A model for predicting confirmed leptospirosis cases in Sri Lanka

Leptospirosis is a significant health issue in Sri Lanka, but current studies have only examined some districts using time series analysis. There is no comprehensive leptospirosis prediction model for the entire country. This study aims to develop a model to predict leptospirosis distribution across Sri Lanka. We collected reported leptospirosis cases in all districts from 2011 to 2022 from the Epidemiology Department. We tested the normality of the data using the Shapiro-Wilk test. Also, we evaluated various models, including linear regression, exponential regression, polynomial regression, Autoregressive Integrated Moving Average (ARIMA) time series, generalised additive models (parametric), Cox proportional hazards regression model (semi-parametric), and median-based linear model (non-parametric). The best-fitting model was selected based on the minimum Akaike information criterion (AIC) and Bayesian Information Criteria (BIC), and its accuracy was verified using the Root Mean Square Error (RMSE). We found that the ARIMA model was the best-fitting model for Galle, Mullaitivu, Batticaloa, and Puttalam. The exponential regression model was the best-fitting model for Kalmunai, while the polynomial regression model was the best-fitting model for Matale and Jaffna. The generalised additive model was the best-fitting model for Gampaha, Hambantota, Matara, Mannar, Vavuniya, Trincomalee, Anuradhapura, Badulla, Monaragala, and Ratnapura. In contrast, the median-based linear model was the best-fitting model for Colombo, Kalutara, Kandy, Nuwara Eliya, Kilinochchi, Ampara, Kurunegala, Polonnaruwa, and Kegalle. The fitted models were then used to forecast leptospirosis incidents in Sri Lanka from 2024 to 2026. For future research, we recommend Bayesian hierarchical models to identify high-risk clusters of leptospirosis in Sri Lanka.