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Timely communication of warnings is essential to protection of lives and properties during tornado outbreaks. Both official and personal channels of communication prove to have considerable impact on the overall outcome. In this study, an agent-based model is developed to simulate warning’s reception–dissemination process in which a person is exposed to, receives, and sends information while interacting with others. The model is applied to an EF5 tornado (EF indicates enhanced Fujita scale) that struck Moore, Oklahoma, in 2013. The parameters are calibrated using publicly available data or a poststorm telephone survey or were derived from literature reviews, expert judgement, and sensitivity analysis. The result shows a reasonable agreement between modeled and observed reception rates for older and younger adults and for different channels, with errors of less than 20 percentage points. Similar agreement is also seen for the average numbers of warning sources. The subsequent simulation indicates that, in the absence of tornado sirens, the overall reception rates for younger and older adults would drop from the baseline by 17 and 6 percentage points, respectively. Concurrently, there is a large decline in the number of warning sources. When a persons’ social network is enlarged, the reception rate for older adults improves from 77% to 80%, whereas for younger adults it stays unchanged. The impact of increased connectivity is more pronounced when people are not watching television or a tornado siren is not available.

Every year, tornadoes cause significant property damage and numerous casualties in the United States. This study aims to understand how tornado warnings reach the at-risk public through various communication channels. Using the agent-based model and simulation, we are able to reconstruct the dynamic patterns of warning’s reception–dissemination process for older and younger adults within a historical EF5 tornado. Further analysis confirms the importance of tornado sirens in not only alerting more residents about the dangerous weather condition but also prompting protective actions. In the meantime, an increase in social connectivity among residents would compensate for the lack of exposure to television and tornado siren. Future work should investigate the robustness of this model and its parameters when applied to other tornado outbreaks.

 

Understanding human movements in the face of natural disasters is critical for disaster evacuation planning, management, and relief. Despite the clear need for such work, these studies are rare in the literature due to the lack of available data measuring spatiotemporal mobility patterns during actual disasters. This study explores the spatiotemporal patterns of evacuation travels by leveraging users’ location information from millions of tweets posted in the hours prior and concurrent to Hurricane Matthew. Our analysis yields several practical insights, including the following: (1) We identified trajectories of Twitter users moving out of evacuation zones once the evacuation was ordered and then returning home after the hurricane passed. (2) Evacuation zone residents produced an unusually large number of tweets outside evacuation zones during the evacuation order period. (3) It took several days for the evacuees in both South Carolina and Georgia to leave their residential areas after the mandatory evacuation was ordered, but Georgia residents typically took more time to return home. (4) Evacuees are more likely to choose larger cities farther away as their destinations for safety instead of nearby small cities. (5) Human movements during the evacuation follow a log-normal distribution.