region enabled the organization to quantify risk factors associated with algal blooms and modify mitigation efforts to better target future bloom events.

Brianne Kendall↗
DOE OSTI2021

Competitive TcO4-, IO3-, and CrO42- Incorporation into Ettringite

Ettringite is a naturally occurring mineral found in cementitious matrices that is known for its ability to incorporate environmentally mobile oxyanion contaminants. To better assess this immobilization mechanism for contaminants within cementitious waste forms intended for nuclear waste storage, this work explores how mixed oxyanion contaminants compete for ettringite incorporation and influence the evolving mineralogy. Ettringite was precipitated in the presence of TcO4-, IO3-, and/or CrO42-, known contaminants of concern to nuclear waste treatment, over pre-determined precipitation periods. Solution analyses quantified contaminant removal and the collected solid was characterized using bulk and microprobe XRD coupled with PDF and microprobe XRF analyses. Results suggest that =96% IO3- is removed from solution, regardless of ettringite precipitation time or the presence of TcO4- or CrO42-. However, TcO4- removal remained <20%, was not significantly improved with longer ettringite precipitation times, and significantly decreased in the presence of IO3-. When IO3- is comingled with CrO42, gypsum is formed as a secondary mineral phase, which results in oxyanion partitioning among mineral phases, e.g., IO3- incorporation into ettringite and CrO42- incorporation into gypsum via SO42- substitution. Results from this work exemplify the importance of competitive immobilization when assessing waste form performance and environmental risk of contaminant release.

Gillispie, Elizabeth C.↗
DOE OSTI2021

A global assessment of policy tools to support climate adaptation

Governments, businesses, and civil society organizations have diverse policy tools to incentivize adaptation. Policy tools can shape the type and extent of adaptation, and therefore, function either as barriers or enablers for reducing risk and vulnerability. Using data from a systematic review of academic literature on global adaptation responses to climate change (n = 1549 peer-reviewed articles), we categorize the types of policy tools used to shape climate adaptation. We apply qualitative and quantitative analyses to assess the contexts where particular tools are used, along with equity implications for groups targeted by the tools, and the tools’ relationships with transformational adaptation indicators such as the depth, scope, and speed of adaptation. We find diverse types of tools documented across sectors and geographic regions. We also identify a mismatch between the tools that consider equity and those that yield more transformational adaptations. Direct regulations, plans, and capacity building are associated with higher depth and scope of adaptation (thus transformational adaptation), while economic instruments, information provisioning, and networks are not; the latter tools, however, are more likely to target marginalized groups in their design and implementation. We identify multiple research gaps, including a need to assess instrument mixes rather than single tools and to assess adaptations that result from policy implementation. Key policy insights Information-based approaches, networks, and economic instruments are the most frequently documented adaptation policy tools worldwide. Direct regulations, plans, and capacity building are associated with higher depth and scope of adaptation, and thus more transformational adaptation. Capacity building, economic instruments, networks, and information provisioning approaches are more likely to target specific marginalized groups and thus equity challenges. There are many regions and sectors where certain tools are not widely documented (e.g. regulations and plans in Africa and Asia), representing a key research gap.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Novel CHI3L1 ‐Associated Angiogenic Phenotypes Define Glioma Microenvironments: Insights From Multi‐Omics Integration

ABSTRACT The CHI3L1 signaling pathway significantly influences glioma angiogenesis, but its role in the tumor microenvironment (TME) remains elusive. We propose a novelCHI3L1‐associated vascular phenotype classification for glioma through integrative analyses of multiple datasets with bulk and single‐cell transcriptome, genomics, digital pathology, and clinical data. We investigated the biological characteristics, genomic alterations, therapeutic vulnerabilities, and immune profiles within these phenotypes through a comprehensive multi‐omics approach. We constructed the vascular‐related risk (VR) score based onCHI3L1‐associated vascular signatures (CAVS) identified by machine learning algorithms. Utilizing unsupervised consensus clustering, gliomas were stratified into three distinct vascular phenotypes: Cluster A, marked by high vascularization and stromal activation with a relatively low levels of tumor‐infiltrating lymphocytes (TILs); Cluster B, characterized by moderate vascularization and stromal activity, coupled with a high density of TILs; and Cluster C, defined by low vascularization and sparse immune cell infiltration. We observed that the CAVS effectively indicated glioma‐associated angiogenesis and immune suppression by single‐cell RNA‐seq analysis. Moreover, the high‐VR‐score group exhibited enhanced angiogenic activity, reduced immune response, resistance to immunotherapy, and poorer clinical outcomes. The VR score independently predicted glioma prognosis and, combined with a nomogram, provided a robust clinical decision‐making tool. Potential drug prediction based on transcription factors for high‐risk patients was also performed. Our study reveals thatCHI3L1‐associated vascular phenotypes shape distinct immune landscapes in gliomas, offering insights for optimizing therapeutic strategies to improve patient outcomes.

Oncology↗
DOE OSTI2023

Genomic and Phenotypic Characterization of Shiga Toxin-Producing Escherichia albertii Strains Isolated from Wild Birds in a Major Agricultural Region in California

Escherichia albertii is an emerging foodborne pathogen. To better understand the pathogenesis and health risk of this pathogen, comparative genomics and phenotypic characterization were applied to assess the pathogenicity potential of E. albertii strains isolated from wild birds in a major agricultural region in California. Shiga toxin genes stx2f were present in all avian strains. Pangenome analyses of 20 complete genomes revealed a total of 11,249 genes, of which nearly 80% were accessory genes. Both core gene-based phylogenetic and accessory gene-based relatedness analyses consistently grouped the three stx2f-positive clinical strains with the five avian strains carrying ST7971. Among the three Stx2f-converting prophage integration sites identified, ssrA was the most common one. Besides the locus of enterocyte effacement and type three secretion system, the high pathogenicity island, OI-122, and type six secretion systems were identified. Substantial strain variation in virulence gene repertoire, Shiga toxin production, and cytotoxicity were revealed. Six avian strains exhibited significantly higher cytotoxicity than that of stx2f-positive E. coli, and three of them exhibited a comparable level of cytotoxicity with that of enterohemorrhagic E. coli outbreak strains, suggesting that wild birds could serve as a reservoir of E. albertii strains with great potential to cause severe diseases in humans.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS1990

A methodology for probabilistic prediction of structural failures of launch vehicle propulsion systems

A quantitative methodology for estimating the probability of occurrence of critical structural failure modes of launch vehicle propulsion systems during mission operation is presented. In this approach, engineering analysis models that characterize specific failure modes based on the physics and mechanics of the failure phenomenon are used in a prescribed probabalistic structure in which quantified uncertainty in the information used in the analysis and quantified uncertainty of the engineering analysis models themselves are both considered in estimating failure risk. The methodology is applicable to high cycle fatigue, low cycle fatigue, flaw propagation stress rupture, and other failure modes of launch vehicle propulsion systems and other space-flight systems. The probabalistic failure assessment presented provides the capability of performing quantitative analyses to rank options to improve reliability, thereby enabling limited financial resources of development and improvement programs to be more effectively allocated.

Moore, N.↗
NASA NTRS1998

Development of Algorithms and Error Analyses for the Short Baseline Lightning Detection and Ranging System

NASA, at the John F. Kennedy Space Center (KSC), developed and operates a unique high-precision lightning location system to provide lightning-related weather warnings. These warnings are used to stop lightning- sensitive operations such as space vehicle launches and ground operations where equipment and personnel are at risk. The data is provided to the Range Weather Operations (45th Weather Squadron, U.S. Air Force) where it is used with other meteorological data to issue weather advisories and warnings for Cape Canaveral Air Station and KSC operations. This system, called Lightning Detection and Ranging (LDAR), provides users with a graphical display in three dimensions of 66 megahertz radio frequency events generated by lightning processes. The locations of these events provide a sound basis for the prediction of lightning hazards. This document provides the basis for the design approach and data analysis for a system of radio frequency receivers to provide azimuth and elevation data for lightning pulses detected simultaneously by the LDAR system. The intent is for this direction-finding system to correct and augment the data provided by LDAR and, thereby, increase the rate of valid data and to correct or discard any invalid data. This document develops the necessary equations and algorithms, identifies sources of systematic errors and means to correct them, and analyzes the algorithms for random error. This data analysis approach is not found in the existing literature and was developed to facilitate the operation of this Short Baseline LDAR (SBLDAR). These algorithms may also be useful for other direction-finding systems using radio pulses or ultrasonic pulse data.

Starr, Stanley O.↗
NASA NTRS2022

Deriving Severe Hail Likelihood from Satellite Observations and Model Reanalysis Parameters using a Deep Neural Network

Geostationary satellite imagers, such as those of the Geostationary Operational Environmental Satellite (GOES) series, have been observing severe convection at 15–60-minute intervals for over 40 years. When properly assessed, such a data record can be valuable in efforts of estimating severe storm risk throughout the diurnal cycle based on automated detection of patterns consistently found atop severe storms. Furthermore, environmental conditions favorable for severe weather are well-known and are thought to be represented well by modern reanalysis products. Promoting resilience against such hazards on local and global scales is a chief goal the NASA Disasters program, which seeks to encourage use of satellite observations to mitigate risk. For instance, hail is the costliest severe weather hazard across the globe in terms of insured loss, but reporting inconsistencies for hail events globally make it difficult to develop models that can quantify the risk. Satellite observation and model reanalysis taken together have the potential to, with reasonable skill and specificity, characterize environmental conditions that are favorable for hazardous weather, and thereby enable creation of hazard climatologie. Such climatologies are particularly useful over regions without extensive radar networks or storm reporting. By mapping the multivariate combination of observed cloud features and reanalysis environmental parameters/indices to United States Next Generation Weather Radar (NEXRAD) radar-estimated Maximum Expected Size of Hail (MESH) by way of a deep neural network (DNN), estimates of likelihood for potentially severe hail can be produced. Such estimates are of greater complexity and efficiency than could be performed with previous multivariate or logistic regression analyses for observed points within convective systems. Statistical distributions of convective parameters from satellite and reanalysis are shown to highlight non-severe/severe class separation for well-known hailstorm predictors, e.g., overshooting cloud top characteristics, deep-layer wind shear, mid-level stability, helicity, and convective inhibition. These complex, multivariate predictor relationships are exploited within a DNN, which can efficiently produce a quantitative hail risk metric with better than 70% detection rate and under 30% false alarms. These hail classifications can then be aggregated across the satellite record to yield a hazard climatology for hail frequency and severity – knowledge of which is of particular interest to those who manage risk (e.g., insurers) and are seeking opportunities to identify hail-prone regions, particularly in developing nations. This NASA study uses satellite observations and model parameters in a DNN to perform climatological hailstorm analysis in support of catastrophe model development, with the hope of promoting risk resilience particularly in regions without adequate weather radar coverage.

Passive Remote Sensing↗
NASA NTRS2015

Epidemiologic and Environmental Risk Factors of Rift Valley Fever in Southern Africa from 2008 to 2011

Background: Rift Valley fever (RVF) outbreaks have been associated with periods of widespread and above normal rainfall over several months. Knowledge on the environmental factors influencing disease transmission dynamics has provided the basis for developing models to predict RVF outbreaks in Africa. From 2008 to 2011, South Africa experienced the worst wave of RVF outbreaks in almost 40 years. We investigated rainfall associated environmental factors in southern Africa preceding these outbreaks. Methods: RVF epizootic records obtained from the World Animal Health Information Database (WAHID), documenting livestock species affected, location, and time, were analyzed. Environmental variables including rainfall and satellite-derived normalized difference vegetation index (NDVI) data were collected and assessed in outbreak regions to understand the underlying drivers of the outbreaks. Results: The predominant domestic vertebrate species affected in 2008 and 2009 were cattle, when outbreaks were concentrated in the eastern provinces of South Africa. In 2010 and 2011, outbreaks occurred in the interior and southern provinces affecting over 16,000 sheep. The highest number of cases occurred between January and April but epidemics occurred in different regions every year, moving from the northeast of South Africa toward the southwest with each progressing year. The outbreaks showed a pattern of increased rainfall preceding epizootics ranging from 9 to 152 days; however, NDVI and rainfall were less correlated with the start of the outbreaks than has been observed in eastern Africa. Conclusions: Analyses of the multiyear RVF outbreaks of 2008 to 2011 in South Africa indicated that rainfall, NDVI, and other environmental and geographical factors, such as land use, drainage, and topography, play a role in disease emergence. Current and future investigations into these factors will be able to contribute to improving spatial accuracy of models to map risk areas, allowing adequate time for preparation and prevention before an outbreak occurs.

Rift Valley Fever (RVF)↗