Abstract: The devastating economic and societal impacts of COVID-19 can be substantially compounded by other secondary events that increase individuals' exposure through mass gatherings such as protests or sheltering due to a natural disaster. Based on the Crichton's Risk Triangle model, this paper proposes a Markov Chain Monte Carlo (MCMC) simulation framework to estimate the impact of mass gatherings on COVID-19 infections by adjusting levels of exposure and vulnerability. To this end, a case study of New York City is considered, at which the impact of mass gathering at public shelters due to a hypothetical hurricane will be studied. The simulation results will be discussed in the context of determining effective policies for reducing the impact of multi-hazard generalizability of our approach to other secondary events that can cause mass gatherings during a pandemic will also be discussed.

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.

Abstract: Outbreak of a destructive pandemic influenza threatens to disrupt societies worldwide. International agencies and national governments have prepared plans and recommendations, but it is often decision-makers with the local authorities that are responsible for implementing the response. A central issue for these decision makers is what interventions are available and effective for the specific local community. The paper presents a simulator architecture and its relation to a workflow for decision support in influenza preparedness and response. The simulator can simulate pandemic scenarios, using localized community models, in the presence of various interventions to support an evaluation of potential response strategies. The architecture includes a customized modeling tool, separated from the simulation engine, which facilitates swift scenario modification and recalculation. This flexibility is essential both to explore alternative solutions in planning, and to adapt to changing requirements, information, and resources in outbreak response. An example simulation, based on actual population data from a reference city, illustrates the approach.

Abstract: In March 2009, a novel influenza A (H1N1) virus emerged from Mexico. The pandemic resulted in a surge of media attention in which a large volume of information was communicated via multiple sources and channels, both traditional and new. In order to better understand the publics perceptions and utilization of health information provided, California residents were surveyed using a mailed questionnaire. Results showed most respondents felt they had received enough information about the outbreak. The study also found participants preferred conventional communication sources, such as television and newspapers, over new media, such websites. Although, there were some statistically significant differences between information source usage by age as well as by education. Even though respondents reported using a variety of sources, as a whole, they were unsure of their accuracy, trustworthiness or usefulness. Further study is needed to understand if these results are representative of experiences in other states and countries.

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.

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.