World Congress on Risk 2015
19-23 July, 2015, Singapore

Online Program



Session Schedule & Abstracts


* Disclaimer: All presentations represent the views of the authors, and not the organizations that support their research. Please apply the standard disclaimer that any opinions, findings, and conclusions or recommendations in abstracts, posters, and presentations at the meeting are those of the authors and do not necessarily reflect the views of any other organization or agency. Meeting attendees and authors should be aware that this disclaimer is intended to apply to all abstracts contained in this document. Authors who wish to emphasize this disclaimer should do so in their presentation or poster. In an effort to make the abstracts as concise as possible and easy for meeting participants to read, the abstracts have been formatted such that they exclude references to papers, affiliations, and/or funding sources. Authors who wish to provide attendees with this information should do so in their presentation or poster.

Common abbreviations

Wednesday 22-07-2015

W3-E
Mortality Risk-Models and Methods

Room: Auditorium   13:30–15:00

Chair(s): Ben Greenfield



1    Modeling the environmental surface contribution to hospital-associated infection transmission. Greenfield B.K., Environmental Health Sciences Division, University of California-Berkeley; Jones R.M., Environmental and Occupational Health Sciences Department, University of Illinois at Chicago; Nicas M., Environmental Health Sciences Division, University of California-Berkeley; McKone T.E., Environmental Health Sciences Division, University of California-Berkeley   greenfieldben1@gmail.com (366)

Abstract: Mechanistic models of human disease transmission help health scientists understand which transmission pathways pose the greatest infection risks. For transmission of bacterial hospital-associated infections (HAI), we describe a quantitative framework for the relative importance of direct skin-to-skin contact versus indirect transfer via environmental textiles and surfaces. We applied an environmental disease transmission model to quantitatively compare transmission pathways among different bacterial taxa and strains. The model is a coupled set of mass-balance equations describing the contact and transmission between a health care worker (HCW) and an infected and an uninfected patient, residing in separate hospital rooms. The model was parameterized for three important HAI species: Staphylococcus aureus (SA), Streptococcus pyogenes (SP), and Acinetobacter baumannii (AB). Model outputs indicate the relative importance of bacterial residence on human skin versus on environmental surfaces, which varied substantially among species. Transfer to and from environmental surfaces contributed 70%, 49%, and 21% to final modeled bacteria count on the uninfected patient skin for AB, SA, and SP, respectively. This suggests that benefits of surface decontamination would similarly vary AB > SA > SP. Results are sensitive to skin and surface loss (die-off) rates of bacteria, suggesting a need to develop predictive models for this parameter. The study illustrates how quantifying environmental contribution to risk can aid in categorizing pathogens, and in prioritizing interventions.

2    Learning from the past, the 18th century Taiwan tsunami and the 19th century Tacloban typhoon. Switzer A.D., Earth Observatory of Singapore; Soria L. *, Earth Observatory of Singapore   aswitzer@ntu.edu.sg (205)

Abstract: The historical archives of southeast Asia is fragmentary history of the region can occasionally yield valuable risk information into the history of past floods, tsunamis and typhoons. In the first example several disparate historical records from southwestern Taiwan allude to a disastrous tsunami coast sometime in the late 18th century, that killed >40,000 people. However, despite consistent reports from southwest Taiwan no corroborative historical reports exist for neighboring shorelines in China or Philippines and a plausible source of this tsunami has not been identified. We compared a series of numerical models of tsunamis generated from different sources and our results indicate that a submarine-mass-failure offshore from southwestern Taiwan is the likely source of the 18th century event. A similar event today could generate large (>10 m), tsunamis that would affect the city of Kaohsiung and the Taiwan Nuclear Power Plant No. 3, at the southern tip of Taiwan. In the second example we consider the historical records of typhoons in Tacloban, Leyte, Philippines a city destroyed by super typhoon (ST) Haiyan in November 2013. Here historical records, point to a comparable predecessor to ST Haiyan, in 1897 (Ty 1897). A typhoon that took a similar path to ST Haiyan but slightly farther north and produced storm surges eerily comparable to ST Haiyan. Additionally the 1897 death toll reflects a similar fatality rate given the massive population growth between the two events. The historical record clearly shows that ST Haiyan’s storm surge was not unprecedented and implies that historical studies may have better prepared the coastal communities affected and questioning the proposed causal link between ST Haiyan and recent global climate change. Both these events typify the knowledge that can be gleaned from historical archives and underline the importance of understanding the dynamics and impact of historical events as a bridge to developing long term records as reference frames for future planning and hazard management.

3    Eliciting prospect theory with a focus on loss of life. Taheri E, Tsinghua University; Wang C, Tsinghua University   taherie10@mails.tsinghua.edu.cn (208)

Abstract: The medical decision-making literature has focused on decisions under risk involving individual patients’ quality of life. The present study aims to investigate decisions in the face of collective life losses; i.e., when the potential consequences are losing the lives of a group of people. In particular, we design a variety of emergency scenarios where decision makers are responsible for rescuing a group of people (e.g., who are trapped in fire), and conduct experiments to elicit the prospect theory components (e.g., probability weighting, reference point, and loss aversion). Preliminary results show that people are indeed risk seeking in the loss domain. Moreover, they attach different monetary values to human lives for different sizes of life losses.

4    Framework of advanced risk assessment of chemical substances used for energy carrier of hydrogen. Tsunemi K, National Institute of Advanced Industrial Science and Technology (AIST); Yoshida K, AIST; Ono K, AIST; Wada Y, AIST   k-tsunemi@aist.go.jp (162)

Abstract: Fuel cell vehicles are now going on the market, and the government is discussing the revision of laws of safety using hydrogen. The government and industry conducted hazard assessment of a hydrogen station under limited scenarios, and they only insisted enough safety of a hydrogen station. However, construction of infrastructure, such as hydrogen stations has been delayed due to the population anxiety about unexpected accident of hydrogen. Therefore, our research objective is to show damage and risk of hydrogen infrastructure and present risk reduction effects by measures in order to improve risk perception of hydrogen station. We are now conducting risk assessment of chemical substances used for energy carrier of hydrogen, such as ammonia, methylcyclohexane (MCH). First, we create a framework of an advanced risk assessment including accident scenario, exposure scenario, damage estimation and risk evaluation. Next, we create vulnerability curves of human health impact by leak and explosion of these chemicals in a hydrogen station. We also create exposure data of buildings and population around the hydrogen station. Then, we estimate damage of buildings and population around the hydrogen station. Furthermore, we estimate accident probability of leak and explosion of these chemicals in a hydrogen station. Finally, we evaluate human health risk of chemical leak and explosion, and integrate these risks using the indicator of quality of life (QOL).



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