Anmol Haque, Duygu Pamukcu, Ruixiang Xie, Mohsen Zaker Esteghamati, Margaret Cowell, & Jennifer L. Irish. (2021). Cascading Effects of Mass Gatherings on COVID-19 Infections from a Multi-hazard Perspective: A Case Study of New York City. 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. 218–227). Blacksburg, VA (USA): Virginia Tech.
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.
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Peter Serwylo, Paul Arbon, & Grace Rumantir. (2011). Predicting patient presentation rates at mass gatherings using machine learning. In E. Portela L. S. M.A. Santos (Ed.), 8th International Conference on Information Systems for Crisis Response and Management: From Early-Warning Systems to Preparedness and Training, ISCRAM 2011. Lisbon: Information Systems for Crisis Response and Management, ISCRAM.
Abstract: Mass gatherings have been defined as events where more than 1,000 people are present for a defined period of time. Such an event presents specific challenges with respect to medical care. First aid is provisioned on-site at most events in order to prevent undue strain on the local emergency services. In order to allocate enough resources to deal with the expected injuries, it is important to be able to accurately predict patient volumes. This study used machine learning techniques to identify which variables are the most important in predicting patient volumes at mass gatherings. Data from 201 mass gatherings across Australia was analysed, finding that event type is the most predictive variable, followed by the state or territory, heat index, humidity, whether it is bounded, and the time of day. Variables with little bearing on the outcome included the presence of alcohol, whether the event was indoors or outdoors, and whether it had one point of focus. The best predictive models produced acceptable predictions of the patient presentations 80% of the time, and this could be further improved using optimization techniques.
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Yasir Javed. (2016). Sensors-based Crisis Response and Management for Mass Gatherings: A Case of Hajj. In A. Tapia, P. Antunes, V.A. Bañuls, K. Moore, & J. Porto (Eds.), ISCRAM 2016 Conference Proceedings ? 13th International Conference on Information Systems for Crisis Response and Management. Rio de Janeiro, Brasil: Federal University of Rio de Janeiro.
Abstract: Many people die or are lost every year during mass gatherings around the world hence making it very difficult for the local authorities to track them and identify them in case of accidents. This paper proposes a system for tracing of lost, injured and dead using network of Radio Frequency Identifiers (RFID) tags and mobile phones. With such a system, time, effort, and cost can be significantly minimized hence eliminating the psychological torture through which relatives of the lost passes though. The proposed system can also be used for crowd management in a real time. For outdoor tracking, where placing RFID readers is not practical, the paper proposes mobile-based peer to peer network for tracking pilgrims who don?t have access to the internet or don?t have GPS facility in their mobile phones. The paper also proposed a plan of testing the prototype in simulation.
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