Presentation Title

Data Fitting and Behavioral Analysis of the Zika Virus

Faculty Mentor

Dr. Anael Verdugo

Start Date

23-11-2019 12:30 PM

End Date

23-11-2019 12:45 PM

Location

Markstein 211

Session

oral 3

Type of Presentation

Oral Talk

Subject Area

biological_agricultural_sciences

Abstract

This work deals with the analysis of the recent Zika virus epidemic by comparing real biological data with a mathematical model in the field of disease dynamics. The model comes from a set of ordinary differential equations motivated from the Replicator-Mutator model and it aims to explain the long-term dynamic behavior of the Zika virus epidemic in Puerto Rico. Parameters are calculated using data from the Center of Disease Control and Prevention (CDC) over a 15-month period in Puerto Rico. Once parameters are chosen, then linear stability analysis on the model shows the existence of one stable fixed point, which yields accurate predictions for the long-term behavior of the infected population. Future work can be extended by means of validating our current model to fit data in other US territories (states, cities, or towns). This will in turn help fine-tune our parameters and thus a more robust model that accurately predicts the Zika virus epidemic behavior in various locations in the US.

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Nov 23rd, 12:30 PM Nov 23rd, 12:45 PM

Data Fitting and Behavioral Analysis of the Zika Virus

Markstein 211

This work deals with the analysis of the recent Zika virus epidemic by comparing real biological data with a mathematical model in the field of disease dynamics. The model comes from a set of ordinary differential equations motivated from the Replicator-Mutator model and it aims to explain the long-term dynamic behavior of the Zika virus epidemic in Puerto Rico. Parameters are calculated using data from the Center of Disease Control and Prevention (CDC) over a 15-month period in Puerto Rico. Once parameters are chosen, then linear stability analysis on the model shows the existence of one stable fixed point, which yields accurate predictions for the long-term behavior of the infected population. Future work can be extended by means of validating our current model to fit data in other US territories (states, cities, or towns). This will in turn help fine-tune our parameters and thus a more robust model that accurately predicts the Zika virus epidemic behavior in various locations in the US.