ORIGINAL RESEARCH article
Front. Trop. Dis.
Sec. Neglected Tropical Diseases
This article is part of the Research TopicRabies in Developing Countries: Challenges Amid Economic Constraints and Co-Existing Neglected Tropical DiseasesView all articles
Forecasting Dog Rabies Dynamics in Tunisia Using Time Series Models: Insights for Early Warning Systems
Provisionally accepted
Sana Kalthoum1*
Mariem Handous2
Kaouther guesmi1
Imed Ben Slimen1
Hajlaoui Haikel1Wiem Khalfaoui1Aziz ben Mbarek3
Chafik Ben salah4
Mounir Bsir5Kaouther Oukaili6Mohamed Naceur Baccar1- 1Centre National de Veille Zoosanitaire, Tunis, Tunisia
- 2Institut Pasteur de Tunis, Tunis, Tunisia
- 3Commissariat régional au développement agricole, Sfax, sfax, Tunisia
- 4Commissariat régional au développement agricole, El Kef,, kef, Tunisia
- 5Commissariat régional au développement agricole, Mahdia, mahdia, Tunisia
- 6Bureau régional de l’organisation mondiale de la santé,, tunis, Tunisia
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Rabies is endemic in Tunisia, with a rising epidemic trend being observed over the years and especially after 2012, leading to substantial economic impacts on both animal and human populations. While temporal trends have been previously documented, time series analysis offers a powerful tool for understanding this evolution.. To inform evidence-based surveillance and control strategies, this study aimed to identify the most accurate time series model for forecasting monthly dog rabies cases. A time series analysis approach was conducted to model and forecast rabies cases in dogs from January 1994 to December 2023. Several forecasting models were evaluated for their performance, including Seasonal Autoregressive Integrated Moving Average (SARIMA), Error-Trend-Seasonal (ETS), neural network nonlinear autoregression (NNAR), Prophet model and the Trigonometric Exponential Smoothing State-Space Model with Box-Cox transformation, ARMA errors, Trend and Seasonal components (TBATS). Model accuracy was evaluated using root mean squared error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE). The TBATS model exhibited the highest forecasting accuracy compared to the other tested models. These results indicate that TBATS is the most reliable model for short-to medium-term rabies forecasting in Tunisia. These findings highlight the potential of advanced time series modeling in improving rabies surveillance and control strategies in Tunisia, supporting more effective resource allocation and intervention planning.
Keywords: Dog rabies, modelling, time-serial, forecast, Tunisia
Received: 31 Aug 2025; Accepted: 10 Nov 2025.
Copyright: © 2025 Kalthoum, Handous, guesmi, Ben Slimen, Haikel, Khalfaoui, Mbarek, salah, Bsir, Oukaili and Baccar. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Sana Kalthoum, kalthoum802008@yahoo.fr
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