Xingquan Zhu

Journal Article Screening for different genotypes of Echinococcus granulosus within China and Argentina by single-strand conformation polymorphism (SSCP) analysis Get access Lihua Zhang, Lihua Zhang 1Molecular Parasitology Unit, Tropical Health Program, Australian Centre for International and Tropical Health and Nutrition, The University of Queensland and The Queensland Institute of Medical Research, P.O. Royal Brisbane Hospital, Herston, Queensland 4029, Australia Search for other works by this author on: Oxford Academic PubMed Google Scholar Robin B. Gasser, Robin B. Gasser 2Department of Veterinary Science, University of Melbourne, 250 Princes Highway, Werribee, Victoria, 3030, Australia Search for other works by this author on: Oxford Academic PubMed Google Scholar Xingquan Zhu, Xingquan Zhu 2Department of Veterinary Science, University of Melbourne, 250 Princes Highway, Werribee, Victoria, 3030, Australia Search for other works by this author on: Oxford Academic PubMed Google Scholar Donald P. McManus Donald P. McManus ⋆ 1Molecular Parasitology Unit, Tropical Health Program, Australian Centre for International and Tropical Health and Nutrition, The University of Queensland and The Queensland Institute of Medical Research, P.O. Royal Brisbane Hospital, Herston, Queensland 4029, Australia ⋆Address for correspondence: Molecular Parasitology Unit, Tropical Health Program, Australian Centre for International and Tropical Health and Nutrition, The University of Queensland and The Queensland Institute of Medical Research, P.O. Royal Brisbane Hospital, Herston, Queensland 4029, Australia; phone +61 7 33620401, fax +61 7 33620104. donM@qimr.edu.au Search for other works by this author on: Oxford Academic PubMed Google Scholar Transactions of The Royal Society of Tropical Medicine and Hygiene, Volume 93, Issue 3, May-June 1999, Pages 329–334, https://doi.org/10.1016/S0035-9203(99)90043-3 Published: 01 June 1999 Article history Received: 06 November 1998 Revision received: 02 February 1999 Accepted: 16 March 1999 Published: 01 June 1999

This paper proposes to study employee retention prediction in a multi-sector organization running retail operations, sales and service centers, and corporate support functions. We examine the uniqueness of the Retail and Trade Sector (RTS) industry and propose to use survival analysis to predict employee tenure and identify retention risks. Using a data set of 4,953 employees in multiple business sectors, we evaluated regression models vs. survival analysis models, and show that our survival analysis approach successfully distinguishes between employees who leave within two years versus those who stay 6+ years. Our methodology employs rate-based temporal feature engineering to capture time-dependent patterns while preventing data leakage, and demonstrates that regression-and classification-based approaches have performance limitations when making accurate predictions for active employees. Survival models enable proactive identification of high-risk employees, providing actionable insights for talent retention strategies, making them particularly useful for dealing with incomplete observations inherent in human resources data.