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Data-driven prediction and origin identification of epidemics in population networks.

ABSTRACT: 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.

SUBMITTER: Larson K

PROVIDER: S-EPMC7890494 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature

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Data-driven prediction and origin identification of epidemics in population networks.

Larson Karen K Arampatzis Georgios G Bowman Clark C Chen Zhizhong Z Hadjidoukas Panagiotis P Papadimitriou Costas C Koumoutsakos Petros P Matzavinos Anastasios A

Royal Society open science 20210120 1

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 ...[more]

PMID: 33614060

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