Epidemic Source Detection in Contact Tracing Networks: Epidemic Centrality in Graphs and Message-Passing Algorithms

We study the epidemic source detection problem in contact tracing networks modeled as a graph-constrained maximum likelihood estimation problem using the susceptible-infected model in epidemiology. Based on a snapshot observation of the infection subgraph, we first study finite degree regular graphs...

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Vydáno v:IEEE journal of selected topics in signal processing Ročník 16; číslo 2; s. 234 - 249
Hlavní autoři: Yu, Pei-Duo, Tan, Chee Wei, Fu, Hung-Lin
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York IEEE 01.02.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Algorithms
computational epidemiology
Contact tracing
Coronaviruses
COVID-19
Epidemics
Epidemiology
graph algorithms
Graph theory
Graphs
inference algorithms
Likelihood ratio
Maximum likelihood estimation
Maximum likelihood estimators
Message passing
Optimization
Pandemics
Performance evaluation
Probabilistic logic
probability
Public healthcare
Signal processing algorithms
Viral diseases
ISSN:1932-4553, 1941-0484
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Shrnutí:We study the epidemic source detection problem in contact tracing networks modeled as a graph-constrained maximum likelihood estimation problem using the susceptible-infected model in epidemiology. Based on a snapshot observation of the infection subgraph, we first study finite degree regular graphs and regular graphs with cycles separately, thereby establishing a mathematical equivalence in maximal likelihood ratio between the case of finite acyclic graphs and that of cyclic graphs. In particular, we show that the optimal solution of the maximum likelihood estimator can be refined to distances on graphs based on a novel statistical distance centrality that captures the optimality of the nonconvex problem. An efficient contact tracing algorithm is then proposed to solve the general case of finite degree-regular graphs with multiple cycles. Our performance evaluation on a variety of graphs shows that our algorithms outperform the existing state-of-the-art heuristics using contact tracing data from the SARS-CoV 2003 and COVID-19 pandemics by correctly identifying the superspreaders on some of the largest superspreading infection clusters in Singapore and Taiwan.
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ISSN:1932-4553
1941-0484
DOI:10.1109/JSTSP.2022.3153168