Space-time modelling of emerging infectious diseases: Assessing leptospirosis risk in Sri Lanka

In this research, models were developed to analyze leptospirosis incidence in Sri Lanka and its relation to rainfall. Before any leptospirosis risk models were developed, rainfall data were evaluated from an agro-ecological monitoring network for producing maps of total monthly rainfall in Sri Lanka. Four spatial interpolation techniques were compared: inverse distance weighting, thin-plate splines, ordinary kriging, and Bayesian kriging. Error metrics were used to validate interpolations against independent data. Satellite data were used to assess the spatial pattern of rainfall. Results indicated that Bayesian kriging and splines performed best in low and high rainfall, respectively. Rainfall maps generated from the agro-ecological network were found to have accuracies consistent with previous studies in Sri Lanka. These rainfall data were then used as the primary predictor in a family of time series leptospirosis forecasting models at varying spatial scales across Sri Lanka. Several modelling scenarios were evaluated using proper scoring rules and numerous other metrics to assess model fit and calibration. A negative binomial integer-valued autoregressive conditional heteroscedasticity (INGARCH) model that included current and previous rainfall covariates, as well as regression on previous cases of leptospirosis at a local and seasonal time scale was selected as the best performing model. It was found that rainfall did not have a significant correlation with leptospirosis incidence in Sri Lanka, but the family of INGARCH models developed was able to forecast leptospirosis incidence and effectively provide early warning for leptospirosis outbreaks at the district level across Sri Lanka