Vol. 9, Special Issue 5, Part B (2024)

Background: In India, high mortality and morbidity rates of human rabies is observed. Hence, a structured surveillance system is yet to be put in place for public health discussion.

Objective: The primary objective of this research study is to develop an accurate and reliable forecasting model for predicting the number of animal bites at the Territory Care Hospital located in Agartala, Tripura i.e., Agartala Government Medical College (AGMC) over the period June 2023 to May 2025. This endeavour aims to facilitate healthcare planning, resource allocation, and effective response to animal-related incidents, ultimately enhancing public health and safety in the region.

Methods and Materials: In this research, primary data have been collected from the Anti Rabies Vaccination (ARV) clinic of Agartala Government Medical College (AGMC), Agartala, Tripura West spanning April 2018 to May 2023 underwent rigorous cleaning, eliminating inconsistencies, duplicates, and outliers. The data was structured chronologically and organized by various regions in Tripura. For forecasting animal bites, a SARIMA (Seasonal Autoregressive Integrated Moving Average) model was meticulously tuned for optimal performance, considering parameters such as differencing order, autoregressive and moving average terms, and seasonal components. Model validation encompassed techniques like cross-validation, out-of-sample testing, and diagnostic checks. This holistic approach aims to provide a robust forecasting tool for predicting animal bites in different places of Tripura state over the period June 2023 to May 2025, thereby aiding healthcare planning and proactive measures to mitigate animal bite incidents in the region.

Results: This research paper employs time series analysis techniques to forecast animal bite incidents over the course of 24 months, spanning the period June 2023 to May 2025. The study establishes the stationarity of the time series data through the Augmented Dickey-Fuller (ADF) and Kwiatkowski-Phillips-Schmidt-Shin (KPSS) tests, while confirming that residuals are normally distributed and independent. Utilizing the ARIMA function, the paper presents a robust forecasting model. The findings hold significance for animal control and public health, offering insights into expected trends and assisting in safety planning.