Ph.D.
Madiha Liaqat
Department of Allied Health Sciences, Ziauddin University, Clifton, Karachi
Title: Survival and Risk Factor Analysis of Rabies Cases from a One Health Surveillance Dataset
Name(s): Madiha Liaqat¹
¹ Allied Health Sciences, Ziauddin University, Karachi
Abstract
Rabies is one of the most fatal zoonotic diseases worldwide, especially in low- and middle-income countries. In Pakistan delays in treatment and inadequate vaccination coverage continue to contribute to preventable deaths for rabies cases. The primary objective of this study is to identify key determinants of rabies survival and mortality by using a comprehensive One Health surveillance dataset comprising 3,000 reported rabies exposure cases.
Descriptive analyses of data were conducted to compare baseline characteristics between deceased (n = 373) and surviving individuals (n = 2,627). The associations between clinical and environmental variables and patient outcomes were evaluated using chi-square tests and independent t-tests. The multivariable logistic regression was employed to identify predictors of survival, and a Cox proportional hazards model was used to assess factors associated with time-to-death. Performance of the model was evaluated using the area under the receiver operating characteristic curve (AUC) and concordance index (C-index).
Results depicted that among all exposure cases, dogs accounted for the majority of bites (79.4%). Vaccination status, bite severity, animal condition, and treatment delay showed significant associations with survival (p < 0.001). A logistic regression demonstrated that vaccinated individuals had substantially higher odds of survival (OR = 4.60, 95% CI: 3.37–6.40), whereas the severe bites markedly reduced survival probability (OR = 0.18, 95% CI: 0.13–0.24). Exposure to stray animals was also associated with lower survival (OR = 0.51, 95% CI: 0.38–0.68). It is depicted that each additional day of treatment delay reduced survival odds by approximately 14% (OR = 0.86, 95% CI: 0.83–0.89). Cox regression findings have been used to confirm that severe bites significantly increased mortality risk (HR = 3.37, 95% CI: 2.64–4.29), while vaccination exhibited a strong protective effect (HR = 0.27, 95% CI: 0.20–0.36). The logistic and survival models demonstrated a good predictive performance, with an AUC of 0.784 and a C-index of 0.745, respectively.
These findings highlight the importance of prompt post-exposure treatment, vaccination, and management of stray animal populations in reducing rabies-related mortality and strengthening One Health rabies control strategies.
Keywords: Rabies, One Health, Survival Analysis, Logistic Regression, Cox Regression, Vaccination, Mortality Risk.
Presenting Author Details
Full Name: Dr. Madiha Liaqat
Contact Number: +92 320 1582761
Email: mdiyaliaqat@gmail.com
LinkedIn: https://www.linkedin.com/in/dr-madiha-liaqat-ph-d-ab8492215/
ResearchGate: https://www.researchgate.net/profile/Madiha-Liaqat-2?ev=hdr_xprf
Google Scholar: https://scholar.google.com/citations?user=cuMxeJEAAAAJ&hl=en
Conference: The 3rd International Conference on Microbiology and One Health (MOH-VN 2026)
Session Name/Number: 7. Zoonosis and One Health
Abstract Title: Survival and Risk Factor Analysis of Rabies Cases from a One Health Surveillance Dataset
Category: Oral Presentation
Keywords: Rabies, One Health, Zoonotic Diseases, Survival Analysis, Logistic Regression, Cox Regression, Vaccination, Public Health Surveillance
Corresponding Author
Dr. Madiha Liaqat
Email: mdiyaliaqat@gmail.com
Phone: +92 320 1582761
Affiliation
Allied Health Sciences, Ziauddin University, Karachi, Pakistan