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Quantifying the dynamics of migration after Hurricane Maria in Puerto Rico.
Acosta, Rolando J; Kishore, Nishant; Irizarry, Rafael A; Buckee, Caroline O.
Affiliation
  • Acosta RJ; Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA 02115.
  • Kishore N; Center for Communicable Disease Dynamics, Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, MA 02115.
  • Irizarry RA; Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA 02115.
  • Buckee CO; Department of Data Science, Dana-Farber Cancer Institute, Boston, MA 02215.
Proc Natl Acad Sci U S A ; 117(51): 32772-32778, 2020 12 22.
Article in En | MEDLINE | ID: mdl-33293417
Population displacement may occur after natural disasters, permanently altering the demographic composition of the affected regions. Measuring this displacement is vital for both optimal postdisaster resource allocation and calculation of measures of public health interest such as mortality estimates. Here, we analyzed data generated by mobile phones and social media to estimate the weekly island-wide population at risk and within-island geographic heterogeneity of migration in Puerto Rico after Hurricane Maria. We compared these two data sources with population estimates derived from air travel records and census data. We observed a loss of population across all data sources throughout the study period; however, the magnitude and dynamics differ by the data source. Census data predict a population loss of just over 129,000 from July 2017 to July 2018, a 4% decrease; air travel data predict a population loss of 168,295 for the same period, a 5% decrease; mobile phone-based estimates predict a loss of 235,375 from July 2017 to May 2018, an 8% decrease; and social media-based estimates predict a loss of 476,779 from August 2017 to August 2018, a 17% decrease. On average, municipalities with a smaller population size lost a bigger proportion of their population. Moreover, we infer that these municipalities experienced greater infrastructure damage as measured by the proportion of unknown locations stemming from these regions. Finally, our analysis measures a general shift of population from rural to urban centers within the island. Passively collected data provide a promising supplement to current at-risk population estimation procedures; however, each data source has its own biases and limitations.
Key words

Full text: 1 Collection: 01-internacional Database: MEDLINE Type of study: Prognostic_studies Country/Region as subject: Puerto rico Language: En Journal: Proc Natl Acad Sci U S A Year: 2020 Type: Article

Full text: 1 Collection: 01-internacional Database: MEDLINE Type of study: Prognostic_studies Country/Region as subject: Puerto rico Language: En Journal: Proc Natl Acad Sci U S A Year: 2020 Type: Article