scholarly journals DDRP: Real-time phenology and climatic suitability modeling of invasive insects

PLoS ONE ◽  
2020 ◽  
Vol 15 (12) ◽  
pp. e0244005
Author(s):  
Brittany S. Barker ◽  
Leonard Coop ◽  
Tyson Wepprich ◽  
Fritzi Grevstad ◽  
Gericke Cook
Keyword(s):  
United States ◽  
Decision Support ◽  
Real Time ◽  
Decision Support Tool ◽  
Insect Species ◽  
Climatic Suitability ◽  
Degree Days ◽  
Support Tool ◽  
Invasive Insect ◽  
Management Actions

Rapidly detecting and responding to new invasive species and the spread of those that are already established is essential for reducing their potential threat to food production, the economy, and the environment. We describe a new spatial modeling platform that integrates mapping of phenology and climatic suitability in real-time to provide timely and comprehensive guidance for stakeholders needing to know both where and when invasive insect species could potentially invade the conterminous United States. The Degree-Days, Risk, and Phenological event mapping (DDRP) platform serves as an open-source and relatively easy-to-parameterize decision support tool to help detect new invasive threats, schedule monitoring and management actions, optimize biological control, and predict potential impacts on agricultural production. DDRP uses a process-based modeling approach in which degree-days and temperature stress are calculated daily and accumulate over time to model phenology and climatic suitability, respectively. Outputs include predictions of the number of completed generations, life stages present, dates of phenological events, and climatically suitable areas based on two levels of climate stress. Species parameter values can be derived from laboratory and field studies or estimated through an additional modeling step. DDRP is written entirely in R, making it flexible and extensible, and capitalizes on multiple R packages to generate gridded and graphical outputs. We illustrate the DDRP modeling platform and the process of model parameterization using two invasive insect species as example threats to United States agriculture: the light brown apple moth, Epiphyas postvittana, and the small tomato borer, Neoleucinodes elegantalis. We then discuss example applications of DDRP as a decision support tool, review its potential limitations and sources of model error, and outline some ideas for future improvements to the platform.

scholarly journals DDRP: real-time phenology and climatic suitability modeling of invasive insects

2020 ◽  
Author(s):  
Brittany S. Barker ◽  
Leonard Coop ◽  
Tyson Wepprich ◽  
Fritzi Grevstad ◽  
Gericke Cook
Keyword(s):  
United States ◽  
Decision Support ◽  
Real Time ◽  
Decision Support Tool ◽  
Insect Species ◽  
Climatic Suitability ◽  
Degree Days ◽  
Support Tool ◽  
Invasive Insect ◽  
Management Actions

AbstractRapidly detecting and responding to new invasive species and the spread of those that are already established is essential for reducing their potential threat to food production, the economy, and the environment. We describe a new multi-species spatial modeling platform that integrates mapping of phenology and climatic suitability in real-time to provide timely and comprehensive guidance for stakeholders needing to know both where and when invasive insect species could potentially invade the conterminous United States. The Degree-Days, Risk, and Phenological event mapping (DDRP) platform serves as an open-source and relatively easy-to-parameterize decision support tool to help detect new invasive threats, schedule monitoring and management actions, optimize biological control, and predict potential impacts on agricultural production. DDRP uses a process-based modeling approach in which degree-days and temperature stress are calculated daily and accumulate over time to model phenology and climatic suitability, respectively. Products include predictions of the number of completed generations, life stages present, dates of phenological events, and climatically suitable areas based on two levels of climate stress. Species parameter values can be derived from laboratory and field studies, and from published and newly fitted CLIMEX models. DDRP is written entirely in R, making it flexible and extensible, and capitalizes on multiple R packages to generate gridded and graphical outputs. We illustrate the DDRP modeling platform and the process of model parameterization using two invasive insect species as example threats to United States agriculture: the light brown apple moth, Epiphyas postvittana, and the small tomato borer, Neoleucinodes elegantalis. We then discuss example applications of DDRP as a decision support tool, review its potential limitations and sources of model error, and outline some ideas for future improvements to the platform.

Lessons learned from the implementation of the near real-time S2S4E Decision Support Tool

2021 ◽  
Author(s):  
Lluís Palma ◽  
Andrea Manrique ◽  
Llorenç Lledó ◽  
Andria Nicodemou ◽  
Pierre-Antoine Bretonnière ◽  
...  
Keyword(s):  
Decision Support ◽  
Real Time ◽  
Climate Models ◽  
Decision Support Tool ◽  
Lessons Learned ◽  
Cloud Infrastructure ◽  
Time Service ◽  
Support Tool ◽  
Wide Range ◽  
Prediction Systems

<p>Under the context of the H2020 S2S4E project, industrial and research partners co-developed a fully-operational Decision Support Tool (DST) providing during 18 months near real-time subseasonal and seasonal  forecasts tailored to the specific needs of the renewable energy sector. The tool aimed to breach the last mile gap between climate information and the end-user by paying attention to the interaction with agents from the sector, already used to work with weather information, and willing to extend their forecasting horizon by incorporating climate predictions into their daily operations.</p><p>With this purpose, the tool gathered a heterogeneous dataset of seven different essential climate variables and nine energy indicators, providing for each of them bias-adjusted probabilistic information paired with a reference skill metric. To achieve this, data from state-of-the-art prediction systems and reanalysis needed to be downloaded and post-processed, fulfilling a set of quality requirements that ensure the proper functioning of the operational service. During the design, implementation, and testing phases, a wide range of scientific and technical choices had to be made, making clear the difficulties of transferring scientific research to a user-oriented real-time service. A brief showcase will be presented, exemplifying the different tools, methodologies, and best practices applied to the data workflow, together with a case study performed in Oracle’s cloud infrastructure. We expect that by making a clear description of the process and the problems encountered, we will provide a valuable experience for both, upcoming attempts of similar implementations, and the organizations providing data from climate models and reanalysis.</p>

Interrater Reliability, Accuracy, and Triage Time Pre- and Post-implementation of a Real-Time Electronic Triage Decision-Support Tool

2020 ◽  
Vol 75 (4) ◽  
pp. 524-531 ◽  
Author(s):  
Shelley L. McLeod ◽  
Joy McCarron ◽  
Tamer Ahmed ◽  
Keerat Grewal ◽  
Nicole Mittmann ◽  
...  
Keyword(s):  
Decision Support ◽  
Real Time ◽  
Interrater Reliability ◽  
Decision Support Tool ◽  
Support Tool ◽  
Triage Decision ◽  
Triage Time

scholarly journals Reducing Wastage of Inhalation Anesthetics Using Real-time Decision Support to Notify of Excessive Fresh Gas Flow

2013 ◽  
Vol 118 (4) ◽  
pp. 874-884 ◽  
Author(s):  
Bala G. Nair ◽  
Gene N. Peterson ◽  
Moni B. Neradilek ◽  
Shu Fang Newman ◽  
Elaine Y. Huang ◽  
...  
Keyword(s):  
Decision Support ◽  
Real Time ◽  
Decision Rule ◽  
Gas Flow ◽  
Decision Rules ◽  
Decision Support Tool ◽  
Information Management System ◽  
Inhalation Anesthetics ◽  
Support Tool ◽  
The Mean

Abstract Background: Reduced consumption of inhalation anesthetics can be safely achieved by reducing excess fresh gas flow (FGF). In this study the authors describe the use of a real-time decision support tool to reduce excess FGF to lower, less wasteful levels. Method: The authors applied a decision support tool called the Smart Anesthesia Manager™ (University of Washington, Seattle, WA) that analyzes real-time data from an Anesthesia Information Management System to notify the anesthesia team if FGF exceeds 1 l/min. If sevoflurane consumption reached 2 minimum alveolar concentration-hour under low flow anesthesia (FGF < 2 l/min), a second message was generated to increase FGF to 2 l/min, to comply with Food and Drug Administration guidelines. To evaluate the tool, mean FGF between surgical incision and the end of procedure was compared in four phases: (1) a baseline period before instituting decision rules, (2) Intervention-1 when decision support to reduce FGF was applied, (3) Intervention-2 when the decision rule to reduce flow was deliberately inactivated, and (4) Intervention-3 when decision rules were reactivated. Results: The mean ± SD FGF reduced from 2.10 ± 1.12 l/min (n = 1,714) during baseline to 1.60 ± 1.01 l/min (n = 2,232) when decision rules were instituted (P < 0.001). When the decision rule to reduce flow was inactivated, mean FGF increased to 1.87 ± 1.15 l/min (n = 1,732) (P < 0.001), with an increasing trend in FGF of 0.1 l/min/month (P = 0.02). On reactivating the decision rules, the mean FGF came down to 1.59 ± 1.02 l/min (n = 1,845). Through the Smart Anesthesia Messenger™ system, the authors saved 9.5 l of sevoflurane, 6.0 l of desflurane, and 0.8 l isoflurane per month, translating to an annual savings of $104,916. Conclusions: Real-time notification is an effective way to reduce inhalation agent usage through decreased excess FGFs.

scholarly journals Real-time reconciled simulation as decision support tool for process operation

2021 ◽  
Vol 100 ◽  
pp. 41-64
Author(s):  
Anibal Galan ◽  
Cesar De Prada ◽  
Gloria Gutierrez ◽  
Daniel Sarabia ◽  
Rafael Gonzalez
Keyword(s):  
Decision Support ◽  
Real Time ◽  
Decision Support Tool ◽  
Support Tool ◽  
Process Operation

scholarly journals Bridging the Text-Image Gap: a Decision Support Tool for Real-Time PACS Browsing

2011 ◽  
Vol 25 (2) ◽  
pp. 227-239 ◽  
Author(s):  
Merlijn Sevenster ◽  
Rob van Ommering ◽  
Yuechen Qian
Keyword(s):  
Decision Support ◽  
Real Time ◽  
Decision Support Tool ◽  
Support Tool
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