We are interested in investigating how deaths and hospitalizations resulting from opioid overdoses cluster across space and time in the US. This analysis will be conducted with the aid of two comprehensive databases: 1) detailed mortality data across the US; and 2) a stratified sample of all hospitalizations in the US, which can be subset to select for opioid overdoses. Analyses will be extended to drug type (prescription drugs, fentanyl etc.) and subject demographics (age, race, etc.). We have previously conducted similar cluster analysis for other health phenomena.

As a secondary aim we are interested in looking into building simple forecast models to estimate incidence of opioid related deaths and/or hospitalizations in the near-term (1-4 time steps, typically months or quarters). We have experience building both mechanistic and statistical forecasting models for infectious diseases (including influenza, West Nile virus, Ebola) and hypothesize that these models could be adapted to forecast opioid-related morbidity and mortality. Such predictions, especially when available at finer geographical resolutions, such as state or county level, could inform public health response to this growing epidemic.

One selected candidate will receive a stipend via the DSI Scholars program. Amount is subject to available funding.

Faculty Advisor

  • Professor Jeffrey Shaman
  • Department/School: Environmental Health Sciences/Mailman School of Public Health
  • Location: CUMC

Project timeline

  • Earliest starting date: 03/01/2019
  • End date: 08/31/2019
  • Number of hours per week of research expected during Spring 2019: ~10
  • Number of hours per week of research expected during Summer 2019: ~20

Candidate requirements

  • Skill sets: Familiarity with data science, machine learning, statistics and numerical methods; coding in R, python and/or Matlab.
  • Student eligibility (as of Spring 2019): freshman, sophomore, junior, senior, master’s
  • International students on F1 or J1 visa: eligible