Seasonal Variation and Time Trend Analysis of Dog Bite Cases Attending the Anti Rabies Clinic in Delhi using ARIMA Model Forecasting

Abstract

Introduction: Rabies is a fatal viral disease which is transmitted to humans through animal bites, most commonly via dogs. Fortunately, this disease is preventable through timely pre and postexposure vaccination. Aim: To study the seasonal predisposition and trend analysis of dog bite cases attending the anti rabies clinic. Materials and Methods: This retrospective cross-sectional study was conducted in the anti rabies clinic of a government hospital in Delhi. Enumeration and inclusion of all dog bite cases were made. An Autoregressive Integrated Moving Average (ARIMA) model was used to analyze the available data of dog bites, from 2011 to 2018. In this study, the least Bayesian Information Criterion (BIC) value was 12.2 and the corresponding model is ARIMA (1, 0, 0) with the goodness of fit 2 (R2 =44%). The model verification was done by noise residual check. The model was applied for time series analysis and forecasting of rabies cases in subsequent years. Results: Total number of dog bite cases were 1,46,344 in last eight years (2011-2018). The maximum number of cases being 27961 in the year 2014 followed by 22385 in the year 2013. A seasonal predisposition of dog bite cases was seen for the month of February to April. The trend analysis forecasting for 2019, 2020, 2021, 2022 and 2023 predicted 11317, 11676, 10157, 8639 and 7120 cases, respectively. Conclusion: Although the dog bite cases will be on a decline in the future, adequate measures need to be strengthened further to sensitise the community about rabies prevention and timely reporting to anti rabies clinic for prophylaxis.

Keywords

• autoregressive integrated moving average
• rabies prevention
• rabies reservoir
• vaccination
• viral disease