Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Data-driven prediction and origin identification of epidemics in
  population networks   Effective intervention strategies for epidemics rely on the identification of
their origin and on the robustness of the predictions made by network disease
models. We introduce a Bayesian uncertainty quantification framework to infer
model parameters for a disease spreading on a network of communities from
limited, noisy observations; the state-of-the-art computational framework
compensates for the model complexity by exploiting massively parallel computing
architectures. Using noisy, synthetic data, we show the potential of the
approach to perform robust model fitting and additionally demonstrate that we
can effectively identify the disease origin via Bayesian model selection. As
disease-related data are increasingly available, the proposed framework has
broad practical relevance for the prediction and management of epidemics.

6
The categories of this paper are: astro-ph
8
The categories of this paper are: hep-ex
0
The categories of this paper are: hep-ph
1
The categories of this paper are: cs
13
The categories of this paper are: quant-ph
27
The categories of this paper are: comp-gas
30
The categories of this paper are: q-alg
23
The categories of this paper are: solv-int
11
The categories of this paper are: hep-lat
22
The categories of this paper are: chao-dyn
35
The categories of this paper are: mtrl-th
33
The categories of this paper are: plasm-ph
4
The categories of this paper are: cond-mat
34
The categories of this paper are: chem-ph
5
The categories of this paper are: gr-qc
9
The categories of this paper are: nlin