Project Title: Data science applied to epidemiological models
Research overview:
My research work is related to mathematical models of life events, specifically to study the spread of communicable diseases and infections in the population, between cities or regions using connectivity networks. The study of mathematical models of communicable diseases helps us predict the effectiveness of interventions that in turn facilitate the government and / or public health departments to make planned decisions to eradicate or mitigate the effect of diseases in the short or long-term population. A mathematical model is a system that uses mathematical proportions as data (parameters and variables) to describe physical or biological processes.
Techniques:
Data collection to construct biologically relevant mathematical models, computational coding in Scilab, Matlab or Python desired but can be learned as part of the research experience. Students will learn differential equations and will use linear algebra and algebraic skills to solve system of equations and computing quantities that are key to predict effectiveness of an intervention to eradicate a disease such as the basic reproductive number (R0) that is a key marker to asses if an epidemic will occur or could be eradicated.
Webpage:
For more information please visit my website (https://mcruzapo.wordpress.com/) or my lab Facebook page (https://www.facebook.com/BiomatCayey2015/)
My research work is related to mathematical models of life events, specifically to study the spread of communicable diseases and infections in the population, between cities or regions using connectivity networks. The study of mathematical models of communicable diseases helps us predict the effectiveness of interventions that in turn facilitate the government and / or public health departments to make planned decisions to eradicate or mitigate the effect of diseases in the short or long-term population. A mathematical model is a system that uses mathematical proportions as data (parameters and variables) to describe physical or biological processes.
Techniques:
Data collection to construct biologically relevant mathematical models, computational coding in Scilab, Matlab or Python desired but can be learned as part of the research experience. Students will learn differential equations and will use linear algebra and algebraic skills to solve system of equations and computing quantities that are key to predict effectiveness of an intervention to eradicate a disease such as the basic reproductive number (R0) that is a key marker to asses if an epidemic will occur or could be eradicated.
Webpage:
For more information please visit my website (https://mcruzapo.wordpress.com/) or my lab Facebook page (https://www.facebook.com/BiomatCayey2015/)