Abildsnes, E., Paulsen, S., & Gonzalez, J. J. (2023). Improving resilience against a pandemic: A novel technology for strategy development with practitioners and decision-makers. In Jaziar Radianti, Ioannis Dokas, Nicolas Lalone, & Deepak Khazanchi (Eds.), Proceedings of the 20th International ISCRAM Conference (pp. 964–974). Omaha, USA: University of Nebraska at Omaha.
Abstract: The project Systemic Pandemic Risk Management (SPRM), funded by the Research Council of Norway, has developed methods to assess and manage pandemic systemic risks. The project consortium includes an enterprise leading the project, public partners and research institutions in Norway, Sweden, and Italy. Kristiansand municipality, a partner in the SPRM project, adopted the project methods to assess and manage systemic risks. Based on a scenario about the potential spread patterns of the COVID-19 Omicron variant developed by the Norwegian Institute of Public Health, staff from Kristiansand employed the SPRM project’s approach to facilitate systemic risk assessment and management workshops. Practitioners and decision-makers from the main hospital in the Agder county and several municipalities proposed risks, their causal consequences and identified practical and impactful mitigation strategies. The strategies were implemented at the county level. The approach can improve handling of systemic risk scenarios beyond pandemics.
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St. Denis, L. A., & Hughes, A. L. (2023). Use of Statistics in Disaster by Local Individuals: An Examination of Tweets during COVID-19. In Jaziar Radianti, Ioannis Dokas, Nicolas Lalone, & Deepak Khazanchi (Eds.), Proceedings of the 20th International ISCRAM Conference (pp. 449–458). Omaha, USA: University of Nebraska at Omaha.
Abstract: We report on how individuals local to the US state of Colorado used statistics in tweets to make sense of the early stages of the COVID-19 pandemic. Tweets provided insight into how people interpreted statistical data, sometimes incorrectly, which has implications for crisis responders tasked with understanding public perceptions and providing accurate information. With widespread concerns about the accuracy and quality of online information, we show how monitoring public reactions to and uses of statistics on social media is important for improving crisis communication. Findings suggest that statistics can be a powerful tool for making sense of a crisis and coping with the stress and uncertainty of a global, rapidly evolving event like the COVID-19 pandemic. We conclude with broader implications for how crisis responders might improve their communications around statistics to the public, and suggestions for how this research might be expanded to look at other types of disasters.
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Sterl, S., Almalla, N., & Gerhold, L. (2023). Conceptualizing a Pandemic Early Warning System Using Various Data: An Integrative Approach. In Jaziar Radianti, Ioannis Dokas, Nicolas Lalone, & Deepak Khazanchi (Eds.), Proceedings of the 20th International ISCRAM Conference (pp. 284–294). Omaha, USA: University of Nebraska at Omaha.
Abstract: Covid-19 demonstrated the vulnerability of various systems and showed, however, that digital tools and data can serve not only to stop infections but also to detect viruses before or immediately after a zoonosis has occurred, thus preventing a potential pandemic. Although several pandemic early warning systems (P-EWS) and German pandemic-related projects (G-PRP) exist, they often use a limited data range or rely on third-party data. Here, we present a concept of an integrative pandemic early warning system (IS-PAN) applied to Germany using various data such as health data (e.g., clinical/syndromic) or internet data (e.g., social media/apps). Based on a systematic literature research of P-EWS and G-PRP on scientific and public health platforms, we derived indicators that help to detect virus threats with a system consisting of modules monitored in parallel. By integrating various pre collected digital data, this approach can help to identify a potential health threat efficiently and effectively.
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Završnik, J., Vošner, H. B. žun, & Kokol, P. (2023). Pandemic crisis management: The EU project STAMINA. In Jaziar Radianti, Ioannis Dokas, Nicolas Lalone, & Deepak Khazanchi (Eds.), Proceedings of the 20th International ISCRAM Conference (p. 1070). Omaha, USA: University of Nebraska at Omaha.
Abstract: Pandemics, as COVID-19 showed, can have the potential to result in serious global health threats and crises. Management of such kind of crisis presents a serious challenge due to the number of affected people, differences in legal, administrative, health procedures, political cultures, and the lack of smart interconnected, and compatible digitalized software tolls. The aim of the STAMINA project, sponsored by EU, was to overcome the above challenges and support efficient and effective pandemic management by providing Artificial intelligence-based decision-support technology which could successfully operate at a regional, national, and global level. The project targeted three stages of the emergency management cycle: Prediction, Preparedness, and Response. The STAMINA solution provides national planners, regional crisis management agencies, first responders, and citizens with new tools as well as a clear guide to how they can be used in line with international standards and legislation.
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Vihan C.N. Weeraratne, Raymond C.Z. Cohen, Mahesh Prakash, Lalitha Ramachandran, Nikhil Garg, & Valentijn Pauwels. (2023). Assessing Climate Vulnerability Under Future Changes to Climate, Demographics and Infrastructure: A Case Study for the Chapel Street Precinct, Melbourne. In V. L. Thomas J. Huggins (Ed.), Proceedings of the ISCRAM Asia Pacific Conference 2022 (pp. 35–44). Palmerston North, New Zealand: Massey Unversity.
Abstract: The Chapel Street Precinct is a busy commercial and residential corridor in the City of Stonnington Local Government Area (LGA) located in metropolitan Melbourne, Australia. Authorities and planners in the LGA are interested in understanding how the changing climate affects the socioeconomic environment of the region. By considering existing climate hazards (such as extreme heat, flood and water availability), infrastructure, and demographic information in the region together with future projections of climate change and demographic changes, a Socioeconomic Vulnerability Index (SVI) was created at a Mesh Block scale to better identify relatively high-risk Mesh Blocks in the region. The climate projections under medium and high future emission scenarios (i.e., representative concentration pathways (RCP)) as per IPCC (Intergovernmental Panel on Climate Change) fifth assessment report (AR5), RCP4.5 and RCP8.5 respectively for 30-year epochs around 2030, 2050 and 2070 were used in the SVI development. The current-day scenario is considered under Baseline conditions for demographic and asset information representing present-day conditions, whereas the baseline climate dataset considers the climate for the 30 year period 1991-2020 to best represent the present-day climate. The multi-model mean of the future climate projections from 6 different climate models were obtained from the Victoria’s Future Climate tool (https://vicfutureclimatetool.indraweb.io), developed by CSIRO (Commonwealth Scientific and Industrial Research Organisation) Data61 together with the Department of Environment, Land, Water and Planning (DELWP) under Data61’s INDRA framework (https://research.csiro.au/indra/). A version of INDRA is currently under development to allow map-based interactivity, experimentation and scrutiny of the vulnerability indices and their subcomponents across the study region. The SVI was created using a weighted indicator approach utilising a range of indicators belonging to 3 categories, exposure, susceptibility, and baseline adaptive capacity. The indicators were first normalised and the final SVI was given a score between 0-1 for each Mesh Block. The worst levels of vulnerability were observed to be for the RCP8.5 2070 scenario. In general, the RCP8.5 scenarios indicated a worse outcome compared to the RCP4.5 scenario. The area along Chapel Street within the precinct which is a densely built-up area high in population was found to be the most vulnerable area in the study region. It is foreseen that decision makers will be able to use the holistic data-driven outcomes of this study to make better informed decisions whilst adapting to climate change.
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