• Population Vulnerability Measures to the spread of Infectious Diseases.

In this research work, population vulnerability to the spread of infectious diseases measures are developed.  Indices focusing on medical, social, economic, demographic and public transportation parameters are used in this study.  These measures can help government officials and decision makers to rank different geographic areas such as neighborhoods from the most vulnerable to the least vulnerable to infectious disease outbreaks.  This research work can be used for strategic planning in response to outbreaks, epidemics, or pandemics.  It can help decision makers in resources planning and allocation.

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• Integrating Human Mobility into Epidemic Vulnerability Measures in the U.S. 

This research work will help guide strategic planning in response to infectious disease outbreaks, in the U.S. by assessing population vulnerability to the spread of infectious diseases.  The application of these measures aims to identify the most vulnerable communities in the U.S. that need more attention than others.  A state-level analysis of the U.S. population’s vulnerability to the spread of infectious diseases is conducted to obtain a national index ranking the U.S. states from the most vulnerable to the least vulnerable to infectious diseases such as COVID-19.

• Design of experiment of different scenarios to simulate the spread of COVID-19 using an agent-based model.

A design of experiment based on “Factorial Designs” was developed to assist with the preparation of a virtual experiment that simulate the spread of an infectious disease over a population in a metropolitan area using an agent-based simulation model.  This approach enables the comprehensive capture of the different factors involved in the development of strategic response plans to infectious disease outbreaks and accommodates the fine-tuning of their features.  These factors encompass both pharmaceutical interventions (Vaccines, Tests) and non-pharmaceutical interventions (Travel restrictions, social distancing, self-quarantining, closure orders of schools/businesses, etc.), while the features are quantitative parameters related to these interventions, such as the number of testing stations, the number of travel-restricted subway stations, the start day of an intervention, and the duration of these interventions.

• Role of Human Mobility in the spread of COVID-19 in the U.S.

While working on an independent study at PSU-GV.  I proposed a methodology for the development of equations describing the spread of COVID-19 to the U.S. states using regression methods.  These equations are based on the number of COVID-19 cases in other states and the travel flows between states.

• A Machine Learning predicting travel restrictions to minimize the impact of COVID-19 in the U.S.

I am currently working on my MS thesis developing a Machine Learning capable of making predictions that would result in insights that help in recommending effective travel restrictions in the U.S. during an epidemic or a pandemic.  These recommendations can help decision-makers in their strategic planning for a response to infectious disease outbreaks.  The ML can predict on which U.S. states travel restrictions must be imposed to minimize deaths and delay the time when deaths will reach the peak by state.

• Sentiment analyses on population opinion toward COVID-19 vaccines

I participated in the development of a Machine Learning model that classifies the emotions of Twitter users regarding COVID-19 vaccines. Deep learning techniques, particularly Recurrent Neural Networks (RNN) were used in this classification process.

• A framework for the assessment of the impact of Artificial Intelligence (AI) on the job markets.

At the PSU-GV, Big Data Lab, we are developing a comprehensive framework designed to assess the impact of AI on job markets. The framework synthesizes various dimensions of this complex relationship, encompassing the direct and indirect effects of AI adoption in diverse sectors and regions. [Join our Facebook community]