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mortality

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ForestESS simulates plant demographic processes (e.g., growth, reproduction, and mortality), competition for light and soil resources, and soil biogeochemical processes. The codes are from the version of LM3-PPA used to simulate the forest successional dynamics in the paper of Weng, E., Farrior, CE, Dybzinski, R, and Pacala, SW, et al. 2016 Global Change Biology.

  • Updated May 17, 2020
  • Fortran

Investigating how carbon emissions, particulate matter, and climate variables/indices impact mortality from chronic respiratory disease. Working with pollutant, climate, mortality, population, and geographic datasets. Modeling with Random Forest regression.

  • Updated Jan 29, 2022
  • HTML

This group project employs an artificial neural network and logistic regression to predict COVID19 mortality rates in individuals from the CDC Surveillance dataset. It contains a report, presentation, and several notebooks that have various functions. The primary notebooks used were Clean, Describe, Logit, MLPClassifier, and Project which is a summary notebook.

  • Updated Jun 25, 2021
  • Jupyter Notebook

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