Hannes Restel, Eridy Lukau, Sebastian Sterl, & Lars Gerhold. (2022). Detecting Covid-19 Relevant Situations using Privacy-by-Design based Mobile Experience Sampling. In Rob Grace, & Hossein Baharmand (Eds.), ISCRAM 2022 Conference Proceedings – 19th International Conference on Information Systems for Crisis Response and Management (pp. 506–527). Tarbes, France.
Abstract: To observe psychosocial effects of the Covid-19 pandemic on the population, multiple retrospective studies have been performed in Germany. However, this approach may lead to response bias regarding affective and cognitive processes as it fails to capture situations as they occur (‘in situ’). Identifying those situations in daily life where individuals are emotionally and cognitively affected by Covid-19 can provide valuable insights for mobile experience sampling method studies (MESM) that evaluate participants’ affective and cognitive processes. This study presents an MESM solution (a self-developed smartphone app and server backend) to detect Covid-19 induced ‘in-situ frames’ which was successfully used in a long-term psychosocial study in Berlin (Germany) from November 2021 to January 2022. As highly sensitive personal data (e.g., emotional state, vaccination status and infection state, socio-demographics) have been collected, the solution places a strong emphasis on privacy, pseudo-anonymization, data-minimization, and security. To support long-time motivation for the participants, good usability and user experience containing gamification elements were also realized. The results indicate that Covid-19-related situations expressed by means of a high emotional risk perception could be identified even though participants located themselves in “rather Covid-19 free” private spaces.
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Zeno Franco, Chris Davis, Adina Kalet, Michelle Horng, Johnathan Horng, Christian Hernandez, et al. (2021). Augmenting Google Sheets to Improvise Community COVID-19 Mask Distribution. In Anouck Adrot, Rob Grace, Kathleen Moore, & Christopher W. Zobel (Eds.), ISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management (pp. 359–375). Blacksburg, VA (USA): Virginia Tech.
Abstract: Face mask scarcity in the United States hindered early infection control efforts during the COVID-19 pandemic. Areas with a history of racial segregation and poverty experienced differential COVID-19 death and morbidity rates. Supplying masks equitably and rapidly became an urgent public health priority. A partnership between a local manufacturer with available polypropylene fabric and the Medical College of Wisconsin, which had the capability to assemble and distribute masks, was formed in April, 2020. An improvised logistics framework allowed for rapid distribution more than 250,000 masks, and later facilitated hand-off to other organizations to distribute over 3 million masks. Using an action research framework three phases of the effort are considered, 1) initial deliveries to community clinics, 2) equitable distribution to community agencies while under “safer at home” orders, and 3) depot deliveries and transfer of logistics management as larger agencies recovered. A multi-actor view was used to interrogate the information needs of faculty and staff remotely directing distribution, medical student volunteers delivering masks, and the manufacturer monitorng overall inventory. Logistics information was managed using Google Sheets augmented with a small SQLite component. A phenomenological view, toggling back and forth from the “socio” to the “technical” provides detailed insight into the strengths and limitations of digital solutions for humanitarian logistics, highlighting where paper-based processes remain more efficient. This case study suggests that rather than building bespoke logistics software, supporting relief efforts with non-traditional responders may benefit from extensible components that augment widely used digital tools.
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Nathan Elrod, Pranav Mahajan, Monica Katragadda, Shane Halse, & Jess Kropczynski. (2021). An Exploration of Methods Using Social Media to Examine Local Attitudes Towards Mask-Wearing During a Pandemic. In Anouck Adrot, Rob Grace, Kathleen Moore, & Christopher W. Zobel (Eds.), ISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management (pp. 345–358). Blacksburg, VA (USA): Virginia Tech.
Abstract: During the COVID-19 health crisis, local public offcials expend considerable energy encouraging citizens to comply with prevention measures in order to reduce the spread of infection. During the pandemic, mask-wearing has been accepted among health offcials as a simple preventative measure; however, some local areas have been more likely to comply than others. This paper explores methods to better understand local attitudes towards mask-wearing as a tool for public health offcials' situational awareness when preparing public messaging campaigns. This exploration compares three methods to explore local attitudes: sentiment analysis, n-grams, and hashtags. We also explore hashtag co-occurrence networks as a starting point to begin the filtering process. The results show that while sentiment analysis is quick and easy to employ, the results oer little insight into specific local attitudes towards mask-wearing, while examining hashtags and hashtag co-occurrence networks may be used a tool for a more robust understanding of local areas when attempting to gain situational awareness.
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Shangde Gao, Yan Wang, & Lisa Platt. (2021). Modeling U.S. Health Agencies' Message Dissemination on Twitter and Users' Exposure to Vaccine-related Misinformation Using System Dynamics. In Anouck Adrot, Rob Grace, Kathleen Moore, & Christopher W. Zobel (Eds.), ISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management (pp. 333–344). Blacksburg, VA (USA): Virginia Tech.
Abstract: This research intends to answer: how do (i) generation frequency and (ii) retweeting count of health agencies' messages impact the exposure of the general users to vaccine-related misinformation on Twitter? We creatively employed a Susceptible-Infected-Recovered (SIR) System Dynamics paradigm to model interactions between message dissemination of 168 U.S. health agencies and proportions of users who are at different exposure statuses to misinformation, namely “Susceptible”, “Infected”, or “Recovered” status. The SIR model was built based on the vaccine-relevant tweets posted over November and December in 2020. Our preliminary outcomes suggest that augmenting the generation frequency of agencies' messages and increasing retweeting count can effectively moderate the exposure risk to vaccine-related misinformation. This model illustrates how health agencies may combat vaccine hesitancy through credible information dissemination on social media. It offers a novel approach for crisis informatics studies to model different information categories and the impacted population in the complex digital world.
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Briony Gray, & Matthew Colling. (2021). Supporting Emergency Health Services during a Pandemic: Lessons from the Canadian Red Cross. In Anouck Adrot, Rob Grace, Kathleen Moore, & Christopher W. Zobel (Eds.), ISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management (pp. 320–332). Blacksburg, VA (USA): Virginia Tech.
Abstract: The COVID-19 pandemic has tested Canada's readiness capacity as emergency health needs continue to exceed some communities' capacity to respond. To address this variance, the Canadian Red Cross (in collaboration with local, provincial, territorial, national, and Indigenous partners) have leveraged international experience in humanitarian response and preparedness, developing innovative new response services, delivery modalities, and protocol through which to mitigate and manage risk. This approach breaks down emergency management into two main streams – health interventions and disaster management – to innovatively and effectively cope with increasingly complex and frequent requests for support. Using internal data from within the Canadian Red Cross, this paper presents and discusses the services, roles and expectations of this two-stream approach which has been designed to (i.) support COVID-19 testing and vaccination, (ii.) support outbreak crisis management, especially through epidemic, prevention, and control interventions, and (iii.) support traditional emergency management responses in the midst of a pandemic. It concludes by reporting on the successes of the two-stream approach to date while scoping further the potential evolutionary track of some of these services, their underpinning methodology, and appetite for recovery operations in the near future. This approach may therefore be of value to other organizations or practitioners coping with emergency management challenges during a pandemic.
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