Dataset Information

Leveraging Twitter to gauge evacuation compliance: Spatiotemporal analysis of Hurricane Matthew.

ABSTRACT: Hurricane Matthew was the deadliest Atlantic storm since Katrina in 2005 and prompted one of the largest recent hurricane evacuations along the Southeastern coast of the United States. The storm and its projected landfall triggered a massive social media reaction. Using Twitter data, this paper examines the spatiotemporal variability in social media response and develops a novel approach to leverage geotagged tweets to assess the evacuation responses of residents. The approach involves the retrieval of tweets from the Twitter Stream, the creation and filtering of different datasets, and the statistical and spatial processing and treatment to extract, plot and map the results. As expected, peak Twitter response was reached during the pre-impact and preparedness phase, and decreased abruptly after the passage of the storm. A comparison between two time periods-pre-evacuation (October 2th-4th) and post-evacuation (October 7th-9th)-indicates that 54% of Twitter users moved away from the coast to a safer location, with observed differences by state on the timing of the evacuation. A specific sub-state analysis of South Carolina illustrated overall compliance with evacuation orders and detailed information on the timing of departure from the coast as well as the destination location. These findings advance the use of big data and citizen-as-sensor approaches for public safety issues, providing an effective and near real-time alternative for measuring compliance with evacuation orders.

SUBMITTER: Martin Y

PROVIDER: S-EPMC5533310 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature

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Leveraging Twitter to gauge evacuation compliance: Spatiotemporal analysis of Hurricane Matthew.

Martín Yago Y Li Zhenlong Z Cutter Susan L SL

PloS one 20170728 7

Hurricane Matthew was the deadliest Atlantic storm since Katrina in 2005 and prompted one of the largest recent hurricane evacuations along the Southeastern coast of the United States. The storm and its projected landfall triggered a massive social media reaction. Using Twitter data, this paper examines the spatiotemporal variability in social media response and develops a novel approach to leverage geotagged tweets to assess the evacuation responses of residents. The approach involves the retri ...[more]

PMID: 28753667

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Project description:Computational models of cholera transmission can provide objective insights into the course of an ongoing epidemic and aid decision making on allocation of health care resources. However, models are typically designed, calibrated and interpreted post-hoc. Here, we report the efforts of a team from academia, field research and humanitarian organizations to model in near real-time the Haitian cholera outbreak after Hurricane Matthew in October 2016, to assess risk and to quantitatively estimate the efficacy of a then ongoing vaccination campaign. A rainfall-driven, spatially-explicit meta-community model of cholera transmission was coupled to a data assimilation scheme for computing short-term projections of the epidemic in near real-time. The model was used to forecast cholera incidence for the months after the passage of the hurricane (October-December 2016) and to predict the impact of a planned oral cholera vaccination campaign. Our first projection, from October 29 to December 31, predicted the highest incidence in the departments of Grande Anse and Sud, accounting for about 45% of the total cases in Haiti. The projection included a second peak in cholera incidence in early December largely driven by heavy rainfall forecasts, confirming the urgency for rapid intervention. A second projection (from November 12 to December 31) used updated rainfall forecasts to estimate that 835 cases would be averted by vaccinations in Grande Anse (90% Prediction Interval [PI] 476-1284) and 995 in Sud (90% PI 508-2043). The experience gained by this modeling effort shows that state-of-the-art computational modeling and data-assimilation methods can produce informative near real-time projections of cholera incidence. Collaboration among modelers and field epidemiologists is indispensable to gain fast access to field data and to translate model results into operational recommendations for emergency management during an outbreak. Future efforts should thus draw together multi-disciplinary teams to ensure model outputs are appropriately based, interpreted and communicated.

Project description:BackgroundOn October 4, 2016, Hurricane Matthew struck southwest Haiti as a category 4 storm. The goal of this study was to evaluate the impact of the hurricane on tuberculosis (TB) services and patient outcomes in the three severely affected departments-Sud, Grand'Anse, and Nippes-of southwest Haiti.MethodsWe developed a standard questionnaire to assess a convenience sample of health facilities in the affected areas, a patient tracking form, and a line list for tracking all patients with drug-susceptible TB registered in care six months before the hurricane. We analyzed data from the national TB electronic surveillance system to determine outcomes for all patients receiving anti-TB treatment in the affected areas. We used logistic regression analysis to determine factors associated with treatment success.ResultsOf the 66 health facilities in the three affected departments, we assessed 31, accounting for 536 (45.7%) of 1,174 TB patients registered in care when Hurricane Matthew made landfall in Haiti. Three (9.7%) health facilities sustained moderate to severe damage, whereas 18 (58.1%) were closed for <1 week, and five (16.1%) for ≥1 week. Four weeks after the hurricane, 398 (73.1%) of the 536 patients in the assessed facilities were located. Treatment success in the affected departments one year after the hurricane was 81.4%. Receiving care outside the municipality of residence (adjusted odds ratio [aOR]: 0.46, 95% confidence interval [CI]: 0.27-0.80) and HIV positivity (aOR: 0.31, 95% CI: 0.19-0.51) or unknown HIV status (aOR: 0.49, 95% CI: 0.33-0.74) were associated with significantly lower rates of treatment success.ConclusionsDespite major challenges, a high percentage of patients receiving anti-TB treatment before the hurricane were located and successfully treated in southwest Haiti. The lessons learned and results presented here may help inform policies and guidelines in similar settings for effective TB control after a natural disaster.

Dataset Information

Leveraging Twitter to gauge evacuation compliance: Spatiotemporal analysis of Hurricane Matthew.

Publications

Leveraging Twitter to gauge evacuation compliance: Spatiotemporal analysis of Hurricane Matthew.

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