TY - JOUR
T1 - Risk-aware temporal cascade reconstruction to detect asymptomatic cases
AU - Jang, Hankyu
AU - Pai, Shreyas
AU - Adhikari, Bijaya
AU - Pemmaraju, Sriram V.
N1 - Funding Information:
This project was funded by CDC MInD Healthcare Network grants U01CK000531 and U01CK000594 and NSF Grant 1955939. The authors acknowledge feedback from other University of Iowa CompEpi group members. This paper is an extended version of work published in ICDM 2021 []. The authors thank the anonymous ICDM 2021 reviewers for providing valuable feedback.
Publisher Copyright:
© 2022, The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature.
PY - 2022/12
Y1 - 2022/12
N2 - This paper studies the problem of detecting asymptomatic cases in a temporal contact network in which multiple outbreaks have occurred. We show that the key to detecting asymptomatic cases well is taking into account both individual risk and the likelihood of disease-flow along edges. We consider both aspects by formulating the asymptomatic case detection problem as a directed prize-collecting Steiner tree (Directed PCST) problem. We present an approximation-preserving reduction from this problem to the directed Steiner tree problem and obtain scalable algorithms for the Directed PCST problem on instances with more than 1.5M edges obtained from both synthetic and fine-grained hospital data. On synthetic data, we demonstrate that our detection methods significantly outperform various baselines (with a gain of 3.6 ×). We apply our method to the infectious disease prediction task by using an additional feature set that captures exposure to detected asymptomatic cases and show that our method outperforms all baselines. We further use our method to detect infection sources (“patient zero”) of outbreaks that outperform baselines. We also demonstrate that the solutions returned by our approach are clinically meaningful by presenting case studies.
AB - This paper studies the problem of detecting asymptomatic cases in a temporal contact network in which multiple outbreaks have occurred. We show that the key to detecting asymptomatic cases well is taking into account both individual risk and the likelihood of disease-flow along edges. We consider both aspects by formulating the asymptomatic case detection problem as a directed prize-collecting Steiner tree (Directed PCST) problem. We present an approximation-preserving reduction from this problem to the directed Steiner tree problem and obtain scalable algorithms for the Directed PCST problem on instances with more than 1.5M edges obtained from both synthetic and fine-grained hospital data. On synthetic data, we demonstrate that our detection methods significantly outperform various baselines (with a gain of 3.6 ×). We apply our method to the infectious disease prediction task by using an additional feature set that captures exposure to detected asymptomatic cases and show that our method outperforms all baselines. We further use our method to detect infection sources (“patient zero”) of outbreaks that outperform baselines. We also demonstrate that the solutions returned by our approach are clinically meaningful by presenting case studies.
KW - Asymptomatic cases
KW - C. diff infections
KW - Prize-collecting Steiner tree
KW - Temporal contact networks
UR - http://www.scopus.com/inward/record.url?scp=85138210484&partnerID=8YFLogxK
U2 - 10.1007/s10115-022-01748-8
DO - 10.1007/s10115-022-01748-8
M3 - Article
AN - SCOPUS:85138210484
SN - 0219-1377
VL - 64
SP - 3373
EP - 3399
JO - Knowledge and Information Systems
JF - Knowledge and Information Systems
IS - 12
ER -