The Epidemic of Covid-19 in Africa: Demographic Effect, Under-Reporting of Cases, Dynamical Complexity and Mitigation Strategies Impact

2021 ◽  
Author(s):  
Sylvain Mangiarotti ◽  
Nathan Thenon ◽  
Marisa Peyre ◽  
Mireille Huc ◽  
François Roger
Keyword(s):  
Mitigation Strategies ◽  
Demographic Effect ◽  
Dynamical Complexity

scholarly journals Deer movement and resource selection during Hurricane Irma: implications for extreme climatic events and wildlife

2019 ◽  
Vol 286 (1916) ◽  
pp. 20192230 ◽  
Author(s):  
H. N. Abernathy ◽  
D. A. Crawford ◽  
E. P. Garrison ◽  
R. B. Chandler ◽  
M. L. Conner ◽  
...  
Keyword(s):  
Resource Selection ◽  
Global Climate ◽  
Field Studies ◽  
Mitigation Strategies ◽  
Home Ranges ◽  
Gps Collars ◽  
Behavioural Responses ◽  
Extreme Climatic Events ◽  
Demographic Effect ◽  
Hurricane Irma

Extreme climatic events (ECEs) are increasing in frequency and intensity and this necessitates understanding their influence on organisms. Animal behaviour may mitigate the effects of ECEs, but field studies are rare because ECEs are infrequent and unpredictable. Hurricane Irma made landfall in southwestern Florida where we were monitoring white-tailed deer ( Odocoileus virginianus seminolus ) with GPS collars. We report on an opportunistic case study of behavioural responses exhibited by a large mammal during an ECE, mitigation strategies for reducing the severity of the ECE effects, and the demographic effect of the ECE based on known-fate of individual animals. Deer altered resource selection by selecting higher elevation pine and hardwood forests and avoiding marshes. Most deer left their home ranges during Hurricane Irma, and the probability of leaving was inversely related to home range area. Movement rates increased the day of the storm, and no mortality was attributed to Hurricane Irma. We suggest deer mobility and refuge habitat allowed deer to behaviourally mitigate the negative effects of the storm, and ultimately, aid in survival. Our work contributes to the small but growing body of literature linking behavioural responses exhibited during ECEs to survival, which cumulatively will provide insight for predictions of a species resilience to ECEs and improve our understanding of how behavioural traits offset the negative impacts of global climate change.

SEISMIC RISK AND RESILIENCE ASSESSMENT OF INDUSTRIAL FACILITIES: CASE STUDY ON A BLACK CARBON PLANT

2020 ◽  
Author(s):  
George Karagiannakis
Keyword(s):  
Seismic Risk ◽  
Numerical Models ◽  
Fragility Curves ◽  
Human Life ◽  
Mitigation Strategies ◽  
Economic Losses ◽  
Risk And Resilience ◽  
Industrial Plants ◽  
Plant Equipment

This paper deals with state of the art risk and resilience calculations for industrial plants. Resilience is a top priority issue on the agenda of societies due to climate change and the all-time demand for human life safety and financial robustness. Industrial plants are highly complex systems containing a considerable number of equipment such as steel storage tanks, pipe rack-piping systems, and other installations. Loss Of Containment (LOC) scenarios triggered by past earthquakes due to failure on critical components were followed by severe repercussions on the community, long recovery times and great economic losses. Hence, facility planners and emergency managers should be aware of possible seismic damages and should have already established recovery plans to maximize the resilience and minimize the losses. Seismic risk assessment is the first step of resilience calculations, as it establishes possible damage scenarios. In order to have an accurate risk analysis, the plant equipment vulnerability must be assessed; this is made feasible either from fragility databases in the literature that refer to customized equipment or through numerical calculations. Two different approaches to fragility assessment will be discussed in this paper: (i) code-based Fragility Curves (FCs); and (ii) fragility curves based on numerical models. A carbon black process plant is used as a case study in order to display the influence of various fragility curve realizations taking their effects on risk and resilience calculations into account. Additionally, a new way of representing the total resilience of industrial installations is proposed. More precisely, all possible scenarios will be endowed with their weighted recovery curves (according to their probability of occurrence) and summed together. The result is a concise graph that can help stakeholders to identify critical plant equipment and make decisions on seismic mitigation strategies for plant safety and efficiency. Finally, possible mitigation strategies, like structural health monitoring and metamaterial-based seismic shields are addressed, in order to show how future developments may enhance plant resilience. The work presented hereafter represents a highly condensed application of the research done during the XP-RESILIENCE project, while more detailed information is available on the project website https://r.unitn.it/en/dicam/xp-resilience.

Validation to Spanish version of the COVID-19 Stress Scale

2020 ◽  
Author(s):  
Edgar Guillermo Pulido
Keyword(s):  
Mental Health ◽  
Goodness Of Fit ◽  
National Sample ◽  
Mitigation Strategies ◽  
Dimensional Structure ◽  
Psychosocial Effects ◽  
Stress Scale ◽  
Global Consensus ◽  
Reliability Indicators ◽  
Effective Assessment

There is a global consensus about the potential of the COVID-19 pandemic to affect people's mental health. In this context, and prior to the formulation of mitigation strategies, tools are required that allow an objective and effective assessment of mental health risk. The purpose of this study was to evaluate the psychometric characteristics of COVID-19 Stress Scale, formulated by Taylor et al. (2020), based on the concept of COVID-19 Stress Syndrome. A national sample of 1214 participating adults was taken in Colombia, who answered a translated version of the scale. Evidence of a hexa-dimensional structure was obtained whose goodness of fit indicators were Chi2 = 1215,759, Sig. =. 000, CMIN / DF = 2.202, RMSEA = .044, NFI = .943, TLI = .964, CFI =. 968 and FMIN = 1,967. Regarding reliability, an α = .924 and Spearman-Brown = .824 were obtained for the entire scale; the reliability indicators of the 6 subscales were also high. The similarities and differences in the findings with respect to the original psychometric study of the scale are discussed, as well as the utility and importance of the instrument as a tool in future efforts to mitigate the psychosocial effects of the pandemic.

A MODELING SYSTEM FOR CLIMATE CHANGE RISK ASSESSMENT, MANAGEMENT AND HEDGING IN COASTAL AREAS

2017 ◽  
Author(s):  
Sergei Soldatenko ◽  
Sergei Soldatenko ◽  
Genrikh Alekseev ◽  
Genrikh Alekseev ◽  
Alexander Danilov ◽  
...  
Keyword(s):  
Climate Change ◽  
Risk Assessment ◽  
Coastal Areas ◽  
Mitigation Strategies ◽  
System Structure ◽  
The Arctic ◽  
Risk Modeling ◽  
Wide Range ◽  
Adverse Weather ◽  
The Impact

Every aspect of human operations faces a wide range of risks, some of which can cause serious consequences. By the start of 21st century, mankind has recognized a new class of risks posed by climate change. It is obvious, that the global climate is changing, and will continue to change, in ways that affect the planning and day to day operations of businesses, government agencies and other organizations and institutions. The manifestations of climate change include but not limited to rising sea levels, increasing temperature, flooding, melting polar sea ice, adverse weather events (e.g. heatwaves, drought, and storms) and a rise in related problems (e.g. health and environmental). Assessing and managing climate risks represent one of the most challenging issues of today and for the future. The purpose of the risk modeling system discussed in this paper is to provide a framework and methodology to quantify risks caused by climate change, to facilitate estimates of the impact of climate change on various spheres of human activities and to compare eventual adaptation and risk mitigation strategies. The system integrates both physical climate system and economic models together with knowledge-based subsystem, which can help support proactive risk management. System structure and its main components are considered. Special attention is paid to climate risk assessment, management and hedging in the Arctic coastal areas.

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