Mathematical methods in infectious disease: developing tools for modeling and analysis of surveillance data

Mathematical and statistical tools play an increasingly important role in the modern practice of
public health. Both mechanistic and data-driven modeling tools have found application in both the
surveillance and forecasting of infectious disease, evaluating the costs and benefits of public health in-
tervention strategies, and the allocation of limited public health resources, among others. This course
will focus on developing and understanding some of the most commonly-deployed mathematical and
statistical techniques in public health. Topics will include the conceptualization, analysis, validation,
data-informed parameterization of compartmental models; statistical and data-driven tools for survival
analysis, incidence estimation, and the estimation of important difficult-to-measure quantities (such as
viral reproduction). The interaction how mechanistic and statistical modeling methods may inform and
complement one another will also be explored in detail. Familiarity with elementary concepts from dif-
ferential equations, linear algebra and probability and statistics is recommended. Experience with basic
programming concepts is helpful but not mandatory. Students from outside the mathematical sciences
(such as Biology) are also welcome.

Schedule:

Monday, 13:30 - 15:30 Aula G

Tuesday 13:30  - 15:30 Aula L

Wednesday 12:30 - 15:30 Aula L