Real-time geospatial control of emerging infectious diseases

Emerging infectious diseases have a significant impact on public
health and global economies. Controlling such epidemics is
challenging, and in such a complex environment, potential mitigation
strategies need to be evaluated using geospatial epidemiological
models. The limited availability of case reports during emerging
epidemics complicates the fitting of such models. Furthermore, the
uncertainty about the progress of emerging epidemics convolutes the
decision-making process of policy makers.
Our goal is to develop a new real-time method to optimize the
mitigation of emerging epidemics of viral pathogens, for which we
identify four objectives. (1) Develop a methodology to use virus
genomes as an additional epidemiological marker for the estimation
of parameters in a geospatial epidemiological model. (2) Devise a
method to automatically learn optimal geospatial mitigation
strategies, taking into account the current state of the epidemic,
given by the models obtained in the first objective. (3) Construct a
pipeline that, for a given outbreak, continuously updates the
distribution over epidemiological models, as epidemiological markers
arrive. Using this distribution, we will learn mitigation policies using
model-based reinforcement learning, to advise policy makers. (4)
Evaluate our method on recent epidemics (i.e., Ebola, Yellow fever
and Zika), which will lead to important insights on how to respond to
these and similar pathogens.