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Integrated causal-predictive machine learning models for tropical cyclone epidemiology.

Rachel C Nethery1, Nina Katz-Christy2, Marianthi-Anna Kioumourtzoglou3

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Summary
This summary is machine-generated.

Tropical cyclones (TCs) pose health risks, but machine learning can improve preparedness. This study quantizes TC health impacts and identifies vulnerable communities, finding wind speed is a key risk factor.

Keywords:
Causal inferenceEnvironmental epidemiologyExtreme weather eventsHurricanesLatent factor modelMatrix completion

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Area of Science:

  • Environmental Epidemiology
  • Public Health
  • Data Science

Background:

  • Strategic preparedness is crucial for mitigating health impacts of tropical cyclones (TCs).
  • Enhanced characterization of TC epidemiology can improve preparedness strategies.
  • Existing methods for assessing TC health impacts lack precision and comprehensive analysis.

Purpose of the Study:

  • To develop a machine learning approach for standardized estimation of historic TC health impacts.
  • To identify patterns and sources of heterogeneity in TC-related health outcomes.
  • To enable precise identification of communities at high risk for future TCs.

Main Methods:

  • Integrated causal inference and predictive modeling for health impact assessment.
  • Utilized a data platform with historic TC exposure and Medicare mortality/hospitalization records.
  • Analyzed associations between TC meteorological features and community characteristics with health outcomes.

Main Results:

  • Observed significant heterogeneity in acute health impacts within and across TCs.
  • Documented substantial TC-attributable increases in respiratory hospitalizations.
  • Identified TC-sustained windspeeds as the primary driver of mortality and respiratory risks.

Conclusions:

  • Machine learning offers a powerful tool for precise TC health impact assessment and risk identification.
  • Understanding heterogeneity in TC health impacts is vital for targeted public health interventions.
  • Sustained windspeed is a critical factor in TC-related mortality and respiratory morbidity.