After completing medical school, I opted to pursue a career in public health with the goal of working with larger populations than would have been possible as a practicing physician. I was accepted by Saint Louis University's College for Public Health and Social Justice as part of the Masters in Public Health (MPH) program with concentration in Biosecurity and Disaster Preparedness (BSDP) which I began full-time in the fall of 2019.
In the BSDP program I supplemented public health study with courses in geographic information systems (GIS) mapping, inferential/predictive modeling, and epidemiological data analysis, and computer automation. After the COVID-19 pandemic began, I focused personal projects on tracking and monitoring the progression of the disease. Examples of that work are linked below.
In the professional environment during the COVID-19 and MPOX pandemics I manged teams as the Integrated Data Team Deputy Lead for the New York City Department of Health and Mental Hygiene performing data engineering and analysis for both internal and external disseminaiton. In Louisiana I was a Program Coordinator part-time monitoring and evaluating public health initiatives and health disparities for Black communities in New Orleans through a Tulane university affiliated nonprofit and am currently employed by the Louisiana Office of Public Health as Data Modernization Initiative Lead where I provide system egineering and data architecture/engineering services to streamline data standardization and access.
In order to assist GIS students predominantly acquainted with Python over the R programming language, I curated a series of lesson plans as Python Jupyter Notebooks. Based on the influential text, An Introduction to Statistical Learning*, the plans harness the capabilities of StatsModels, Scikit-Learn, and various open-source GIS Python libraries. The project not only bridges the gap between theoretical concepts and practical application for data enthusiasts and researchers but also ingrains a GIS-focused approach within the broader spectrum of statistical learning. The four comprehensive lesson plans delve into pivotal topics, all through a GIS-centric lens.
GitHub Links to Notebooks:
* Since the creation of these nobooks in 2021, a Python focused edition has been published.
Domestic violence rates have increased across the world during the COVID-19 pandemic. To compare changes
in the number of 911 calls during the pandemic for domestic violence in New Orleans, I created maps and
plots to examine the trends in previous years and during the pandemic.
Map Link: New Orleans: 911 Calls - Domestic Violence
Social determinants of health help describe the conditions which may secondarially affect the health of a
population. These can range from the conditions where people live, work, and play to their yearly income.
In order to monitor an evaluate these social determinants of health, I designed these maps to visually display some of the base statistics of the local communities and overall population of New Orleans.
Map Link: New Orleans Base Statistics
Understanding disparities is just as important as understanding where resources lie. This new map shows
COVID-19 vaccination facilities (as of January 9th, 2021) and household vehicle access.
The purpose of this map is to provide a better understanding of vaccination locations in areas where transportation may be a potential barrier to vaccine access.
Map Link: COVID-19 Vaccinaiton and Vehicle Access
During 2020 I worked on mapping COVID-19 testing centers in New Orleans. The disparity in free testing
centers is a concern for those in poverty areas and households without vehicle access.
Map Link: New Orleans Free COVID-19 Testing Disparity
In my spare time, Python coding has gained my interest. Low-powered and
low-cost computing and data visualization may be the defining factors in the success of developing countries
and communities. In an effort to begin to understand the potential of these technologies I created a weather
display using a Raspberry Pi single board computer and an electronic paper display.
Photos and Code: GitHub Repository
Remote sensing and geospatial analysis is becoming increasingly important in the field of public health. To
explore the ways with which to analyze this data, I created a completely free and open source Python script
which uses the Landsat8 satellite data to create visualizations and GeoTIFF files for further analysis.
This image is an example of the script's calculation of near infrared composite on September 18th, 2020 during the Oregon/Washington forest fires. Areas in red have healthy vegetation.
Code and Information: GitHub Repository